Getting started with marketing analytics shouldn’t be daunting!

Plan your way forward and be confident

Unless you have been living in the middle of the Amazon jungle, you will have heard the phrase ‘marketing analytics’ bandied around.

While we avoid buzz words like the plague at Ipsos Jarmany, we certainly subscribe to the fact that hidden in your ever-growing amounts of sales and marketing data are answers to some important questions. Answers that you really need to know.

But just how do you get from data held in various systems to a place where, at the click of a mouse, you can get the real-time insight into the numbers that matter?

In our experience, the apparent size of this task is often why the implementation of marketing analytics fails to take off. When laid out end-to-end, the complexity, time and cost involved in harnessing the commercial power of your sales and marketing data is daunting. In fact, it can often seem insurmountable to the people you need to get on board.

This is where a proof of concept comes in. A proof of concept, or more aptly, a proof of value is a discrete test that will tell you if it is worth harnessing your data. As well as being sensible, it’s a much easier way to get buy-in and budget.

Three things to think about when considering a proof of concept.

1. Aim to deliver a few quick wins the business will value

Could you analyse which are the best performing stores within a sales territory? Or which customers could be migrated to an online account within a certain segment? Answers to these questions will provide actionable insight and commercial wins with clear return on investment. The same answers should also help you get a green light for more budget.

2. Make it achievable in a relatively short time frame

This is self-explanatory, but an often-overlooked point. Set a realistic deadline with a realistic implementation plan. You might need experts on board (erm…like us) who have done this before and understands the roadblocks that occur when implementing a proof of concept, such as access to data. Something small, like understanding what data is available and how to get it, can speed a project up enormously.

3. Provide a road map to implementation

Remember, this process is going to open your eyes and those of your colleagues in the sales and marketing functions. If the proof of concept is successful – and the whole point of designing one is to show market analytics delivers measurable benefits – then what you learn should be communicated and fed into the implementation road map.

Your next move

Follow our three steps above and it will pave the way to a more complete implementation. It will also show the huge benefits that implementing marketing analytics can bring to an organisation. What’s your next step going to be?

When software alone is rarely the answer

Buying software seems the obvious solution

So you want to be a data driven organisation. You’ve read the hype all over the internet and in print. A few others in your organisation even share your enthusiasm.  Finally, you are ready to make your first move.

Next you pick up the phone, arrange meetings with software vendors or tech integrators, and it feels like the end is in sight…right?

It makes perfect sense, but wrong

Of course, you absolutely do need software and systems. Technology is after all the enabler. Without the software, storage and processing power, analysing your ‘big’ data simply isn’t possible. But bear in mind, technology is just a tool. A means to an end. You don’t put some keys and screwdriver on the floor and expect it to build your IKEA flat-pack wardrobes for you. Data is the same.

Our advice is don’t start meeting vendors or resellers without a clear understanding of what you hope the solution will eventually achieve. Get the right people involved and be clear about the benefits and payoff. Run a small scale pilot if you can.

Preparation and scoping is vital. The other equally important part of the puzzle is human beings. If you have no data experts or data scientists in the building, then who are you expecting to be able to use the software? You? A colleague? If so, will either of you have time to be trained and take on the extra workload?

Software companies, resellers and integrators all love to show you how intuitive their front-end systems are. They will tell you how anyone could run a string of variables that pinpoint the 16 factors that lead to a customer defecting to a competitor. But then they would wouldn’t they — seeing as they make money out of selling software?

There is a better way…

Unless you’ve got time, a bit of tech knowledge and intent, you’re better off having data as a service. This is why we are completely software agnostic at Ipsos Jarmany. We can use it all on your behalf, and don’t sell products or platforms. Doesn’t that sound like a better proposition?

Four ways to be more agile with data

It’s do or die

Who would have known twenty years ago that most of us would browse the internet every day on our mobile phones? Or in 2005 that something called Twitter would be the main channel for customer feedback? Or even five years ago that companies would live or die by their ability to analyse the purchasing habits of millions of customers?

This is why modern businesses need to be agile – to be able to respond rapidly and adapt to the blisteringly fast and constant change. Companies that weren’t, like Kodak and Blockbuster… well you know what happened to them.

What you need to be

Agility is now a vital part of any business and its culture. But what does that mean for you and how you work with data? We’ve collected some thoughts on this thorny issue, looking at how organisations need to be faster and more reactive.

1. Analyse swiftly

You might have a lot of data to go through, but you no longer have months or years to collect and sift through it all. More and more companies are looking to analyse their data in real time and extract actionable insights (through predictive analytics), as soon as they can. Using tools that can make sense of data quickly, often through visualisation, has become increasingly important.

2. Be agile across the business

No part of the organisation can afford to slow down the rest when it comes to data. Sales and marketing need to move with customers and deliver the right messages and products in the right place, at the right time. That requires delivering data-based insight that’s hot off the press. Yesterday’s news isn’t any good

3. Break down the silos

A typical business infrastructure might include multiple data warehouses, cloud systems, business units and so on. These can be inaccessible, leading to ‘dark data’ – information that you might not even know about, and that certainly cannot be analysed.

Investing time and resources can help to connect and access these disparate systems and datasets. It’s the only way to ensure that the right data is available for analysis when needed. But at the same time, remember that a lot of the data your business will use comes from external sources as well.

4. Adapt your data based on real-time performance

Data is continually flowing. It can change course at a moment’s notice based on how consumers behave, particularly online. That means you need to be able to carry out continuous updates and revisions to incorporate the most recent actions and behaviours of each customer. And as you learn more about those customers, they need to be constantly re-evaluated.

What prescriptive analytics can do

Your route to better decision making

‘Data analytics’ is a term that covers a wide range of activities. Businesses can use their data to look at what has happened, why it happened and, ideally, make predictions of what might happen in the future.

Prescriptive analytics takes analysis of data a step further. This type of analysis not only anticipates what will happen and when it will happen. It also tells you why it will happen.

What’s the benefit?

In a nutshell, prescriptive analytics helps businesses translate their forecasts, predictions and business information into feasible, actionable plans.

This is because it suggests options for taking advantage of a future opportunity (or mitigate a future risk) and shows the implications of each decision. Prescriptive analytics can also continually take in new data to re-predict and re-prescribe forecasts, automatically improving accuracy.

In essence, prescriptive analytics helps you make the right decision for the future. For example, a retailer may want to choose the ideal sales promotion that will appeal to the right customers at the right time. It can use prescriptive analytics to evaluate its options and get a clear understanding of how customers are likely to react to different offers and promotions.

Businesses can also use ‘what if’ simulations and models to optimise their decisions, evaluating a range of actions and their likely outcomes. As with all data science, it’s about using the information at hand to make better business decisions.

What could you achieve?

At Ipsos Jarmany we regularly help our clients use prescriptive analytics to make better decisions that drive sales. Get in touch to find out more.