4 Analytic Methods and When to Use Them

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How to analyse your customer data for better marketing

Getting your data processed, stored, and managed correctly is just the first part of your data strategy. Once your data has been cleansed, stored, and integrated, what’s next?

The answer is – analyse it. What is your data telling you? And, more importantly, what are you going to do about it? Your analytics should always give you an action to follow. Whether it is to inform your business, sales, or marketing strategies.

Analytics can help you get to know and engage your customers. This is especially important as we aim to reconnect with them and resume normality as the pandemic continues to ease. It can also help improve customer experience, increase conversions, decrease churn – and ultimately – improve your return on investment.

Here, we explore four different analytic methods that can deliver powerful insights and help you unlock the value your data holds.

1. Descriptive Analytics

Descriptive analytics is all about what has happened or is happening. Descriptors can usually be split into four categories – demographic, geographic, attitudinal, and behavioural. This analytic method helps you understand more about your customers. It includes anything that can help give you more factual information about your individuals.

Descriptive analytics is useful for two main reasons. Firstly, it allows you to segment your database into groups with similar characteristics so you can be more targeted with your communications. We worked with the RNLI to help them do exactly this. One technique which worked really well for the RNLI was using different images for different age groups. The RNLI found that younger people were more likely to engage if an image of a similarly aged individual was used.

Secondly, it allows you to target new look-a-like audiences elsewhere. For example, if you realise your biggest spenders are millennials in London who like dogs, you might launch a campaign to specifically target those people. Combine that with content crafted specifically for that group, and you have the recipe for a successful campaign.

2. Diagnostic Analytics

Diagnostic analytics helps you uncover why something is happening. Typically, you start by looking at what’s working well and what’s not. Then, as you begin to spot trends, you can then determine a root cause for them.

Diagnostic analytics can be achieved through data modelling. The aim is to recognise relationships and patterns within the data to facilitate a deep understanding of the database.

For example, if you notice that customers who were sent a Christmas present tended to make a purchase from you in the new year, this might suggest your Christmas present had an impact on their spending behaviour. So, next Christmas, you might consider sending out more.

3. Predictive Analytics

Closely linked with diagnostic analytics, predictive analytics is about understanding what is likely to happen. Once you can see why something has happened, you’re more able to predict what will happen next and understand what the likely future outcomes will be.

Predicting churn is a perfect example of this. Your data might show you that prior to losing a customer, they tend to contact customer services a number of times. This might suggest they are having issues with your services. So from this information, you can then predict who is likely to unsubscribe in the near future.

You don’t even always need to understand why immediately. It might be that you can spot the signals typically shown by someone who is about to churn, and from this, you can predict which customers you are likely to lose next and put measures in place to avoid this. You might not initially know why this is happening but knowing what is about to happen is still useful.

4. Prescriptive Analytics

Prescriptive analytics takes predictive modelling to the next level. Rather than simply looking at what the customer is likely to do next, it looks at how likely they are to be influenced by other factors.

While predictive models are usually based on transactional data and look at actions, a prescriptive model looks at other external factors that could affect the eventual outcome.

A GPS system is a good example of this. You input your desired destination, it calculates the quickest way of reaching it, and as you travel, it takes in more data (roadworks or traffic jams for example) and recalculates your route and time of arrival.

In a business context, prescriptive analytics can be used to not just calculate what revenue you’re expecting from a customer, for example, but how this will be affected if you haven’t got the products available to serve those customers. Or, to determine which products you need to sell instead to deliver that same revenue.

It’s effectively a method of prescribing routes, combining predictive modelling, the influence of external factors, and machine learning, to determine an overall objective.

So, now I’ve got all this knowledge, how do I use it?

Once you’re able to break down and interpret your data with these different analytic approaches, you can begin to apply strategies and initiatives to deliver maximum value. In fact, 63% of companies say that improved efficiency is the top benefit of data analytics, while 57% cite more effective decision-making.

Here are just some of the ways your newfound insight can be put to use:

1. Data visualisation and reporting

Now you’ve discovered these insights in your data, it needs to be presented in a way that helps you to understand the trends and patterns. You also need to be able to see whether the action you’re taking is having the desired effect. For example, if you’ve built a predictive model that looks at anti-lapse, is it working effectively? Can you clearly see who is about to lapse or churn? Can you tell what you need to do to prevent that? Data visualisation and reporting should give you the complete picture and help you to understand how you’re performing, and what action you should take next.

2. Personalisation

Personalisation can be applied at any point as a result of the insights unearthed by analytics. Customers expect highly personalised communications in return for giving up their information. By personalising communications and making tailored suggestions, you’re much more likely to keep their attention and stop them from looking elsewhere. Personalisation is also an effective way to bridge the gap between overwhelming amounts of choice and the individual’s personal wants and needs. Leading them to products and services they’re most likely to want can save them time and make their life easier. It also allows them to feel noticed and understood by you, making them feel more valued and loyal.

3. Omnichannel automation

Now you’ve formed an understanding of where and how your audience is interacting with you, you can deliver the right communications in the right place at the right time. These messages should be interwoven through the customer journey, regardless of which channel they use at which time.

When it comes to automating customer channels, many organisations don’t realise that this is not limited to just digital. Using the right marketing automation software means you can apply it to any and all channels. Whether that is direct mail, face-to-face interactions, applications, search, advertising, SMS and so on.

This means that whatever channel your customer jumps from, you can continue the conversation. This helps them to feel valued by your organisation and in turn, transforms them into a loyal customer.

Whether you are at the insights stage of your data journey, or just starting out, our expert team can help guide you every step of the way. We can recommend the most effective blend of techniques to help you achieve all of this and more. Get in touch today to speak to one of our advisors.

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