4 Ways You Can Use Data to Enhance Your ABM Strategy

Account based marketing is a strategic approach to B2B marketing, whereby a business pools it’s resources to target a specific set of customers. Campaigns are then personalised to these target customers and designed to establish communication, build relationships and ultimately win new business. 

Whilst ABM has been used by companies in various forms for a couple of decades, many businesses are still missing out on a fundamental element which can help enhance their strategy and drive success… data. 

Did you know that: 

  • 1/3 of businesses aren’t using data for better decision-making 
  • 57% of businesses do not have a single view of data on their top clients across marketing, sales and customer success (I.e a consolidated view of account performance, activity and engagement stats)
  • 32% are not using data to better make decisions across marketing, sales and customer success. 


Here are 4 ways you can (and should) be using data to help bolster your ABM approach. 

#1 Connect your First-Party Data

Before you can launch your ABM strategy, you need to have a good understanding of your current customer base so you can establish important information such as: 

  • Who your contacts are in each account 
  • How they currently interact with you 
  • How often they interact with you 
  • The type of content they are interacting with 

To get a holistic view of your current customers you need to be consolidating your first party data from numerous data sets like CRM, website visits, social engagement, advertising impressions, product usage, emails and meetings. 

Not only will this help you to understand your current relationship with existing customers, but it will also help to unlock insights into the type of content they are engaging with, so you can also deliver relevant content. This is hugely important as consumers are 80% more likely to buy from a company that offers them a personalised experience. 

Whilst it sounds easy, it involves consolidating this data from numerous sources, cleaning it so it’s accurate, in-date and relevant, and then analysing and visualising this data so you can derive valuable insights.  

If you’re in the early stages of mapping out your ABM strategy, getting a true picture of your current customers will help you to map out characteristics of your ideal customer profile (ICP) so you can pinpoint target customers that match your current business model.


#2 Augment Intelligence with Third-Party Data

Once you’ve collated your first-party data, it’s then important to augment this intelligence with third-party data. This includes researching and collating information on: 

  • Industry size 
  • Job titles of influencers and key decision makers in that organisation/business sector  
  • Technographics 
  • Intent data
  • Industry news that may be impacting their business and creating pain points you can help to solve 

It’s important to deliver content that is relevant and personalised to customers their business and their industry. 76% expect more personalised digital experience from companies, so using third party data will provide you with the macro insights to help you achieve this. 


#3 Use AI to Derive Insights from your First and Third-Party Data

Once you’ve collated your first and third-party data, you can then make sense of this through AI. Data and analytics techniques, such as predictive analytics and propensity modelling, can help you derive information that will guide your ABM strategy. Specifically, these models can help you: 

  • Identify and score buying patterns 
  • Establish other customers that are showing similar patterns 
  • Predict pipelines 

This information will then help you to pinpoint opportunities within your existing accounts and new target accounts.


#4 Use Data Modelling to Understand the True Impact of your ABM Campaign

The previous points have focused on how data can help you with the planning stages of ABM, however it’s important to note that data can also derive useful insights post-ABM campaign too. 

Cross-channel activation is a big part of any ABM campaign, but how do you know which marketing tactics are having the most impact and delivering the best ROI?  

Data and analytics techniques, such as marketing mix modelling (MMM), can help with this. Not only will MMM tell you which elements of your campaign are working best, it will guide you towards the channels that are providing the best ROI vs the ones that aren’t, so you can better invest your marketing budget in the right channels. 


Account Based Marketing is all about delivering a personalised customer first experience to those key customers that you’re targeting – data can help you achieve this, and more. Speak to a member of the team today to find out how Jarmany can support you on your ABM journey. 

How to Measure the Success of Your Marketing Strategy

In an ultra-competitive business environment, marketing remains a critical factor that can contribute significantly to the overall success of a business. That’s because an effective marketing strategy:

  • Builds brand awareness
  • Boosts conversion rates
  • Drives growth and revenue

Given the benefits of a successful strategy, it’s no surprise that demand for digital marketing solutions is growing. Last year, 63% of businesses increased their digital marketing budgets, while the industry is growing at an average rate of 9% per year.1 However, without a clear understanding of the impact of your marketing strategy and the metrics to look at, businesses have no way to measure success or improve processes. 

In this article, we’ll explain how the impact of marketing can be measured, helping businesses maximise return on investment (ROI) and boost their bottom line.



Measuring marketing success

So, how do businesses measure the impact of their marketing? First, it’s important to note that marketing is a catch-all term for a range of activities designed to generate leads, and boost sales. These activities include, but are not restricted to: 

  • Email marketing
  • Social media
  • Paid advertising

As such, there’s no single metric to define marketing success. Instead, businesses can use a range of marketing metrics to examine the impact of their strategy, including:

  1. Return on investment (ROI): ROI shows the direct correlation between marketing activities and sales. Businesses can measure ROI either on a per-campaign basis or holistically before using the results to assess what’s working and what isn’t. This makes it easier to set marketing budgets and justify marketing spend moving forward.
  2. Social metrics: Social media is a great way to engage existing customers and attract new ones, and also provides access to key performance metrics that allow you to understand the impact of marketing campaigns and content. Some platforms also allow you to build online communities where customers can discuss products or services, and get answers to product-related questions.
  3. Analytics reports: An analytics report should provide a detailed overview of how a campaign’s results measure up to its goals. One drawback, however, is that they are generated weeks or months after the campaign has finished, meaning they can’t improve outcomes in real-time. Despite this, they can still serve as valuable indicators of what and how to improve in future marketing campaigns.
  4. Cost per lead (CPL) and cost per win (CPW): CPL measures how much a business spends to generate a lead — calculated by dividing the cost of lead-generating activities by the number of leads acquired. CPW measures how much the business spends to generate a single sale. For example, if a marketing campaign costs £5000 to run and resulted in 100 sales, the CPW would be £50.

Broaden your understanding of your marketing performance

Data is playing an increasingly important role in measuring marketing impact. And the good news is that businesses have access to more data than ever before, with 2.5 quintillion bytes of data created each day.2 

But raw data is like a raw material — it needs to be transformed into something usable. For example, businesses need to carry out a series of processes to turn their raw data into actionable insights, including:

  • Data collection: Data is collected from multiple channels, platforms, and systems before it is consolidated in a single location.
  • Data cleansing: The data is cleaned, removing information that is inaccurate, duplicated, or irrelevant, ensuring that the data is high-quality.
  • Data visualisation: Using cutting-edge data visualisation tools, the resulting insights are presented in a clear, graphical, actionable way.
  • Data analysis: The data is then analysed to extract useful information that can inform decision-making.

So, let’s take a look at some of the data-driven techniques that can enhance your understanding of your business’s overall marketing performance.

Attribution modelling

In simple terms, an attribution model is a method used by digital marketing teams to identify how much each of their individual marketing channels has contributed to sales efforts.

There are a variety of attribution models that can be used to distribute the value of a conversation across each touchpoint differently. The six most commonly used are:

  • First interaction
  • Last interaction
  • Last non-direct click
  • Linear
  • Time-decay
  • Position-based

By implementing attribution modelling, marketers can take a holistic view of their efforts. That means measuring the marketing effectiveness of keywords, ads and landing pages, helping to identify which are driving the most value in the long term.

However, attribution models will be impacted by Google’s decisions to phase out third-party cookies in Chrome by 2024, making it difficult to identify, target and measure users and generate consistent user IDs across the funnel.3 Fortunately, businesses can prepare for this by turning their attention to first party cookies.

First party cookies offer another way for websites to collect relevant data. As a result, businesses can benefit from implementing tools that narrow down on first party data and better utilising first party data that they already have for the development of relevant consumer profiles that include age, location, and browsing history.

Product performance analysis

The practice of utilising data to analyse the performance of a specific product is known as product performance analysis. This approach looks at every aspect of the customer journey, including data related to marketing, sales and other metrics.

By undertaking product performance analysis, businesses are able to gain a better understanding of:

  • Which customer segments products appeal to most
  • The number of returning customers
  • Which product details or features are being used and which aren’t

By using customer data in this way, businesses can gain insights into how their marketing approach has impacted the type of people using their products — identifying common characteristics that allow them to build an understanding of their target audience.

Armed with these insights, marketing teams can build optimised campaigns that target the right people, making it easier to boost conversion rates and increase customer lifetime value.

In-depth user journey analysis

User journey analytics is the process of undertaking an in-depth analysis of customer behaviour across numerous touchpoints over a specific period of time. 

This is an approach that is gaining momentum as businesses recognise the value of customer journeys as a means of monitoring customer experience performance and identifying opportunities for improvement.

By implementing this analysis, marketers are able to gain a better understanding of how their current engagement with customers and prospects via their marketing channels is working. 

Furthermore, marketers can also use the insights they can obtain with journey analytics to optimise the way in which they engage with their users throughout the customer journey.

Start measuring success and improving outcomes

Data can unlock massive improvements in your marketing strategy by highlighting what’s working and what isn’t. However, many businesses don’t have the in-house expertise required. What’s more, attempting to become a data-driven business without the right skills and tools is likely to result in poor-quality data and inaccurate insights.

As a result, working with outside providers is becoming increasingly popular for businesses that want to enjoy the benefits of data-driven processes without the hassle, costs and risks of an in-house approach. That’s where Jarmany can help.

By partnering with us, you can access the expertise, skills, and tools required for an effective data strategy. You’ll be able to undertake marketing impact measurements, identify areas for improvement and optimise decision-making. We help our clients become more data-driven with: 

  • Data strategy: We can help you build comprehensive and successful data strategies
  • Data platforms: We take a tech-agnostic approach, tailoring our tools and technology to suit your needs
  • Data science: We build complex AI solutions that help you plan for today and predict tomorrow
  • Data people: Our team of data experts is on hand to help you realise your goals

To find out how Jarmany could help you unlock the power of data and measure the effectiveness of your marketing activities, get in touch with us today and talk to a member of the team.



1  165 Strategy-Changing Digital Marketing Statistics for 2022

2  30 Big Data Statistics Everybody’s Talking About

3 Google Delays Phasing Out Ad Cookies on Chrome Until 2024 

11 Benefits of Using Power BI for Data Analytics

Digital transformation has made it possible to capture data on virtually every process, strategy and touchpoint imaginable. As a result, businesses now have access to an unprecedented amount of data — by 2025 the amount of data generated each day is expected to reach 463 exabytes globally.1

But collecting this data is only part of the story. To drive successful outcomes, businesses need to turn raw data into actionable insights. Those that manage to do this successfully will have a huge competitive advantage over those that don’t.

That’s because data-driven businesses are more likely to:

  • Acquire new customers
  • Retain their existing customers
  • Remain profitable in the long term

There are numerous data analysis techniques businesses can use to unlock cutting-edge insights, but they all require access to the right tools. Cutting-edge data analytics platforms simplify and automate the complex processes involved in data collection, management, analysis, and visualisation.  

While there are various options on the market, including Tableau, Qlik Sense, and Looker, in this article, we’ll be focusing on one of the most popular and powerful platforms — Microsoft Power BI.


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Introducing Power BI

Built by Microsoft and released as a stand-alone product in July 2015, Power BI is a data analytics and visualisation platform that helps organisations manage their data with the help of a modern, easy-to-use dashboard. 

The ‘BI’ in its name refers to Business intelligence, a catch-all term used to describe the strategies, processes and technologies that organisations use to enable data-driven decision-making

Power BI allows businesses to bridge the gap between data and decision-making by transforming raw data from a range of different sources into powerful insights. These insights are then displayed in a clear, graphical, actionable way via the dashboard. 

Since it first hit the market, Power BI has been regarded as a leader in the business analytics and intelligence field, making it the go-to tool for the top organisations around the globe.

Now that we know what Power BI is, let’s break down the key benefits it can bring to your business.

#1 Ease of use

One of Power BI’s greatest benefits is its simplicity. All the complexity of data collection and analysis happens under the hood and users are presented with the final product — insights that are effectively visualised, easy to understand and relevant.

Through its simple, user-friendly interface, users can generate BI reports and dashboards from multiple data sources with minimal effort. This ease of use allows anyone within the organisation to benefit from Power BI and data analytics, not just data experts.

#2 Time-saving templates

Power BI allows users to easily create and use various reporting templates. These can then be saved and subsequently used by different teams and departments across an organisation. 

This helps to:

  • Streamline processes
  • Drastically reduce the time and effort associated with reporting
  • Create internal efficiencies

#3 Security-focused

When it comes to handling and processing data, security is a critical issue for any responsible business. Power BI provides robust and dependable security features that keep your data safe: 

  • Data is secured end-to-end with double encryption, allowing businesses to share analytics insights with confidence
  • The ability to identify patterns of suspicious behaviour with oversight capabilities
  • Strong permissions that reduce the risk of network intrusion
  • Sensitivity labels that ensure your data continues to be protected upon export
  • Built-in compliance measures at a country and industry level
  • Azure services eliminate data exposure to the public internet
  • Row level security (RLS) allows certain people to see certain data points, for example, a sales manager can only see sales related to their team
  • Object levels security (OLS) can show or hide items, such as sales targets, depending on a user’s role permissions

#4 Regular updates

Microsoft releases regular software updates for the platform, usually on a monthly basis.2 These updates help to ensure that Power BI desktop’s features and usability are always ahead of the curve. 

What’s more, Microsoft actively encourages and acts upon the feedback it receives from its users, with feature recommendations often the driving force behind new functionality. 

#5 Cost efficiency

If you’re an existing Microsoft Enterprise Agreement customer, Power BI is available at no additional cost to your subscription. If you’re not, the cost of Power BI varies depending on the plan you choose, either:

  • Pro
  • Premium Per User 
  • Premium Per Capacity.

With Power BI, you can say goodbye to upfront licensing fees, support services and technical experts. Easy to use and powered by citizen development, Power BI allows you to make your own data sets, reports and connectors for your specific needs — all of which can help highlight ways to reduce risk and long-term costs.

#6 Excel integrations

Being a Microsoft product, Power BI offers seamless integration with other Microsoft tools. One of the most useful integrations is with Excel, as this allows users to connect Excel reports, data models, and queries to Power BI dashboards. This enables the rapid gathering and sharing of Excel data in new ways. 

Furthermore, the data that users have already visualised in Power BI can be exported to an Excel spreadsheet seamlessly. This helps to facilitate easy and simple analysis by the end user. 

#7 Real-time analytics

As data collection is a continuous process, the best data analytics platforms are dynamic. Power BI offers real-time analytics, with insights updated at regular intervals depending on the license level you have. 

For example, with Power BI Pro, data can be refreshed up to eight times a day, while with Power BI Premium, it can be updated 48 times a day.

#8 Build personalised dashboards

Power BI is also highly customisable, allowing users to build dashboards — complete with intuitive visualisations — that fit their own unique requirements. What’s more, Power BI’s drag-and-drop functionality makes this process incredibly quick and easy. 

Users can also drill down into data visualisations, providing insights at a more granular level.

#9 Enhanced connectivity

One key aspect of any business intelligence platform is how it interacts with external data sources. Power BI allows you to connect to a wide range of sources and import important business data, such as:

  • Sales data recorded in your CRM
  • Financial data from your ERP

Power BI also connects to a host of third-party solutions and software, including: 

  • Google Analytics
  • Salesforce
  • Spark
  • Zendesk
  • Marketo

Power BI users can also connect data files — such as XML and JSON — and SQL server databases, allowing them to create new and compelling data sets from multiple sources. These data sets can then be used for analysis and reporting. 

#10 Q&A feature

Another key feature of Power BI is its natural language Q&A functionality, which allows users to type a data-related query into a search box and receive answers in real-time. For example, a user might enter the following: 

“Which of our products sold the most units in Q1 of this year?”

Power BI then queries the relevant data sets and returns intelligent, up-to-date answers. This feature also supports autocomplete, allowing it to guess the data sets and categories that users are interested in. 

#11 Cutting-edge visualisations

Last but by no means least, Power BI is renowned for presenting data insights in a clear, coherent, and actionable way. 

Data visualisation is more than just pretty charts. It’s the final piece of the business intelligence puzzle, allowing users to understand and act on data insights. Power BI does it in style with the following options:

  • Pie charts
  • Column charts
  • Stacked column charts
  • Tables
  • Lists
  • Area charts
  • Scatter charts
  • Geo maps
  • Line charts


Making Power BI work for you

Becoming more data-driven should be the goal of any forward-thinking business, and Power BI is one of the best solutions on the market to unlock the power of data. But without the necessary experience and expertise, businesses may find that they only scratch the surface of what Power BI solutions have to offer. 

Often, partnering with an external expert is the best, most cost-effective way to leverage tools like Power BI, ensuring that you get maximum ROI. 

At Jarmany, we’ve built a first-class team of Power BI consultants that can help your business harness the power of data effectively. Whether you are looking for a fully outsourced team or support for your in-house IT team, we can provide you with seamless expertise at a competitive cost.

If you’d like to know more about how Jarmany could help you maximise the value of Power BI to drive smarter decision-making across your company, contact us today.


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  2. What’s new in Power BI

How Data Visualisation Can Improve Your Decision Making

In 1597 Sir Francis Bacon famously said, “knowledge itself is power.”1 Four centuries later, his words are proving to be more accurate than ever, as knowledge in the form of big data delivers an increasing amount of power to businesses.

Tech giants like Google and Facebook have made it abundantly clear that, to them, big data is a goldmine of insights. Therefore, forward-thinking organisations need to invest in and develop a comprehensive data strategy to improve how they obtain, store, manage, share, and use their data.

However, many businesses struggle to make data work for them. A Mckinsey survey found that 47% of business leaders feel that data & analytics have fundamentally transformed their industries, but they still had difficulties putting data to work for their organisations.2

While new technologies allow organisations to collect lots of data, raw data in and of itself has little value. Instead, the value arises when that data is presented in a way that provides actionable insights, informing business leaders on the best course of action.

That’s why in this blog post we’re going to be looking at how data visualization improves decision making. Let’s dive straight in.

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What is data visualisation?

Data visualisation is the final part of a process that includes the collection, cleansing and analysis of information from numerous data sources. This final stage is all about creating a pictorial representation of that data which can then function as a single source of truth for businesses. 

The goal behind creating these visually stimulating visualisations is to tell a compelling story using raw data whilst keeping crucial KPIs in mind during review processes. With the help of data visualisation, key insights and information, such as trends and patterns, can be digested and understood by stakeholders much quicker.

Types of data visualisation

When it comes to visualising their data to help communicate the story behind it to their stakeholders, there are a number of things businesses need to consider. Chief among these is the category of visualisation they want to focus their efforts on, either:

  • Data exploration: Data exploration helps to uncover insights and identify patterns that need further attention.
  • Data explanation: By presenting an easy-to-understand graph or illustration, data explanation helps an audience better understand the results of that data.

Understanding which of those two ends a given visualisation is intended to achieve is essential in order to achieve success in an overarching data strategy.

While there are just two broad categories of data visualisation, there are a number of specific types of visualisations that organisations can deploy to better understand their data. These include:

  • 2D area visualisations: 2D area data visualisations are typically geospatial, as they relate to the relative position of things on the earth’s surface.
  • Temporal visualisations: Temporal visualisations have a start and finish time and elements that may overlap.
  • Multidimensional charts: Multidimensional charts are those with two or more dimensions that help explore correlations and discover casualty, which is why these are amongst the most commonly used visualisations.
  • Hierarchical charts: Hierarchical data sets are the arrangement of groups in which larger groups encompass smaller sets, allowing users to drill down or drill up to conduct in-depth analysis.
  • Network visualisations: Network data visualisations show how data points are related within a wider network.

How does data visualisation improve decision-making?

Data visualisation helps decision-makers see the big picture. From understanding trends and patterns to highlighting issues and areas of concern, data visualisation is crucial to obtaining enhanced oversight over business operations.

Research has shown that organisations that leverage their customer behaviour data to generate insights and make data-driven decisions can outperform their peers by as much as 85% in sales growth. 3

Consequently, any organisation with an eye on the future needs to make sense of its data through data visualisation techniques and tools to enlighten its decision-making processes. Without effective visualisation, organisations are relying more on guesswork and interpretation when it comes to making crucial decisions.

Benefits of data visualisation

Whilst the primary benefit of data visualisation centres around making better business decisions, it’s worth digging into some of the more specific benefits it can help organisations obtain. These include:

  1. Improving speed: Many bad decisions are just good choices with bad timing, as timing is an often overlooked aspect of decision-making. Data visualisation can help businesses draw insights from vast amounts of data in real-time, increasing response times to challenges.
  2. More accurate numbers: Although data provides decision-makers with potentially all the information they need, it’s usually not presented in an easily digestible format. Data visualisation simplifies the information, boosting our comprehension of the data and reducing the need to fill the gaps with our biases, making our decisions more accurate. However, in order to ensure accuracy, it’s pivotal that the data used within visualisations is of the highest quality.
  3. Simplified communication: Once executives and other decision-makers use data to decide on a specific direction, that decision must be communicated to the team responsible for implementation. While the decision may seem obvious, other stakeholders may not fully understand the reasoning behind it, thereby reducing efficiency. With data visualisation, decision-makers could use graphs and charts to communicate the reasons behind the decision clearly.
  4. Identify benchmarks and trends: An effective visualisation makes it easier than ever before for users to recognise relationships and patterns within their data. By exploring these patterns, users are able to focus on specific areas that need attention to help drive their business forward.
  5. Empowering collaboration: Data visualisation helps organisations by presenting data in a universally understood form, empowering people to contribute to decision-making with their perspectives. Approaching any challenge from multiple perspectives enables decision-makers to make better choices.
  6. Understand the story behind your data: Ultimately, all of these benefits of data visualisation lead to one key outcome — a more comprehensive understanding of the story behind a business’s data. Armed with this knowledge, businesses can make better informed decisions that help to drive outcomes and business success in the long term.

Data visualisation tools

Cutting-edge data visualisation tools are essential for converting raw data into actionable insights. As a result, identifying and deploying the right tools is vital for businesses looking to uncover valuable insights that can help drive growth.

Fortunately, there are now a range of data visualisations tools available to businesses looking to harness the power of their data. The most popular among these include:

  • Domo: Domo is a cloud software company specialising in business intelligence tools and data visualisation.
  • Dundas BI: Dundas Data Visualization, Inc. is a software company specialising in data visualisation and dashboard solutions.
  • Infogram: Infogram is a web-based data visualisation and infographics platform.
  • Looker: Part of the Google Cloud Platform following a 2019 acquisition, Looker markets a data exploration and discovery business intelligence platform.
  • Microsoft Power BI: Power BI is an interactive data visualisation software developed by Microsoft with a primary focus on business intelligence.
  • Qlik: Qlik is a business analytics platform that provides software products such as business intelligence and data integration.
  • Sisense: Sisense is a business intelligence software company best known for embedded analytics.
  • Tableau: Tableau Software is an interactive data visualisation software company focused on business intelligence specialising in visualisation techniques.

Even if businesses have access to one or more of these tools, that isn’t enough to ensure effective visualisations. Remember, collecting, sorting, cleansing and analysing data before it gets fed into a cutting-edge tool is essential to ensuring accurate and relevant insights.

And that’s not all. On top of that, businesses also need knowledge, skills and expertise to ensure that tools such as those outlined above are used correctly and therefore produce results that drive positive outcomes.

Enhance your decision-making with data visualisation

Data visualisation has a track record of driving progress. For example, the 1854 Cholera Outbreak Map of London marked the locations of outbreaks, revealing that affected households used the same drinking water wells. Examination of these wells demonstrated a connection between cholera and contaminated water.4 These results helped the city eradicate cholera and contributed to Louis Pasteur’s discovery of modern germ theory.

Over a hundred years later, businesses are looking to leverage data to ensure both growth and prosperity. A comprehensive data strategy that facilitates visualisations that enhance decision-making processes has therefore become essential to long-term success.

However, that requires access to significant knowledge, expertise and cutting-edge tools, all of which can be difficult to obtain and retain in-house. That’s where data analytics providers like Jarmany come in. We’re here to ensure that your business can establish a successful data strategy that delivers insights through stimulating visualisations.

So, if you’re ready to start using your data to predict needs, deliver efficiencies, connect people and achieve growth targets, get in touch with us today.

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1  Knowledge Is Power: How Data Is Feeding Disruption

2  Catch them if you can: How leaders in data and analytics have pulled ahead

3  Delivering personalized experiences in times of change

4  John Snow’s data journalism: the cholera map that changed the world

7 Best Practices for Managing Your Data

In today’s digital world, companies have access to a staggering amount of data. Those that leverage it to drive efficiency and growth have a significant advantage over those that don’t, as author and management consultant Geoffrey Moore pointed out:  

“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.”1 

But to enjoy the benefits that data analytics brings, companies need to know how to collect, store, and utilise data in a secure, efficient, and cost-effective way. This makes data management of critical importance. 

In this article, we’ll explain how to manage data effectively, outlining seven data management best practices you should follow to build a strategy that gives you security, compliance, and results.

1. Outline your goals 

Collecting data is one thing, but knowing what to do with it is another thing entirely. Many businesses sit on a goldmine of data but fail to properly utilise it. Others retain data that they’ll never need, leaving their data management systems crowded and disorganised in the process. 

One of the most important steps you can take is to outline the goals you want to achieve through your data. This helps you to filter the important information from the surplus or irrelevant information. 

Here are some examples of wider business goals that can underpin your data strategy:

  • Make smarter, better-informed decisions
  • Boost efficiency by streamlining and/or automating processes
  • Understand customer behaviour and trends better
  • Build smarter, more effective marketing campaigns
  • Improve employee engagement and retention

When it comes to setting your goals, you need to consider where you want your business to go and what you want it to look like moving forward. This will make the process of outlining what you want to achieve much easier. 

Once you know what you want to achieve, that’s when you can start looking at and collecting the data that can help you realise those goals and drive long-term success. 

2. Focus on security

Alongside its huge potential benefits, data collection also comes with risks. Ineffective security practices and inadequate data governance increases the likelihood of data breaches, which can be disastrous for companies, resulting in:

  • Financial losses
  • The loss of sensitive information or intellectual property
  • Damage to reputation and trust that is hard to recover from
  • Legal action from affected customers

On top of this, cyberattacks are increasing in scope and scale all the time. Just last year, ransomware attacks surged by 105%, putting businesses under increased pressure and scrutiny to protect their data.2 

At the same time, phishing remains the most common attack vector, which emphasises the need for effective security processes, training, and education at all levels of the organisation. 

To keep up with the advanced nature of cyberattacks, businesses must adopt increasingly sophisticated systems to protect their data. One such example is a multilayered security system.

By deploying multilayered systems with distinct components to protect different assets, you can ensure that there’s no single point of failure when it comes to data security. Ultimately, the aim is to ensure that any given component is protected by more than one layer of security. When combined, these layers reduce the risk of a successful cyberattack or data breach.

3. Stay compliant

Compliance is another area where businesses can’t afford to slip up, with severe financial and reputational costs for those that fail to stay in line with data protection regulations.

Like security, the compliance landscape rarely stands still for long. Since GDPR came into force in 2018, businesses that operate in the EU and UK have had much more stringent rules to adhere to around data collection, retention, and privacy. 

Staying on top of the latest compliance requirements isn’t easy, especially as regulations continue to evolve. Businesses therefore need to ensure that they have access to the right tools and have proper processes in place. 

Likewise, putting the right people in charge of compliance and ensuring that everyone understands how to handle critical and sensitive data is key. That might mean hiring in-house experts, providing additional training to existing employees or working with outside consultants.

4. Ensure quality 

With an increasing amount of data being collected across an array of platforms and systems, businesses must focus on maintaining data quality. If left unchecked, good data can become contaminated with bad, outdated, or irrelevant data. 

This can have huge repercussions for businesses that rely on data-driven processes such as analytics and automation. Poor-quality data leads to inaccurate or irrelevant insights, which can lead to poor decision-making.

There are a number of steps that businesses can take in order to ensure the quality of their data and thereby access accurate and relevant insights. This includes:

  • Regularly checking data for accuracy and relevance across different data sources
  • Purging outdated, irrelevant, or duplicate data from data management software
  • Ensuring that all staff who have access to data are trained in data collection best practices
  • Implementing tracking and tagging for web analytics to better understand statistics, get a granular view of data and group product lines and families together

5. Make data accessible 

Striking the right balance between data accessibility and security is essential for any well-run business. It’s crucial that people who need access to data can use it without having to jump through hoops.

However, this shouldn’t come at the cost of security. It’s also vital to ensure that it’s impossible for someone to access company data without the right permission.

Remember, some roles require access to only specific data sets, while others require a global view of all data. Compare the needs of a chief data officer to a customer success agent, for example. 

The best way to strike this balance is to set up access permissions based on specific roles and their data needs, rather than applying generic permissions. Industry leading tools and platforms can help with this, providing the functionality you need to manage permissions and ensure access for people who need it.

6. Utilise the right tools 

The right tools and data management software is key to establishing effective data management practices. Yet with an abundance of options available, many businesses make the wrong choices for their needs, resulting in systems that are: 

  • Far too complex to understand or use effectively
  • Unsuitable for the size or type of business — an enterprise solution being used in a startup, for example
  • Weak on security, representing a single point of failure against cyberattacks and data breaches

Comprehensive platforms hide complex data management processes under intuitive, easy-to-understand interfaces. They automatically clean and enrich data to ensure accuracy and quality. And they turn raw data into clear, actionable insights, allowing you to make smarter business decisions that drive efficiency and growth.

In short, using the best platform to store and visualise your data is crucial to success, so it’s worth utilising leading solutions like:

7. Work with experts 

Data management is a high-stakes process. The cost and implications of getting it wrong are considerable, with large-scale data breaches and cyberattacks representing an existential threat to businesses. 

On top of this, the requirements for effective and safe data management processes are constantly shifting in line with technology, regulations, and the threat landscape. Likewise, the skills and expertise needed to implement effective data strategies are constantly changing too. 

As a result, many businesses have skills gaps that are expensive and time-consuming to close, making it particularly tricky to handle data management in-house. But thankfully, there’s another option. By partnering with a trusted data expert, you can:

  • Enjoy the benefits of effective data management with none of the hassle or risk
  • Access cutting-edge data tools and platforms
  • Build data strategies that are tailor-made to your business needs
  • Implement data processes that are bulletproof and effective
  • Free up your resources to focus on adding value to your organisation 

Get the data management support you need with Jarmany

Whilst there are a number of best practices businesses can deploy in order to effectively manage their data, these require significant skills and expertise to implement correctly. Given the costs of hiring, training and retaining in-house talent, partnering with data analytics agencies is an increasingly attractive proposition.

As a leading data management and analytics agency, at Jarmany we have the knowledge and tools required to build data strategies that drive long-term growth and success. We work with some of the biggest organisations in the world, setting up processes that keep their data safe, clean, and accessible, thereby helping them make efficient, data-driven decisions

On top of that, we are a certified ISO 27001:2013 company, which means we have to demonstrate the ability to efficiently handle confidential customer data through robustly managed processes and adhere to stringent compliance checks.

If you’d like to find out how Jarmany can help you build a data management strategy and utilise your data to thrive in an ever-changing business landscape, get in touch with us today.

How to Use Predictive Analytics in Marketing

In today’s world of digital transformation, data is an essential commodity that directly fuels growth and success. And with an abundance of data now available, businesses that harness the power of data analytics to make better decisions will have a competitive advantage over those that don’t. For example:

  • Data-driven businesses are 58% more likely to beat revenue goals1
  • Data-driven businesses are three times more likely to report a significant improvement in decision-making2 

There is a wide range of data analytics techniques businesses can deploy in order to improve efficiency, ensure growth and obtain a competitive advantage. Amongst them, predictive analytics is one of the most advanced and potentially advantageous. 

In this article, we’ll explore predictive analytics and how it works, focusing on one of its most powerful use cases – marketing.

What is predictive analytics?

While it may sound complex, predictive analytics is relatively simple. It’s about using current and historical data to accurately predict future events, outcomes, trends, and behaviour.

These predictions are generated using a combination of statistics, predictive modelling, artificial intelligence (AI), and machine learning (ML), which analyse patterns and trends in data to predict possible future outcomes.

As a result, predictive analytics is often deployed to help organisations navigate during terms of uncertainty. This includes difficult economic times where consumer confidence drops, as predictive analytics can help forecast demand and identify where investments need to be made.

How does predictive analytics work?

Predictive analytics requires a good deal of planning, input, and expertise to yield results. Let’s take a look at some of the steps businesses can take to turn raw data into actionable insights about the future.

#1 Understand your goals

Before we even talk about data, it’s important to set the foundation for what you want to achieve. Understanding the business goals or issues you want to work towards is a critical first step upon which to build. In marketing terms, for example, underlying goals are likely to include: 

  • Understanding how customer behaviour may change in the future
  • Boosting revenue through cross-selling, upselling, and optimised pricing
  • Launching smarter, more effective marketing campaigns

#2 Develop a plan to collect the right data

Once you know what you want to achieve, it’s time to start looking at the data you’ll need to realise those goals. In many cases, customer data can be spread across a wide range of systems and platforms, so understanding what you have and where is key. 

Remember, predictive analytics often requires extensive work with large data sets. That’s why it’s crucial to ensure that you have the ability to collect and analyse sufficient marketing data to accurately predict outcomes.

Businesses should also consider broadening their insights with third-party data. For example, performance data from third-party retailer sites and additional industry data can help you obtain a better understanding of what benchmarks should be, how competitors are performing and the current state of the industry. 

#3 Analyse the data you have collected

Once you have collected the data you need, it’s time to analyse it. In order to make accurate and relevant predictions that ensure well-informed decision-making processes moving forward, predictive analytics needs data that is:

  • Clean
  • Complete
  • In a suitable format

#4 Create a predictive model

Predictive modelling is a core function in the predictive analytics process. Data scientists build them using algorithms and statistical models and then ‘train’ them using subsets of data. Once they are proven to work effectively, they can be applied to full data sets to generate insights. 

Examples of predictive models include:

  • Decision trees
  • Neural networks
  • Linear regression
  • Time-series analysis
  • Cluster models

Building a predictive model is a complex process requiring a great deal of expertise. A defective or poorly trained model will generate inaccurate predictions, which could lead to disastrous outcomes. 

#5 Use data for actionable insights

This is where the magic happens. Once you’ve outlined your goals, ensured that your data is relevant and clean, and built a predictive model that works, it’s time to put it all into action. Now you can use your predictive insights to guide your decision-making and give you a competitive edge.

Predictive analytics in marketing

So what is predictive analytics in marketing? Put simply, it’s the process of using customer data to make predictions about the future that help marketing teams become more effective and intentional in their decision-making.

In an ultra-competitive world, predictive analytics helps businesses to decode past buying habits, enabling them to project future buying habits. Armed with this information, marketers can make smarter, better-informed decisions, allowing them to:

  • Preempt future demand, behaviour, and trends
  • Create targeted, well-informed marketing campaigns 
  • Maximise their resources 
  • Engage existing customers and acquire new ones
  • Outmanoeuvre the competition
  • Identify when and where to target customers

Predicting future trends and behaviour with a high level of certainty brings huge advantages for marketing teams. Without the power of data, decisions around what to market, how, and to whom are essentially best guesses. 

Uses of predictive analytics in marketing

Now we know what predictive analytics means within a marketing context, let’s take a look at some of the ways predictive analytics can be used in order to transform marketing operations and improve outcomes:

  • Analysing and forecasting seasonal behaviour: Customers have different needs and preferences at different times of the year. Having a clear, data-driven understanding of seasonal behaviour allows you to plan for spikes in demand, focus marketing efforts in the right areas, and maximise revenue. 
  • Targeting the most profitable products to customers: Not all sales are equal. Some products have a higher profit margin than others. With predictive analytics, you can boost revenue by targeting customers with the most profitable products and services, helping you to squeeze more out of each sale. 
  • Conduct ‘what if’ scenarios: What if demand for your product dries up? What if customer behaviour shifts significantly in the coming years? What if a new competitor takes a large chunk of your market share? What if you decide to target a completely new customer segment next quarter? Predictive analytics allows you to answer questions like this by exploring different future scenarios and their likelihood, helping you to create long-term plans that are flexible and effective. 
  • Developing more effective marketing strategies: By leveraging the power of predictive analytics, you’ll have a better understanding of what customers want and how your offering lines up. You’ll be able to understand different customer segments and the potential value they bring, while pinpointing opportunities for upselling and cross-selling. All of this will help you design and deliver marketing campaigns that are smarter and more effective. 
  • Prioritising key customers: Predictive analytics can give you a clear picture of your customer’s potential lifetime value, helping you focus your marketing efforts on the ones most likely to bring repeat sales over time.

The benefits of predictive analytics within your marketing team

The ability to understand and plan for future events and trends before they happen is a real game-changer. Predictive analytics offers numerous benefits across your business, and particularly in marketing, such as:

  • Enhanced performance due to data-driven decision-making 
  • Increased lead generation and conversion
  • Improved funnel efficiency
  • The ability to build long-term plans that are flexible and futureproof
  • Greater ROI on marketing campaigns
  • An improved customer experience 
  • A better understanding of your customers and what drives their behaviour

Get the support you need to implement predictive analytics

The benefits we’ve outlined in this article aren’t guaranteed. Predictive analytics is a serious endeavour that requires the right skills, expertise, and systems to get right. If you don’t already have the skills and resources to implement predictive analytics in-house, you have two options: 

  • Close the skills gap by hiring fresh talent or upskilling current staff — both of which are expensive and time-consuming processes
  • Work with outside professionals who can guide you towards your goals with maximum efficiency and minimum hassle

In our experience, it pays to go with the latter option. As one of the UK’s leading data analytics agencies, here at Jarmany we can provide the tools and expertise you need to implement a predictive analytics marketing strategy that yields outstanding results. 

We use Azure Machine Learning and Python to deliver rapid and accurate marketing analytics results that help some of the biggest companies in the world make smarter, better-informed decisions. If you’d like to join them in harnessing the power of predictive analytics, get in touch with us today.

A Guide to Product Performance Analysis

The business world has become increasingly competitive in recent years, and as a result, it has never been more difficult for businesses and their products to stand out. To survive in an unforgiving market, it’s crucial to have a competitive edge. 

One way to make smarter decisions and outperform competitors is to harness the power of your business’s data. Companies that leverage data analytics often make better decisions, helping them become more efficient, cost-effective, and strategic.

That’s why, according to one survey, 99% of firms have invested in data initiatives.1 However, that alone isn’t enough to ensure success. Businesses also need to understand and implement best practices when it comes to data and utilise effective tools. 

In this article, we’re going to focus on a data analytics technique that can help you drive long-term success through smarter product decisions — product performance analysis.

What is product performance analysis?

As the name suggests, product performance analysis is the process of using data to analyse product performance and subsequently glean actionable insights. For example, you might learn:

  • Which customer segments your product appeals to most
  • The number of customers who return time and again
  • Which product details or features are being used and which aren’t
  • How you can streamline in-product processes

Product managers can then use these insights to guide their decision-making process, allowing them to optimise product performance, improve the user experience, and drive towards product-related KPIs.

Why is product performance analysis important?

In ultra-competitive markets, gaining insights into your product’s performance and the way users interact with it can give you a competitive advantage and help to ensure success in the long term.

On top of that, data can also provide insights into how products are performing on affiliate sites. Data analytics can help bridge this gap with web scraping technology that provides insights with regards to share of voice (SoV), the customer journey and how customers are interacting with these sites.

Without these and other data-driven insights, you are left with basic performance metrics like product sales or the number of new customers acquired. These metrics are useful as a fundamental indicator of the success of a product or service, but they don’t provide any context as to why a product is successful or otherwise.

Product performance analysis allows you to drill down into the factors behind a product’s success or failure, providing the following benefits:

  • Deep knowledge of how customers interact with your product: Understanding how your customers use digital products is critical to enhancing the user experience. With product performance analysis, product teams can see the processes customers follow to perform certain tasks – the buttons they click on and the pages they visit, for example. This allows them to pinpoint inefficiencies and make small adjustments that have a big impact on UX. 
  • Reduced user churn: Customers are the lifeblood of any business. And studies have shown that acquiring new clients is significantly more expensive that keeping existing customers, which is why businesses must do everything they can to reduce user churn.2 Improving their products is a great place to start. Product performance analysis gives you the insights you need to make products that are intuitive, beautiful, and relevant — products that understand user needs. If your customers are happy with your products, chances are they’ll stick around. 
  • A better understanding of who your ideal customer is: Using customer data, you can gain powerful insights into the type of people who are using your product. You can spot common characteristics that allow you to build an understanding of what your target customer looks like. With these insights, you can build products, features, and marketing campaigns that target the right people, allowing you to boost conversion rates and keep key customers happy.
  • Understanding of what drives user engagement: Seeing which product features your customers are using is one thing. Understanding the factors behind that usage is another altogether. With product analytics, you can gain valuable insights into the way users discover and use certain features. This allows you to fine-tune processes within your product, and design your customer onboarding in a way that maximises product usage. 

How does product performance analysis work?

Now we’ve discussed some of the benefits, let’s focus on how to conduct product performance analysis. Here are some key steps to understand before you implement them in your business.

Align data needs with business goals

This is crucial. Before you start thinking about results, it is critical that you understand and outline your business goals. This will help you define the specific metrics you need to track and the data sources you will use. 

Build a data tracking plan

One way to ensure that your analytics efforts are coherent and in line with your wider business goals is to build a data tracking plan. Essentially, a tracking plan helps you to:

  • Outline critical events and user actions you need to track in order to capture relevant data – examples include clicking buttons, making a purchase, loading a page, or performing a search
  • Determine the methods you’ll use to track this data
  • Ensure that your data tracking efforts are aligned with your wider business goals
  • Prevent key details from being missed
  • Keep everyone on the same page during data collection and analysis

Collect user actions

Once your plan is set up and you have determined the user actions you want to track, it’s time to implement the plan. This involves instrumenting your product to collect the relevant data. Make sure you understand which user actions relate to which key metrics. 

You can then analyse this data to gain actionable insights that help you make smarter decisions about the direction of your product and marketing operations. 

Ensure that you have the right skills and expertise 

Like any branch of data analytics, product performance analytics is an esoteric discipline that requires the right skills and expertise to be successful. Data analytics is also a fast-evolving discipline, making it difficult to keep up with the latest trends and techniques.

A poorly executed analysis will likely lead to inaccurate or irrelevant data points. This can lead to false assumptions, which in turn leads to poor decision-making. 

To reap the benefits of product performance analysis, you will need to make sure you have the right skills in-house. In many cases, this means hiring new staff or upskilling existing ones. 

Alternatively, you can outsource your analytics processes to an external partner, allowing you access to all the benefits of product performance analytics without having to make major internal changes. 

Use the right metrics and analysis techniques

There is a whole range of metrics and analysis techniques you can use when performing a product performance analysis. The approaches you choose will depend on your specific product and the business goals that are underpinning your efforts. Here are some examples:

  • Funnel analysis: This type of analysis maps out the processes users take on your website to achieve a certain goal, such as making a purchase or signing up for something. Tracking the user journey through different phases of the funnel allows you to spot pain points and optimise the UX. 
  • Trend analysis: Using current and historic data, trend analysis provides you with insights into customer behaviour and changes in demand. It also allows you to see the customer segments that your product resonates with most. Ultimately, these insights provide you with a stronger understanding of your customers, allowing you to take a more strategic approach to product development and customer retention.
  • Customer journey analysis: This process allows you to analyse the complete customer journey – from awareness through activation all the way to advocacy. At each stage of this journey, there are various touchpoints and actions that move customers on to the next. Customer journey analysis allows you to see which stages of this process are working and which aren’t, helping you to optimise the journey at all stages.
  • Cohort analysis: Cohort analysis takes your customer data and divides it into subsets – or cohorts – based on shared characteristics. For example, users could be segmented into cohorts based on their behaviour within a product, their sign-up date, or whether they make repeat purchases or not. These cohorts can then be further analysed, allowing you to understand the motivations behind different groups’ decisions and behaviour. This helps you to target key cohorts and take a more flexible, strategic approach to meeting customer needs.
  • Retention analysis: This process helps you to understand the percentage of customers that stick around, which is a vital metric for understanding product success. You can apply various timeframes to measure customer retention rates, depending on your needs.

Get the most out of product performance analysis

As we have already touched on, product performance analysis — and data analytics in general — is a complex process that requires a specific set of skills and expertise. Given the dynamic nature of this discipline, many companies have a skills gap that must be addressed if they are to successfully handle analytics in-house. 

Fortunately, there are ways businesses can overcome this and benefit from the power of data analytics. For example, partnering with a trusted third party that specialises in data analytics allows you to maximise the potential of techniques such as product performance analysis.

At Jarmany, that’s exactly what we offer our clients. As one of the UK’s leading analytics specialists, we have the expertise, skills, and tools you need to turn raw data into actionable insights. We also work with outside partners to provide additional insights around performance on third-party retailer sites, giving you a full picture of product performance.

By working with us you can gain all the benefits of a thorough product performance analysis with none of the risk or hassle. If that sounds interesting, contact us today to find out more.


What is Predictive Analytics? An Introductory Guide

The amount of data available to companies has exploded in recent years. Driven by the wide-scale digitisation of everyday processes, it’s now possible to gather information on an unprecedented scale — and this is a trend that looks set to continue.  

According to one survey:

  • The volume of data created, captured, and consumed worldwide in 2021 was approximately 79 zettabytes
  • By 2025, this number is predicted to grow to 181 zettabytes – a 129% increase1

Given the volumes of data they now have access to, businesses are searching for ways to leverage it in order to obtain a competitive advantage with well-informed, rapid decision-making that can ensure consistent and high-quality outcomes. That’s exactly what predictive analytics can deliver when implemented correctly. 

This article will offer an introduction to predictive analytics, examine how it works, and outline some of the benefits it can bring to your business.

Defining predictive analytics

Predictive analytics is the process driven by propensity modelling that looks to transform historical and current data into future insights. It uses a combination of artificial intelligence (AI) and machine learning (ML) techniques, which are able to identify patterns and trends in data.

When done right, predictive analytics offers a reliable, transparent, and accurate way to predict future events, trends, and outcomes — offering a competitive advantage to businesses that leverage it effectively. 

How does predictive analytics work?

Predictive analytics is a complex process that requires a good deal of planning and attention. Before you can think about results, it’s important to take a step back and understand what business goals you are looking to achieve. For example, your goal might be to:

  • Avoid future skills gaps
  • Understand how customer behaviour might change over time
  • Ensure the correct staffing levels to meet customer demand
  • Maximise revenue through optimal pricing
  • Drive operational efficiencies to ensure resources are distributed to the right place at the right time 

Whatever your goals may be, you’ll need to make sure you have the right data to model accurate predictions. Predictive analytics requires large data sets to generate clear, actionable insights. The cleaner, more accurate, and more complete the data, the more reliable those predictions will be. 

Once you have the right data, it is then fed into neural networks or statistical models designed to spot trends and predict or identify outcomes. For example, a predictive maintenance model assesses the chances of essential equipment breaking down, allowing you to foresee when replacements will be needed. 

Some other real-life examples of this process in action include: 

  • Airlines use predictive analytics to decide how many tickets to sell at a specific price for a flight
  • Hotels predict the future number of guests they can expect on any given night in order to adjust prices, maximise occupancy, and increase revenue
  • Marketers are able to determine cross-selling opportunities based on customer responses and purchases
  • Banks use it to determine a person’s credit score, affecting their eligibility for credit cards and loans
  • Insurance companies use it to understand the chances of a claim being made, allowing them to adjust premiums when pricing a policy

Predictive analytics best practices

Knowing how predictive analytics works and the outcomes you want to achieve is one thing, but it’s not enough to get the desired results. That requires skills, knowledge, and the implementation of processes that help drive success.

So, let’s take a look at some best practices that businesses can apply to maximise the effectiveness of predictive analytics, resulting in accurate and reliable outcomes that contribute toward a competitive advantage.

Ensure that your data sets are large and valid  

Predictive analytics relies on historical data to predict future outcomes. Over time, the machine-learning algorithms and models used to generate predictions fine-tune themselves, becoming increasingly more accurate. But the original data that you start with is all-important.

If you use data sets that are small, incomplete, or even invalid, the quality and accuracy of those predictions will be severely compromised. Furthermore, trusting predictions based on poor or incomplete data can have severe consequences for businesses, as decisions based on inaccurate data are likely to have adverse outcomes.

Before you embark on your predictive analytics journey, make sure that your data sets are large enough to work with. You can’t make accurate predictions based on a week’s worth of data. You should also ensure that the data sets you use are complete, clean, reliable, and valid. 

Identify and draw from the best data sources

Not all data is equal. In many cases, you’ll have data spread across multiple different sources, including a business’s various platforms and systems. As a result, some datasets will inevitably be better quality or more appropriate than others.

In many cases, predictive analytics models have to be dynamic, meaning they take account of new data in real-time, adjusting their models and predictions accordingly. By identifying the most suitable data streams, you can work to guarantee the best predictive outcomes. 

Present predictions clearly

High-quality data visualisation is an essential component of effective predictive analytics. That’s because the predictions and insights generated need to be communicated in a way that is:

  • Clear
  • Easy to understand
  • Actionable

This is essential for all stakeholders — whether it’s the decision-maker whose job is to turn insights into action or the executive who wants a clear understanding of ROI. Presenting the analytics results in a straightforward and visually pleasing way helps ensure stakeholder buy-in and facilitates seamless decision-making processes.

The benefits of predictive analytics for business

In the words of Angela Ahrendts, the former senior vice president of retail at Apple, “consumer data will be the biggest differentiator in the next two or three years. Whoever unlocks the reams of data and uses it strategically will win.”2

So, the message is clear — businesses looking to stand out from the competition and drive long-term success need to find ways of utilising their data effectively. As we have outlined above, one of the best ways of doing just that is with predictive analytics.

But what are the actual benefits of predictive analytics? And how do these translate into successful outcomes for businesses? Let’s take a closer look. 

Improve decision-making

In a world of constant and rapid change, human experience and intuition are less reliable than ever as the basis for decision-making. While they have a role to play, decision-making needs to be informed by other factors, including data. 

Predictive analytics leverages massive data sets to predict trends and events before they happen, taking into account the multitude of competing factors that influence outcomes. The resulting insights allow you to make smarter, better-informed decisions about the future. 

Predict demand

Without a reliable way to predict demand for a particular product or service, it can be difficult to know how to organise and manage supply chains. Too little of a product and you’ll lose customers and revenue. Too much and you’ll waste time and resources. 

Predictive analytics takes into account current and historical trends in customer behaviour to allow you to meet supply with demand in the most cost-effective and efficient way.

Optimise pricing

Pricing goods and services aren’t as simple as it seems. You need to maximise revenue while remaining competitive. Set prices too high and customers will look elsewhere. Set them too low and you’ll cut into your profit margin. 

Predictive analytics allows you to set prices at the optimum level at any given moment, helping you to squeeze the maximum amount of revenue from your offering.

Improve customer retention

Customers are the lifeblood of any business. But in an ever-changing world, how do you keep them happy and loyal to your brand? Predictive analytics ensures that your customers stick around for longer by predicting:

  • Churn before it happens, allowing you to re-engage at-risk customers
  • Customer lifetime value, allowing you to focus your efforts where it matters most
  • What customers are likely to buy next, allowing you to target them with marketing campaigns and cross-selling opportunities

Reduce risk

Predictive analytics provides you with powerful insights into the likelihood of future events and how trends and behaviour may change. This allows you to prepare for the future, mitigate risk, and adapt to change seamlessly. 

Gain a competitive advantage

Gaining an accurate picture of the future is a game-changer. Predictive analytics helps you to see around corners, allowing you to increase efficiency and productivity. Put simply, businesses that leverage the power of predictive analysis stand to gain a competitive advantage over those that don’t.

Getting started with predictive analytics

Predictive analytics is a serious business. To be able to trust your data-driven and data-informed decisions, you need to be able to trust the insights that guide them. But such insights are only as accurate or relevant as the data used to generate them. 

Remember, the consequences of getting predictive analytics wrong are significant. Using poor data or carrying out poor analytics processes can result in poor decision-making and negative outcomes that set your business back considerably.

While it is possible to tackle predictive analytics in-house, it requires time, money, and data science expertise to get it right. In many cases, you’ll need to hire data analysts or upskill your current staff in the field of data analysis, diverting resources away from critical business objectives.

This is why it pays to partner with third-party providers like Jarmany. As one of the UK’s leading data analytics companies, we help our clients get the most out of their data, allowing them to enjoy all the benefits of predictive analytics without hassle or risk. We use cutting-edge analytics tools to deliver forecasting solutions that provide operational insights for some of the biggest companies in the world. If you’d like to find out how we could do the same for your organisation, get in touch with our team today.

A Guide to Data Consolidation: Tips, Techniques and Benefits

It’s a simple fact that businesses today have access to a goldmine of data. Driven by process advancements and new technology that impacts virtually every area of a business, it is now possible to capture more data than ever before.

This represents a huge opportunity. Data unlocks powerful insights, allowing you to make smarter decisions and predict outcomes with a higher degree of certainty than ever before. As a result, data has become a vital tool for the world’s leading organisations — Linkedin Chief Executive, Jeff Weiner, once admitted that “data really powers everything that we do.”1

Due to the powerful insights it can unleash, businesses are increasingly looking to analyse data sets as a collective and find relationships between them as opposed to looking at them in isolation. However, growing volumes of data spread over different systems make leveraging it to achieve desirable outcomes difficult, and 95% of businesses say that their inability to understand data is holding them back.2

That’s where data consolidation can help.

In this article, we’re going to look at the concept of data consolidation, common data consolidation techniques and the benefits they can provide. Let’s get started. 

What is data consolidation?

In the simplest terms, data consolidation is the process of gathering and combining data from multiple sources into a single location. It’s a relatively new concept that has risen to prominence in recent years due to the sheer amount of data now available to businesses. 

For example, the average business will collect data from a wide range of business systems and platforms. This includes, but is not limited to:

  • Customer relationship management (CRM) software
  • HR systems
  • Product databases
  • Sales-related data
  • Content management systems (CMS) 
  • First-party website app-related data
  • Third-party sales data, including sales data from affiliate sites
  • Manually created data, such as Excel sheets, CSV files, and PDFs

Data consolidation is all about taking all of this data and moving it into a single location like a data centre. This process allows you to access and work with data from a single point of access, facilitating the process of turning raw data into actionable insights that can ultimately drive more effective decision-making and long-term success.

Data consolidation techniques

Before delving into the benefits of data consolidation and the best practices businesses should look to implement, it’s first crucial to understand the various techniques available. Remember, there are a number of different processes that can be utilised in order to consolidate data.

The approach an organisation chooses will affect its overall strategy and the tools required to make it a reality. In this section, we’re going to focus on three of the most widely used data consolidation techniques.

Extract, Transform, Load (ETL) 

As the name suggests, ETL is a data transformation process that consists of three parts: 

  1. Extract: The first step is to extract the data from a source system. The data is then consolidated in a single format in preparation for the next step.
  2. Transform: Certain processes are then applied to the data in preparation for the final stage. These may include cleansing to remove inconsistencies and errors, sorting to organise the data by type, and removing duplicates to ensure redundant data is discarded.
  3. Load: Finally, the transformed data is loaded into a target system – for example, a data centre or database – where it can be analysed and monitored more easily. 

It’s also important to be aware of the fact that there are two ways that you can perform the ETL process — real-time ETL and batch processing. Let’s take a look at the differences:

  • Real-time ETL: As the name suggests, real-time ETL transfers data into the target system as it is captured. It does this using a process called change data capture (CDC), which recognises changes in the data source. 
  • Batch processing: Batch processing transfers data in bulk. The data is collected and stored during a certain window of time, and then transformed in one batch to the target system. This is ideal for high-volume, repetitive datasets. 

Data virtualisation

Data virtualisation is a process that sees an organisation’s data integrated from across numerous different sources without replicating or moving it. This approach makes it possible for an application to retrieve and manipulate data without requiring technical details about the data, such as how it is formatted at source, or where it is physically located.

Essentially, data virtualisation allows data to be stored in different data models and integrated virtually. This provides users with a consolidated view of their organisation’s data from which they can look to glean valuable insights.

Unlike the ETL process, data stays in its original source following data visualisation. As a result, the data can still be retrieved by front-end technology such as applications, dashboards, and portals for future use.

Data warehousing

Data warehousing involves consolidating data from multiple sources into a centralised location, known as a data warehouse. This data can then be used for ad hoc queries, business intelligence, analytics, and to uncover various critical insights. 

Over time, data warehouses build a vast amount of historical data, and as such come to act as a single source of truth for a business. Having all of a business’s data located in one place makes it much easier to identify trends and subsequently create strategies for enhancing business operations and optimising outcomes. 

On top of this, data warehousing provides businesses with the capacity to categorise data, facilitating improved analysis relating to a particular function or process, including:

  • Sales
  • Recruitment
  • Marketing

The benefits of data consolidation

Now we know what data consolidation is and the techniques you can use to achieve it, let’s dive deeper into the benefits it can bring once implementation is complete. These are numerous and wide-ranging and include:

  • Reduced costs: We’ll start with a big one. Data consolidation directly boosts your bottom line by reducing maintenance costs and eliminating redundant and time-consuming processes. 
  • A single source of truth: Data consolidation provides a single source of truth for all data-driven decisions. With all data standardised and processed according to the same rules, you gain a level of clarity and accuracy that would be impossible if your data were scattered across multiple platforms and systems.
  • Enhanced oversight: Consolidating data effectively allows organisations to obtain a 360-degree view of performance across categories and revenue streams whilst combining first and third-party data sources. 
  • Simplified management and maintenance: Having all your data in a single repository allows you to manage and maintain it with comparative ease. There are fewer points of failure, meaning fewer outages, less downtime, and less risk. 
  • Improved security: Data consolidation helps to reduce the attack landscape, allowing you to keep your data more secure. In the unfortunate event of an attack, data consolidation also improves disaster recovery capabilities. 
  • Support for compliance: In a world of ever-changing rules and regulations, it’s never been more important to set your data infrastructure up for success. Data consolidation makes it easier to ensure that your data collection and data management efforts meet strict compliance requirements. 
  • Rapid analysis: If your data is spread across multiple systems, it can be virtually impossible to undertake a rapid analysis. Data consolidation allows you to get a fast and accurate reading on all or any of your data. 
  • Improved productivity and flexibility: Data consolidation streamlines your IT and data infrastructure while improving data quality and accuracy. This allows you greater productivity and flexibility than ever before.

Data consolidation best practices

Despite the numerous benefits of data consolidation outlined above, it can be a time-consuming and complicated process. That’s why it’s also crucial to get it right. Otherwise, an organisation’s data-informed processes and efficiency are likely to suffer.

As a result, there are a number of best practices that businesses should look to implement in order to minimise issues and maximise the return on investment (ROI) that data consolidation can offer. The most significant of these include:

  • Effective planning: As with any endeavour, comprehensive planning is critical for success. Ensure that you have a realistic timeline and budget – and set measurable targets for success.
  • Ensure data cleanliness: It’s vital to clean data prior to its integration into data models and categories for analysis. That means aggregating data into the correct levels of granularity to make it easier to manage, maintain and subsequently digest.
  • Working towards continuity: Data consolidation isn’t just a one-off process, it’s a continuous one. That’s why it’s pivotal to build an ongoing data consolidation process that will serve the business over the long run.
  • Keep data raw: Keeping data in its original form helps to keep it accessible, prevents continual referrals back to the source system, and makes it easier for data tables to be repopulated on demand if required. 
  • Using the right tools: The right technology helps to ensure that the process of data consolidation is smooth and error-free. For example, if you are gathering data from multiple locations, you’ll need an ETL tool that can connect to them all.
  • Getting the right skills: Data consolidation is a complicated process that requires particular skills and expertise. While it’s important to understand and address any skills gaps before you undertake any large-scale changes to your data infrastructure, the process of hiring, training, and retraining staff can be expensive and time-consuming.
  • Working with outside experts: By partnering with external data experts, you’ll save the time and cost of equipping employees with the skills needed to implement data consolidation processes.

Getting started with data consolidation

The benefits of data consolidation are numerous. Perhaps the biggest challenge that stands in the way of businesses realising those benefits is a lack of expertise, with a significant skills gap in the market making it difficult for businesses to hire and retain top talent.

Unfortunately, this means that undertaking data consolidating processes in-house can be time-consuming and expensive. Furthermore, trying to implement data consolidation without the required knowledge and expertise to be successful can result in poor data, poor insights, and ultimately poor business performance.

This is why it pays to work with experts. By teaming up with an external partner, you can implement complicated and nuanced data processes with maximum efficiency and minimum hassle, saving you valuable time, money, and resources. 

At Jarmany, we help businesses build powerful data strategies, including data consolidation. By partnering with us, you can gain access to the expertise, experience, and tools you need to harness the power of data — allowing you to make better decisions, boost efficiency, and gain a competitive advantage in your industry. If you’re ready to start the process of consolidating your data in order to harness insights and drive long-term business success, contact us today.

Six Benefits of Data-Driven Decision-Making

“Data is a force, and that force can turn into something of a burden — or something that truly liberates you, your business and the things that you do. Data, when tamed, can be an asset like nothing else.”1 – Sri Shivananda, SVP and CTO of PayPal. Your ability to tame data is ultimately about your ability to use data to drive effective decision-making. Whether or not your company succeeds or fails comes down to the decisions people make – so, this isn’t something to take lightly. With so much at stake, how can businesses ensure that the decisions they make come with less risk and increase their chances of long-term success? One survey reported only 20% of employees believe their organisation excels at decision-making, and more than half feel time dedicated to decision-making is used ineffectively.2 As a result of these struggles, more businesses are looking to implement data-driven decision-making. It’s not hard to see why, as data-driven organisations are:
  • 23x more likely to acquire customers
  • 19x more likely to be profitable3
In this article, we’ll explain how data-driven decision-making can transform your business, helping to drive productivity, efficiency, and consistency across all areas of the organisation.

What is data-driven decision-making?

Data-driven decision-making is the practice of using insights derived from data to make better-informed business decisions, optimise strategies and drive successful outcomes. It sounds simple enough on the surface, but the decision-making part is the endpoint of a complex process involving the following steps:
  • Data collection: Raw data is harvested from multiple sources across the business
  • Data cleaning: The data is then cleaned and sorted in preparation for analysis
  • Data analysis: Next, the data needs to be analysed before the results can be shared with key stakeholders
  • Data visualisation: The results of the analysis stage are presented with a visualisation that highlights patterns and insights from the data
Now that we’ve covered what data-driven decision-making involves, let’s dive deeper into some of the benefits of becoming a data-driven business.

#1: Make better-informed decisions

Traditionally, businesses relied on the experience, wisdom, and gut feeling of key decision-makers to guide them in the right direction. This put a tremendous amount of responsibility and pressure on decision-makers and magnified the potential harm caused by poor decision-making. In a world of constant change and unpredictability, this approach becomes even riskier. Today, there is a different way. By harnessing the power of analytics, we can make smarter decisions based on data — decisions free from bias or human error. For example, according to a recent report, data-driven decision-making makes your business:
  • 23 times more likely to acquire customers
  • 6 times more likely to retain them
  • 19 times more likely to be profitable as a result4
And that’s just scratching the surface. Whether it’s customer acquisition, process management, marketing or e-commerce, companies that leverage data-driven decision-making have a huge advantage over those that don’t. Ever wondered how Amazon manages to ship products so quickly despite an ever-increasing customer base? It’s simple. The largest e-commerce business in the world leverages the power of data analytics to predict demand. In other words, they know what people are likely to purchase and when. They then use these insights to ensure that distribution centres are well stocked.

#2: Improve productivity 

Data-driven decision-making has a direct impact on business outcomes. By making better-informed decisions, you can streamline processes, reduce inefficiencies, and predict outcomes — all of which boost productivity. One such example is econometric modelling, which involves understanding the relationships between variables to forecast future developments. Econometric modelling allows you to understand with a high degree of certainty how adjusting a process or modifying an action might have an impact on sales, turnover, or other key business metrics. Data analytics allows you to build accurate models that can predict future outcomes across a wide range of business functions. Here are some examples:
  • Understanding the variables that result in productivity allows you to create a workplace environment conducive to high-output, high-value performance
  • Understanding skills gaps, talent shortages, and potential flight risks allows you to take a proactive, rather than a reactive, approach to recruitment and succession planning
Whatever the department or process, basing your decision-making on data rather than human instinct results in improved productivity. For example, in an attempt to boost employee engagement and productivity, Google — a company well known for taking a data-driven approach — created a People Analytics Department. Using performance reviews and employer surveys, the department crunched the data to understand what makes a great manager, settling on eight key behaviours. They then measured managers’ performance against these behaviours, helping to make them even more effective at motivating and leading their teams. Motivated employees perform better and stick around longer.

#3 Optimise campaign performance

In highly competitive and evolving markets, well-thought-out and expertly executed marketing efforts can go a long way to ensuring a competitive advantage and long-term success. However, that requires businesses to obtain a number of significant insights into their customers. To improve the outcomes of their marketing tactics, businesses need to acquire an enhanced understanding of:
  • Who they should target
  • When they should target people
  • How users are engaging with marketing material
  • How and where to make optimisations that drive success
Within the context of the end of third-party cookies, which provide powerful insights that enhance and inform digital marketing efforts, this is a particularly important focus for businesses at the moment. Fortunately, data can provide a solution. In the cookieless future businesses now face, first-party data looks set to play a big role in helping businesses make crucial marketing decisions. That’s because by collecting data and gleaning insights using predictive analytics, businesses can better understand their customers and subsequently target them more effectively. As a result, data holds the key to making effective decisions around marketing tactics moving forward. In simple terms, data is allowing businesses to enhance their marketing approach and plan for a future without third-party cookies simultaneously.

#4: Drive internal efficiencies

Data-driven decision-making also increases efficiency across your business. All those wasted hours discussing options in an attempt to understand risks and outcomes are saved. Now, in the era of big data, hard work is done for you. In the long term, this frees up business leaders to focus on other important areas, including market research or developing their products or services. The process of collecting and analysing data also shines a light on any inefficiencies in your business, allowing you to overhaul processes, systems, and strategies that don’t work — and replace them with ones that do.

#5: Enhance internal accountability

Data analytics also helps to boost transparency and accountability around the decision-making process. Before, there was no clear way to track how decisions were made or by whom, or to fully understand the impact of those decisions. With data-driven decision-making, there is a clear framework for accountability, and the ability to understand and measure the impact of decisions. Improving both transparency and accountability can bring a variety of benefits to your company, including:
  • Fewer internal conflicts or disagreements
  • Greater efficiency
  • More trust and confidence
These benefits impact all levels of the business. At the leadership and executive level, data analytics provides an objective framework for making decisions, free from bias or the internal politics that plagues many organisations. For regular employees, it provides clarity around how the business is run and how decisions that impact them are taken.

#6: Strive for consistency

Genuine consistency is incredibly difficult to apply to human processes. That’s because, for humans, opinions are always going to be influenced by a myriad of external and internal factors. We have good days and bad days. We are swayed by our emotions, which are in a constant state of flux. We are biased, whether we are aware of it or not. All of these factors make our behaviour inconsistent and erratic to varying degrees. While context is important, you don’t want gut instinct or outside influences to have too much influence when you make crucial business decisions. With customers, sales, and ultimately the survival of your organisation at stake, you need to be able to reach decisions without being swayed by bias or emotion. That’s why decisions driven and informed by data are so important to a successful business. They provide both accuracy and objectivity when you need it most, which allows a level of stability and consistency to decision-making that would otherwise be impossible to achieve.

Get the support you need to unlock data-driven decisions

Data-driven decision-making offers powerful benefits, enabling you to build a more efficient and effective business across all departments. But realising these benefits requires resources and expertise. While you can handle data analytics in-house, that isn’t always the best approach, mainly due to the skills required. Businesses often misinterpret the true scope of data analytics. Without specialist skills and expertise, delays and bad outcomes become more likely. That’s not all. Looking after your own data strategy can be:
  • Expensive: You need to invest significant time and resources into infrastructure, training, and recruitment
  • Time-consuming: The process of collecting, cleaning, analysing, and visualising data can be complex and arduous
  • Potentially risky: Poor-quality data and inefficient systems can result in false assumptions, which, when used to make decisions, lead to undesirable outcomes
By partnering with an experienced data analytics provider, you can get all of the benefits of data-driven decision-making with less hassle and risk. That’s what we offer at Jarmany. Our philosophy is simple — when you work with data the right way, nothing is unpredictable and no threat or opportunity to improve will ever be missed. We can help you harness the benefits of a data-driven approach to decision-making and go one step further, guiding you to incorporate experience and specific business context alongside data to make decisions. That’s what we like to call being data-informed. Whether you are looking to implement a data-driven culture, build an effective data platform, apply data science techniques, or access top expertise in the field of data analysis, we’re here to help. Your business has unlimited potential, and data holds the key to unlocking it. If you’d like to learn more about how we can help your organisation, get in touch today.

5 Tips for Improving Your Website UX

The trends we have seen towards more online shopping in recent times show no signs of slowing down. According to the Office for National Statistics, online retail reached record levels in 2021, accounting for 35% of all sales in the UK.1

This brings both opportunities and challenges for businesses looking to take advantage of the eCommerce boom. The way consumers purchase products and services has changed, but so have the demands on businesses. For example, 88% of online shoppers surveyed say they wouldn’t return to a website after a bad user experience (UX).2

Why is UX so important? It’s all about fulfilling the user’s needs. A positive experience creates loyal customers, which in turn facilitates increased sales and other positive outcomes. Furthermore, a poor website UX can do real damage. 67% of users say a poor website UX negatively impacts their opinion of a brand.3

That’s why in this blog post, we’re going to look at five ways you can improve the UX your website offers. Let’s dive straight into the first one.

#1 Use design wisely

Your website’s design is critical to user experience. For example, incorporating images, videos and icons onto a business’s website is a great way to visualise a brand’s identity and unique personality to prospects. It can also help users absorb information — our brains process images approximately 60k times faster than text.4

However, it’s not as straightforward as filling a site with images and expecting an exceptional UX to materialise. Like everything else included on a site, images need to be used efficiently. Images, videos and icons all need to present information in a digestible way that attracts attention and ultimately drives clicks and views. Unfortunately, there are several common mistakes made that can actually harm UX, including:

  • Too much: While images can play a pivotal role in a site’s UX, too many on one page can confuse navigation. This ultimately makes it difficult for users to navigate and digest important information, increasing bounce rates. It can also cause your page load time to increase, which is bad for SEO.  
  • Using the wrong images: Stock or generic images are now easily identifiable for the average internet user. As a result, businesses should look to utilise their own images when possible, as this can demonstrate a unique identity and build consumer trust.
  • Poorly located design elements: Even if you’re using the right images, they need to be strategically placed. If images aren’t adding value or context to the rest of a site’s content, they might negatively impact UX.
  • Incorrectly sized images: Poorly sized images can create a confusing user experience. For example, we’ve found that including large imagery at the top of pages can increase bounce rates if users don’t see the info they want when landing on the page.  

While images, videos and icons can contribute to a great user experience, they can also make a website difficult for users to navigate if they are utilised ineffectively. This can detract from the experience a site offers and prevent prospects or returning customers from taking the next step on the customer journey.

However, there are methods available to ensure a site’s use of images is optimised. One of the most effective is data analytics, specifically multivariate testing. This technique helps determine which combination of variables, in this case images, creates an experience that helps users successfully navigate a site, providing actionable insights for optimisation. By combining this with heat mapping, you can better understand how users engage with your page’s design features, and figure out which images, videos and icons engage users and which they seem to ignore. 

#2 Deploy impactful CTA’s

Impactful calls to action (CTA) are a tried and tested method of providing visitors with a next step that most makes sense for their journey, and eventually to become a lead or a customer. In the context of the UX a website offers, CTAs need to be utilised to guide users, helping them navigate to where you want them to be.

Despite their wide usage, you’d be surprised how often websites get CTA’s wrong. It’s pretty simple. Whenever you include a CTA on your site, you need to consider:

  • Colour: Although it may not seem important, using eye-catching and visually pleasing colours can really enhance the effectiveness of CTAs. For example, the Laura Ashley website increased checkouts by 11% by testing colour variations to find the optimised experience for users5 . 
  • Font: As with colour choices, the fonts used for website CTAs can significantly impact the user experience. By using a clear and stylish text design across CTAs, websites can start to drive more users to take the next action they have to offer. 
  • Placement: Where CTAs physically appear on a site is crucial to the UX on offer. CTAs should appear in a relevant section of the page after users have digested the information on offer. Surrounding CTAs with white space that gives them room to breathe can also increase their effectiveness.
  • Functionality: You should make filling out a form on your website as simple as possible. There is no point in directing users through more pages than necessary. You also need to make sure that your CTAs actually work. This might be obvious, but you don’t want to lose conversions because you’ve embedded the wrong link.   

Each of these factors can influence a user’s thoughts, feelings and perceptions of a website, so getting them right is essential. The key is testing what works well and what doesn’t in the same way it’s possible to test variations of images we’ve outlined above.

There are additional, tried and tested methods that can be deployed to enhance the experience CTAs offer on a website. In order to give CTAs the best possible chance of driving conversions, they should always be:

  • Time-sensitive
  • Emotive
  • Actionable 

#3 Be mobile-friendly

Just as consumers’ shopping preferences have changed with the advent of eCommerce, so has the way they make purchases online. For example, in the UK, 46.5% of consumers utilise online shopping using a mobile device.6  

What this means is that businesses need to carefully consider the mobile browsing experience that they can offer to users. As a result, several aspects of websites need to be optimised with mobile users in mind. This includes:

  • How images are used
  • Image specs
  • The typography deployed
  • Site navigation
  • General layout
  • How the hover action works 

Tracking and analysing website visitors’ behaviour is also a crucial element of optimising the experience on offer to users on mobile devices. In order to identify where and how overall experience can be improved, it’s essential to always keep an eye on:

  • Visits
  • Bounce and exit rates
  • The browsers and devices being used
  • The number of repeated visits
  • How many pages users visit
  • Order conversions
  • Page funnelling 

Pro tip: It’s worth noting that desktop conversion rates remain higher — indicating that while people like to browse on mobile, they still want to make that purchase on a computer.7 Mobile-friendly doesn’t mean forgetting about desktop users. Your site needs to be able to accommodate both.

#4 Web errors

There are few things more frustrating for a user than site errors that detract from their overall browsing experience. While there are numerous errors to watch out for, the most common that detract from a smooth UX include:

  • 404s, the status code that flashes up when a requested page is unavailable
  • Broken links
  • Broken images
  • Delayed page loading times

If users can’t navigate a site seamlessly and access the pages they want when they want to, potential customers will disappear. Fortunately, there are various things businesses can do to try and minimise and eliminate the impact of these types of errors, including:

  • Identifying 404s: The easiest way to catch 404s before they cause problems is with Google Search Console (GSC). By proactively checking your site for errors using GSC, you can significantly reduce the disruption users have to endure and maintain a smooth UX on your site. 
  • Designing 404 pages: An error page is unappealing to users and prevents seamless browsing. While you want to avoid 404s, a friendly, personalised error page can help visitors navigate your site easily when an error does occur, thereby encouraging them to keep browsing.
  • Catching and fixing broken links: Free tools like Google Analytics make it easier to quickly locate broken links on a site, facilitating the process of fixing links, either internal or external, to ensure an optimised UX at all times. 
  • Compressing images: There are numerous ways to optimise loading times, but perhaps the most effective is focusing on imagery. Large images can delay loading times, so compressing images and testing the impact that has on loading times can help to ensure users don’t face delays when they browse. 

Pro tip: Although 404s are very common, don’t forget that there is more than just one kind of error message. 401s, 400s, 403s and 500s can all equally damage user experience. Make sure to audit your site for these errors as well and then take steps to minimise their impact.

#5 Utilise the power of data

Whilst there are numerous ways to improve website UX, each should be underpinned by the most powerful tool at your disposal — data. Establishing which elements drive engagement and a seamless experience provides the insights required to enhance website UX long-term. In the era of big data, you can do all that and more with effective analytics.

As we’ve seen with some of the strategies outlined above, data and analytics can help businesses identify actionable insights that can drive optimisation. Within the context of improving a site’s UX, data is instrumental when it comes to:

  • Multivariate testing: This allows businesses to examine how each variable on a page impacts the experience it offers. This information can be used to pinpoint where UX isn’t currently being optimised by the web design or layout, facilitating informed decision-making when it comes to making improvements. 
  • A/B testing: By focusing your analysis on just one variable, you can dig further into the details and ensure that you are making decisions able to drive the outcomes you need.  
  • Customer journey analysis: Carrying out an analysis of the customer journey is all about looking at consumer behaviour across touchpoints and over time. By better understanding the user’s current experience, businesses can obtain insights into how it can be optimised moving forward.
  • CRO: Generating conversions is a central function of your website. Everything from site structure and branding, to the use of overlays and promotions, can transform your conversion rate. By focusing your data analysis and testing on how variables impact CRO, you can make a large impact on the overall outcomes you generate. 

Ultimately, data enables you to create tailored and personalised user experiences. This will help to improve UX, increase loyalty and generate repeat purchases. For example, Amazon uses data science to offer a personalised and tailored UX by showing ‘your browsing history’, ‘inspired by your purchases’ and ‘top picks for you’. Netflix also uses data science, AI and ML to present thumbnail images that are most likely to resonate with the user based on other programmes and genres they tend to watch.8

Knowing that data holds the key to UX optimisation is one thing, but knowing how to utilise data to reach that outcome is something else entirely. It requires a data strategy that facilitates data:

While this can be done in-house, it’s time and labour-intensive, and the data skills gap makes internal hiring and training an expensive undertaking.

Get support for your data strategy with Jarmany

If you’re serious about improving your site’s UX and increasing visits, conversions and sales, you need to be utilising your organisation’s data. An in-house approach is one way to go, but getting outside help can ensure you benefit from the insights stored within your data without investing in in-house teams, processes and overheads.

Here at Jarmany, our mission is to ensure that organisations can understand the story behind their data and use that information to drive successful outcomes. Backed by our expert team of consultants, analysts, architects, technicians and data scientists, we can help you drive growth and make operational improvements using data.

With our managed service offering, you can access a whole range of benefits from data analytics that drive long-term business success, including: 

  • Saving time and internal resources
  • Support from a dedicated team of analysts
  • Increased resolution speed
  • Internal education on data solutions/dashboards

If you’re ready to start improving the user experience on your site by harnessing the potential of a data-driven approach, get in touch today and see how Jarmany can help..

Crumbs! What do I do when Cookies are phased out?

First, let’s head back to the basics…what are cookies and what do they do?

Cookies refer to the data that website’s collect on their users so they can get a better understanding of their website visitors and find out information such as their browsing history, what device they are using and how they’ve interacted with your site. Think of it as users leaving cookie crumbs across the internet so you can follow their trail.

Now, aside from stalking internet users, cookies aren’t all bad. They allow advertisers to serve up more personalised content that closely aligns to the interests and behaviour of users. Regardless of cookies, you’re going to get served up ads, so surely you’d want to them to at least be relevant to your interests… right? Well, not if it comes at the cost of your data privacy.

Before we get into the data privacy thing, it’s important to draw the differentiation between first party cookies and third party cookies:

First party cookies – this is where data is collected by the same website the user is visiting. This type of cookie tracks information such as language, payment method, details to pre-populate form fields, and items you may have previously viewed on their site.
Third party cookies – this is where data is collected by a separate domain to the one the user is visiting. It’s typically used for marketing and advertising purposes and tracks browser history and cross-site navigation.
It’s third party cookies which pose concerns over data privacy and security of personally identifiable information (PII), fuelled by heightened controls over privacy regulations such as GDPR.

So, what’s changing and how will it affect me?

Third party cookies are due to be phased out by the end of this year, with 90% of browsers expected to reject these cookies by default by 2023*. This is going to shake up the digital world and will impact the future of targeting and measurement of digital advertising campaigns. With mobile eCommerce accounting for over 70% of the total eCommerce market in 2021**, the rise of the app is also contributing towards the need for robust tracking to provide businesses with a holistic understanding of customer behaviour throughout their lifetime.

For advertisers, this means losing the ability to track user’s engagement and cross-site navigation. There will be notably less data on consumer interests and behaviour which will significantly impact how you use behavioural insights to target new customers. As a result, the digital advertising industry will need to look for new ways to identify and target potential audiences, without compromising ad spend by serving up irrelevant ads to the wrong audience.

For website users, don’t get too excited… you’re still going to get followed around the internet by adverts. They’ll just feel less personalised but therefore less intrusive too. Most importantly, this change will bring about more security and privacy whilst you browse the internet.

5 tips to bridge the data gap

With brands losing the ability to track online engagements, it’s important to pivot to new methods that can bridge the gap between informed insights and digital strategy. We’re in the data business, so you may think we’re biased but… data strategy is the new digital strategy.

Check out our 5 top tips to help you prepare for a post-cookie world:

1. Strengthen first party data

With the loss of third party data, the first most obvious thing to do is to leverage your first party data (which arguably is more important anyway). But before you can do that you need to make sure you’re effectively collecting, storing, and utilising your first party data. The more you know and understand about your existing customers, the easier you can model new audiences and increase sales through repeat purchase. Customer Data Platforms (CDP’s) are a clear solution here, with 67% of advertisers from a recent survey either currently or planning to use CDPs within the next 6 months*. This software allows users to aggregate, organise and structure vast amounts of customer data, which is pivotal for gaining in-depth insights into your existing audience and executing data-informed campaigns.

2. Improve your customer experience

Customer experience will be key to enriching your first party data. The larger subscriber base you have, the more you’ll be able to understand and learn from them in order to segment, personalise and ultimately create relevant experiences – which in turn will lead to brand loyalty, advocacy and repeat purchases. A positive customer experience means they are much more likely to trust your brand and therefore consent their personal information.

3. Collaborate with other data holders

Whilst first party data is crucial, it won’t take you the whole way there on your journey to finding new mechanics to target new audiences. Collaboration between media owners and advertisers is therefore a great way to use interest-based anonymised first party data to target new audiences. These types of interest-based cohorts are built by publishers grouping together their consented viewers based on viewing habits. Brands can then leverage this first party data by advertising through the publisher’s channels and targeting cohorts whose interests match their business offering.

4. Get more intelligent with your data intelligence

It’s not just about having data, you need to ensure you have the right data and you’re utilising it properly. This is where data science tools and techniques like Machine Learning, AI and predictive modelling are helpful to extrapolate learnings from your first party data. The end of third party cookies is also going to impact attribution modelling, so it’s important to be aware of new solutions that will help you identify last-touch and click-through attribution. Google are reviewing new solutions for this, such as their FLoC (Federated Learning on Cohorts) algorithm, to enable interest-based advertising on the internet by grouping together large anonymous audiences with common browsing habits.

5. Don’t underestimate the skills required

Whilst the solutions may seem straight-forward, implementing them can be a whole other story, especially if you don’t already have the suitable in-house resource and capabilities. There’s a major skills gap in the industry which is driving up the cost of in-house resourcing, so consider outsourcing your data requirements to help speed up the process and save you time and effort.

At Jarmany, we’re committed to helping you enrich and utilise your data to predict customers’ needs, increase efficiency and drive growth in a cookie-less world. We do that by applying the world’s most advanced technologies to your sales, marketing and operational data. Start the conversation today by getting in touch. 


*Embracing a Cookieless Future – webinar by Havas Media **11 Reasons Why You Need a Mobile eCommerce App | BuildFire 

Google Analytics is changing; here’s what you need to know

What is happening & when?

Universal Analytics and Universal Analytics 360 (the Enterprise version) will both be phased out and new data will no longer be processed through these platforms. 

Universal Analytics is sunsetting on 1st July 2023, and UA360 will be sunsetting 3 months later on 1st October 2023. Both platforms will enable users to have access to view historical data for a further 6 months. 

What are the key differences?

The primary driver for moving to GA4 is the better management of data privacy. 

GA4 will: 

  • Anonymise IP addresses by default, therefore preventing the misuse of personally identifiable information and protecting personal privacy; bringing the platform in-line with GDPR compliance.
  • Will be less reliant on cookies and instead focus on an event-based data model. This helps to make the platform more future-proof and, by moving away from the previous ‘browser-page framework’, will help to improve the insights available across multiple platforms.
  • Allow optimised integration with Google’s BigQuery; helping businesses utilise their data for broader insights and modelling purposes. 

As a result, GA4 will enable you and your business to benefit from: 

  • Broader multi-platform insights 
  • More valuable data and data-driven attribution 
  • More actionable data 

What do you need to do?

It’s important to be proactive and create your action plan so you’re geared up and ready for when this change takes place. Here are our top 4 tips for ensuring you are ‘GA4-Ready’: 

1. Transition existing report settings 

As you’ll no longer be able to process data through GA3 from mid-2023, you’ll need to ensure that you transition your existing tracking tags, in Google Tag Manager or other tagging systems, and report settings over to GA4. This won’t be done automatically by Google so will require some manual work. 

2. Use this time to audit current reporting 

Before you transition your existing reporting settings, you may want to view this time as a good opportunity to audit your existing reporting setup. Think about what your current reporting is showing you and whether it’s capturing all the business-critical, as well as ‘nice-to-have’, insights that you require. Don’t transition over a reporting framework that isn’t working for you, use this time to optimise your reporting workflow and improve efficiencies. If you’re unsure of how to optimise your reporting, seek consultation from an agency that can dedicate resources to guide you. 

3. Save historical data 

You’ll still be able to access previously processed historical data for around 6 months after the sunsetting of Universal Analytics and Universal Analytics 360. However, you may want to consider exporting any reports so you can refer to these at a later date. If you don’t already track historical data outside of GA, this may be a good time to kick-start that! 

4. Seek additional support for your reporting 

Data and analytics can be a complicated web of stats, charts, and figures if you’re not familiar with it. Instead of trying to weave through this alone seek support from specialists that are experts in Google Analytics, as well as other analytics platforms. By onboarding an agency to manage this for you, it will help you to smoothly transition your analytics and reporting set-up whilst saving you time, resources and hassle.


The transition to GA4 doesn’t have to be difficult.
Book a call with one of our experts to get started.

Start your conversation with  Jarmany today to see how we can help you transition to GA4 and improve your reporting and insights by emailing hello@jarmany.com or calling us on +44 (0)203 051 4960.

Data Visualisation Tools 2021

Data visualisation is a vital element of all data management activities. When used right, the best data viz software can tell the story of the data and provide a single source of truth to a business. It can enable informed, rapid data-driven decision-making and allow businesses to become more agile, responsive to emerging trends and be more in tune with their customers.


Data visualisation tools are software tools (often with a browser interface) that can import data, merge and mix data streams, and present the required views of data at the right level of detail for the intended user.

Businesses and organisations typically have many streams of data. For these streams to be usable they need to be presented in a form that’s understandable by those who need it to make decisions.

Data viz tools transform “raw” data into graphs, charts, tables, lists and maps. They will also allow for reports to be created which can include narrative commentary supplied by data analysts.

Key requirements are ease-of-use, speed of creating new reports, ability to handle big data sets easily, and a user interface that’s attractive and good to look at. Total cost of ownership is obviously going to be a key factor in the decision to adopt a particular tool. Different licencing models are in play, including simple subscriptions, per-seat models and feature-based models.

Reports should be easy to download and print, but data visualisation tools must also take advantage of the interactivity available on the web to provide users with the ability to easily navigate the reports, drill down from top-level data into detail and get the best user experience on the widest range of desktops and even mobiles.

Data viz tools are in fact much more than the user interface and presentation part. They must support the data connectors necessary to connect to a wide range of data sources, and preferably allow third-party providers to develop their own data connectors, if they don’t already exist.


There are many types of users. The most obvious is management teams who need data that’s continuously up to date, in order to make timely decisions.

There are literally hundreds of data viz tools on the market today and the landscape is continually changing. Let’s look at four popular data viz tools that are dominating the marketplace in 2021: Power BI from Microsoft, Tableau, Looker and Qlik.


A Capterra Top Performer in 2021, Microsoft Power BI (Business Information) is a strong contender for best-of-breed data viz tool. Microsoft pitches its product as a way of consolidating multiple streams of data and moving away from “raw” data views such as Excel, SQL Server, Access, text-based formats such as XML, jSON etc. Like most data viz tools, Power BI can be configured to import data from unstructured sources such as emails, PDFs etc as well as tabular or relational data.


Data can be presented on desktops, on the web (secured for access only by authorised users if necessary) and on mobiles.

A key feature, which is emerging in many data viz tools is the ability for users to enter a natural language query into a search box to ask questions in their native language to interrogate the underlying data. For example:

“Which sales team had the highest revenue last quarter?”

“Show me the top 10 products by sales in 2020”

“Show me sales by category as pie chart”.

As you can see these types of queries will return quite different results, intelligently and in real time. This feature is called Power BI Q&A and also supports autocomplete to guess the data sets/categories you are interested in.

Power BI also supports Data Analysis Expressions (DAX). Similar in concept to Excel’s formulas, DAX allows users to query data using expressions, maths and string expressions.

Visualisation types include the full range of traditional charts, including pie charts, column charts, stacked column charts, tables, lists, area charts, scatter charts, geo maps, line charts of all types.

Power BI Consultancy


Tableau is another data visualisation tool that can create beautiful dashboards and visualisations. It supports numerous data sources including both structured and unstructured data, drill-down, the capability to be programmed using popular languages such as Python and R and has some unique features too.

Like many of the other tools, Tableau has a range of ways to connect your data including Amazon Redshift, Google Analytics, Excel, Azure databases, Databricks, MySQL and many other data sources. Databases can be cross joined so they don’t necessarily have to be merged together in a pre-visualisation operation, which can slow down the workflow.

Tableau dashboards can be viewed on mobiles and tablets as well as desktops. It supports a very wide range of mathematical and statistical functions. And of course, it supports all the expected chart types including pie chart, bar charts as well as histograms, Gantt chart, treemaps, geomaps and more.

Tableau consultancy

Power BI logo

“A great data viz tool will help management and teams understand the backstory behind their data


Business Intelligence platform Qlik is another choice for understanding and visualising your data.

The Qlik angle is to close the gap between data (what you know) and action (what you should do about it) by providing ways to visualise data streams that give informative, actionable insights.

Qlik doesn’t force users to move data to the cloud, which can be difficult to do with legacy systems in play. But it also plays nicely with cloud data platforms including Amazon, Azure, Google Big Query, Snowflake, Databricks etc.

Qlik’s product suite offers more than just visualisation – it also includes data replication tools, automation, data management and more.

A unique feature of Qlik is the “associative engine”, which creates an internal catalogue of the relationships between your data points to find patterns and insights that you may not even have thought about. Using a visual interface, users (who could be non-technical) can explore data sets visually. Whereas, traditional data querying is done via query languages such as SQL (Structured Query Language) which can only filter and show the data that you specify in advance.

Qlik also has a mobile app, making it easy to use by team members who work in the field.  

Qlik consultancy

Qlik logo


Looker bills itself as “Data at your moment of need” and as you might expect from a Google product (Looker was purchased by Google some months ago) there is a focus on speed in this visualisation tool.

Looker has no desktop app (it’s all browser-based). It allows you to create amazing visualisation of data, features strong security and sharing capabilities, allows you to schedule reports to be created and disseminated amongst team members, set up alerts when data points meet specified thresholds, and much more.

Looker might not be the right choice if your data stack is primarily Microsoft (in which case you should probably consider Power BI) but if you are more platform-agnostic Looker could be a good choice.

Like other tools, Google features powerful AI which can interpret English-language queries, avoiding the need for the data-consumers within your business to know database query languages such as SQL.

Looker has out-of-the-box integrations with many SaaS packages commonly used by businesses today, including Salesforce, Confluence, SharePoint etc.

Looker consultancy

Looker logo


To make sure your data is telling you the whole story about what’s going on in your organisation, look to Jarmany.

How to Use Big Data Effectively


Big data simply refers to large amounts of data from a disparate variety of sources.

“Big data is big, fast-flowing, and often messy.”

It could include information such as customer transactions, consumer behaviour, health records, internet searches, inventories, road mapping, weather conditions, financial data – or thousands of other types of data .

Characteristic of “big data” is that it tends to be large in volume (often terabytes and petabytes in size), it tends to flow into the business at a high speed, and it is, in most cases, unstructured.

Unstructured data tends not to fit into the traditional relational (table/column) based structure in which data has commonly been stored in the past. Instead, it can include all kinds of documents, emails, images, video, and many other unrelated data formats, all of which can vary in size and format from record to record.

In recent years, software tools to manage and make sense of these disparate data sources have emerged.

By combining strategic goals with sophisticated software tools, companies can mine this data for useable insights that can transform their business. For example, big data analytics can reveal customer preferences, market trends, identify operational efficiencies, predict required production volumes, and so much more.

Companies are seizing the opportunity to use big data. Big data analytics is a growing sector – estimated to be worth $14.85bn annually and predicted to be worth $68bn in 2025[i]

By understanding the many streams of data that affect your organisation, you can quickly and effectively adapt your business to consumer needs.


In our highly connected world, consumers are inundated with ads and information about companies and organisations every day. But we can only pay attention to so much before it all becomes just white noise. So, how do organisations ensure that their brand gets customers’ attention?

According to Forbes, one answer is big data. They argue that:

‘Big data analytics offers a competitive advantage to the brands that are able to work faster and target their consumers more effectively.’

Here are some examples of how you can use big data to benefit your marketing campaigns:

  • Creating focused and targeted advertising campaigns
  • Identifying upsell and cross-sell opportunities
  • Improving customer acquisition and retention
  • Effective upselling
  • Cost reduction
  • Identifying new revenue streams
  • Risk management


Banking and the financial sector have long been at the forefront of developing new ways of exploiting the vast amounts of data they produce. Whilst they are developing new systems, they must retain older, legacy systems, which are too expensive to replace. The data produced by legacy systems must be integrated with newer systems seamlessly.

Banks are able to collect far more data on customers than ever before through use of apps, open banking systems and websites.

Financial services companies can use this data to analyse new fraud and security threats, manage risk, reduce operational costs, provide new services through open banking, work more closely with insurance companies, manage customer relationships more efficiently, identify cross-selling and upselling opportunities, accurately identify high-value customers, manage credit risk and (for businesses) liquidity and viability problems.


At Jarmany, our experts can dive deep into your business and provide solutions that give you the insights you need to transform your business. We can help your spot opportunities and avoid pitfalls to give you a competitive edge.

Small businesses may not generate as much data as Google or Amazon but will have data that can be used to benefit the business. In many ways, small businesses are ideally suited to using big data because they are often more agile and can respond quickly to data driven insights.

As well as internal systems such as sales logs, transaction histories, customer interactions via phone and email etc., data can also be fed into small businesses from social media, online reviews, online trend data such as Google Trends and many other online data sources.

Operational data may come from Internet-connected devices using sensors in the warehouse or factory. For example, pubs and bars are fitting sensors inside beer pumps, providing a data stream that can help reduce wasted beer, improve drinks quality, monitor stock levels and much more.

Data analysis can provide insights into how customers prefer to buy, why they adopt products and services (and why they abandon them).


Big data isn’t only useful for businesses. Charities can use big data analytics to adapt their services to better meet the needs of their service users as well as reduce costs and improve recruitment and funding.

According to Charity Digital, in their article Big Data Trends for 2021:

‘Artificial intelligence, virtual reality, and other innovations will make big data more valuable than ever to charities in the coming months.’

Big data can help charities establish target donors by analysing the demographic data such as age and gender of service users as well as any other relevant user information.

At Jarmany, we understand the specific needs of charities and have the expertise and tools to provide data analytics that can transform non-profits.

Using big data analytics, non-profits can identify gaps in their provision and prevent vulnerable people from slipping through the net. In addition, charities can use donor data to target campaigns and improve donor loyalty.


Big data can give any business a competitive edge, but there is so much more to running a business in the 21st Century than just turnover and profit. The environmental impact of companies is becoming ever more important to customers as we experience the effects of climate change. Big data can help businesses improve their environmental credentials.

According to Nature Communications ‘The private sector is increasingly making influential environmental decisions and some large companies are committing to sustainability in their supply chains. Examples include ‘zero-deforestation’ and sustainably sourced palm oil pledges from Nestlé and McDonalds.’ These companies know that their environmental policies are important to their customers.

Businesses can use green data to improve their green credentials as well as to reduce energy use and carbon footprint. Analysing data about energy use, downtime and transportation costs can save money and help the planet.


Every business is different. However, all businesses will have a range of data available to them that they can use to improve their business. It’s just a matter of collecting and analysing that data so that it reveals the insights that you need.

At Jarmany, we can work with you to define what data you need and how to use it to benefit your business. We can provide you with the expertise and the tools to make sure you know your business inside and out. This will ensure you can adapt quickly in an ever-changing world.

We start with an audit to get a view of where you are at with your data. We also look at your board level KPIs, your market challenges and build a data strategy that will deliver insights for your business on every level.

If you would like more information on how you can transform your company to a data driven business capable of responding to changes in real time, contact us today.

[i] https://www.statista.com/statistics/947745/worldwide-total-data-market-revenue/

Image credit: Starline | freepik.com