Alongside this, Product Performance Analysis also gives you the intel to understand and optimise the customer journey, from when someone identifies a need to when they convert by purchasing a product or service.
In this blog we delve in to what product performance is, the benefits, and the top analytics techniques you should be leveraging in order to gain a true and holistic understanding.
What is Product Performance Analysis?
For anyone who hasn’t read our guide to Product Performance Analysis, here is a quick rundown of what it’s about. Essentially, you’re using data to analyse your products’ performance at any time. This allows product managers to gain insights, such as:
- How well is the product performing in terms of sales and revenue?
- What is the market share of the product?
- Which customer segments does your product appeal to most?
- Which customers are abandoning their customer journeys and at which stages?
- What do customers think about the product?
- Which products tend to be purchased at the same time, and are therefore good cross-sell or up-sell opportunities?
- Which product details or features are being used and which aren’t?
- What information are customers seeking out about the product?
- How does the product compare to competitor’s products, in terms of sales performance?
- What market trends are impacting, or could impact, the sales performance of my product?
- How effective are the marketing efforts for the product, and with which customer segments?
The Benefits of Product Performance Analysis
Product Performance Analysis is key for ongoing measurement of your products’ performance, however it can also be a key asset for assessing product launches and campaign performance, such as Black Friday or Christmas campaigns. For example, at Ipsos Jarmany we do a lot of work with some of our clients in the consumer tech industry to help them understand how new product launches have performed, and what optimisations can be made to boost sales performance.
Product Performance Analysis also enables data-driven decision-making, which can improve campaign effectiveness, increasing response rates by up to 600%. In addition, it can also help you to expand the number of engaged customers, known to generate 23% more revenue than average customers and be a tool for tracking indirect sales to provide a holistic view of product performance.
The Product Performance Analysis Techniques You Need to Know
Product Performance Analysis isn’t singular, you need to take a multi-faceted approach which, using a blend of techniques, can give you a holistic overview of your products’ performance. There are multiple techniques you can use to gain the product performance insights you need to achieve an edge in today’s business world, and guide your decision-making for both offline and online sales strategies.
In this blog we differentiate between direct and indirect performance insights, providing you with 9 key techniques for best-in-class direct performance analysis, and 3 key techniques for acquiring valuable indirect insights.
Direct Performance Insights
#1 Funnel Analysis
Firstly, we have Funnel Analysis. This involves mapping and analysing the steps to achieve a desired outcome on a website and assessing how many users get through each step. The idea is that you’ll be able to identify drop-off points along the way to the desired outcome and, therefore, understand where improvements need to be made to increase conversions.
When it comes to funnel analysis, it’s important to define your goal and map out the customer journey to achieving that goal – whether it’s to gain newsletter sign-ups, purchase a product, create a user account, register for an event or download your app. The AIDA model, which includes Awareness, Interest, Desire and Action stages, is often used to map sales journeys. You can then track customers’ progress, seeing the funnel stages where they drop off, alerting you to friction points that need attention.
Funnel analysis should be a key component in your product performance analysis as it allows you to identify bottlenecks, optimise the user journey, understand user behaviour and improve your conversion rates. Facilitating a seamless conversion process is crucial to fostering repeat business among customers. In fact, 88% of online shoppers express that they are unlikely to revisit a website following a negative user experience.
#2 Trend Analysis
Trend analysis is another important technique to help you to better understand your customers and gain insights into their motivations, expectations, and the external influences, like economic, social and technological trends, that impact their behaviour. Nike, for example, uses Trend Analysis to ensure its products keep meeting the needs of customers. Based on insights that showed growing consumer concerns about sustainability, the brand launched Nike Space Hippie, a line of sneakers engineered from recycled materials.
To establish trends you need to use current and historical data from various sources, including surveys, reviews, feedback, market research and web analytics. This will allow you to discover the customer segments where your products resonate most and at what times of the year. The findings then allow you to optimise product sales analytics, engagement strategies and marketing campaigns.
Additionally, Trend Analysis can help you to:
- Identify new product opportunities
- Understand customer behaviour
- Pre-empt customer needs
- Personalise marketing campaigns and comms based on customer insights
- Track your business progress and results.
#3 Customer Journey Analysis
A critical component in understanding how your products are performing, is by looking in to how your customers are finding you, and what part of the customer journey is having the biggest impact on conversions. This is where Customer Journey Analysis and Customer Journey Mapping comes in. These two techniques are often confused but are two distinct processes, with mapping a subset of analysis. Think about them in this way: Journey Mapping tells you what happened, and Journey Analysis tells you why it happened.
By analysing your customers’ journey map, you can identify the various touchpoints and interactions a customer has with your business throughout their entire lifecycle. It also helps you to understand areas of friction along the journey, spot unnecessary touchpoints, establish which touchpoints have a great attribution towards conversions, and call out points where customer expectations were delivered or exceeded.
Customer journey analysis is hugely interconnected with Product Performance Analysis as it allows you to gain comprehensive insights into how customers interact with your products and identify pivotal moments in that journey that results in a conversion.
#4 Cohort Analysis
Next up we have Cohort Analysis. This allows you to identify the behavioural characteristics of groups within your customer base to detect patterns and insights that you can use to optimise customer retention.
Three types of cohorts are commonly identified as:
- Acquisition cohorts – based on when someone became a customer.
- Behavioural cohorts – reflecting past behaviours or profile properties.
- Predictive cohorts – based on what a customer is expected to do in the future.
To complete a Cohort Analysis, you need to first set a clear goal of what you want to accomplish, such as reducing churn rates. Next, select a question like, are our products still meeting the needs of customers? Decide what you need to measure to answer the question and choose the attributes for the cohorts in your analysis. Once you have your data, you need to test your findings, which you can do through a simple A/B test.
#5 Retention Analysis
Retention Analysis quantifies usage data to determine customer churn or retention potential. It highlights why customers decide to stay, giving you data to drive product development, services and customer support strategies, and increase customer life-time value (LTV).
Customer retention is critical for businesses, with retaining customers costing five times less than acquiring new ones. Therefore, you want to use Retention Analysis to focus on your power users and look at their behaviours to analyse their engagement and discover areas of the customer journey for improvement. Look at what features power users engage with and ask yourself why. While the average retention rate across industries maybe 75%, it’s not uncommon for software-as-a-service companies with Retention Analysis strategies to reach 90%-plus.
#6 Predictive Analytics
Most Product Performance Analysis techniques are centred around analysing past or present performance to glean actionable insights. However all businesses want (and need) insights into the future to make the most profitable decisions. Predictive Analytics, which uses historical data, statistics and machine learning to anticipate the future, can be fed into your decision-making process to optimise your strategies. There are no limits: it can support all your business functions, including marketing, sales, operations and finance.
It can help marketers understand customer behaviour better and predict the impact of marketing messages more accurately. You can spot industry trends earlier than competitors, find hidden relationships between customer data points, spot the most promising marketing prospects, and highlight at-risk customers that need more attention. Amazon is a convert of Predictive Analytics, using the technology to establish what products users are likely to purchase in the future, based on previous purchases and the behaviour of similar consumers. With this information, they are then able to provide personalised recommendations to improve cross-sell and up-sell product conversions.
#7 Customer Feedback Analysis
Whilst many of these techniques have been focused on quantitative analytics, you also need to evaluate qualitative insights based on customer feedback. This type of insights can highlight your customers’ needs and areas where you can improve. Customer Feedback Analysis enables you to process that feedback in bulk, extracting insights a human might overlook. It is a crucial driver of revenue growth since 86% of buyers have said they are willing to pay higher prices for a great customer experience.
There are lots of data sources to give you feedback. They can be surveys, reviews or customer support conversations. Some of the most commonly used sources are:
- Customer satisfaction (CSAT) surveys
- Net promoter surveys (NPS)
- Customer effort scores (CES).
Things to note are NPS is a relationship study metric, while CSAT and CES are primarily transactional metrics. Companies often use NPS to understand customer loyalty, while they may turn to CSAT feedback if they want to change their product portfolio.
#8 CRM Analysis & Optimisation
Customer relationship management (CRM) systems have been around for decades.
They store vast amounts of data, including customer and prospect data, customer interactions and service issues, and play a pivotal role in understanding and improving product performance. For example, CRM analysis can help you to identify customer segments, create personalised and targeted marketing campaigns, present cross sell and up-sell opportunities, and collect customer feedback.
Analysing your CRM data in detail can also provide you with insights such as:
- Lead drop-out rates across the sales cycle
- Number, length and success of sales calls
- Marketing email open rates and times
- Social media post interactions
- Customer service pain points
Solutions like Microsoft Dynamics 365, used by 97% of Fortune 500 companies, come with analytics capabilities, including visualisations, dashboards and goal management. It includes a familiar Microsoft interface, minimising training, and delivers cutting-edge analytics, including sales forecasting.
Using heat maps lets you see what users do when they visit your web page. You can identify the places where they click, how far they scroll and what areas they seem to avoid. You can also detect the messages that resonate more with customers and locate messaging in the high-exposure positions to drive users down your funnel.
Software solutions for heat mapping are widely available, for example Microsoft offers Microsoft Clarity, a free tool to capture how people use your website. There are multiple paid-for options, with tools for different use cases, from understanding user behaviour to optimising landing pages. Heat maps are a powerful solution; they have been known to increase website click-through rates by as much as 276%.
Indirect Performance Insights
#1 Indirect Channel Data
Up to this point, we’ve explored various techniques for obtaining performance insights through direct data sources. However, it is crucial to complement this approach by incorporating data from external sources to achieve a comprehensive understanding of your products’ performance. A pivotal starting point for this integration is through indirect channel data, which refers to information directly provided by third-party retailers. This is typically sales data, with insights around number of units sold, sales by regional location, and stock levels.
By leveraging these insights, you can effectively compare the performance of your direct-to-consumer sales with your indirect-to-consumer sales. This comparative analysis enables you to identify key opportunities, both offline and online, and strategically focus your marketing efforts where they are likely to yield the greatest impact.
#2 Third-Party Data
Whilst indirect channel data will provide valuable insights, third-party retailers often provide limited information. To help bridge this gap and unlock broader insights, you should lean on third-party data providers to gain insights around competitors’ performance, industry insights and market conditions.
Well-known providers, like GfK, provide sales and market intelligence to help you connect the dots and improve decision-making. This could include insights around media consumption by your target audience, or industry sales by channel and price.
#3 Web Scraping
In addition to gathering indirect data from retailers and other third-parties, you can also collect information for Product Performance Analysis through web scraping technologies. This will provide you with insights on your own performance, as well as competitor’s performance.
Web Scraping automates the extraction of valuable information from across the internet, in turn providing you with industry insights, competitive analysis, and information on what customers say about your product. You can obtain information on your share of voice and benchmark your operations against competitors.
Web Scraping requires the development of scripts, using tools like Python, to launch virtual machines that gather data, which is then structured, cleansed and processed for analysis. It is essential if you’re selling indirectly through third-party affiliate sites, as it allows you to gain deeper insights into your products’ performance on those sites and insights into your competitors’ performance.
How To Maximise Product Performance Analysis
Many of these techniques for Product Performance Analysis may be familiar. You may have started using a few and are now considering how best to implement some others to get to the next level. It can feel like a complex process, requiring skills, expertise and time that may present barriers.
At Ipsos Jarmany, we have been helping businesses improve their analytics capabilities to thrive in a world where organisations are increasingly data-driven. Our analytics specialists are helping them turn raw data into actionable insights so they can make the most effective decisions in terms of their products and product development.
By working with us, we can help you gain maximum ROI from Product Performance Analysis with none of the hassle. If that sounds interesting, please get in touch with us today.