With every e-commerce brand gathering ‘Big’ and ‘Little’ data, the concept of calculating CLV is more tangible than ever. So will it prove to be revolutionary for digital marketers, or is it still unobtainable to everyone but the biggest brands?

This article looks at some of the ways organisations are calculating CLV and explores the options for those looking to use it to inform marketing decisions and create personalised experiences for customers.

Each person’s digital footprint offers brand owners a goldmine of information they can use to target and personalise their marketing and evaluate its success. But we need a strategy – we need to know where to dig and we need to be able to recognise the gold when we find it. Customer lifetime value (CLV) promises to show us the path, by starting at the very end of the line – what is this individual customer worth to us? Not now, not last year, and not even next week. If we can accurately predict CLV, we can compare that to the total cost of acquiring, retaining and rewarding that customer – and predict how profitable they will end up being for our company.

Why is this an important metric to know? Well, if you can accurately predict CLV you can:

  • Understand the optimum amount you should spend on the acquisition of new customers in each channel, as well as which campaigns deliver the most valuable customers
  • Ensure retention activity is focused on the right customers – the ones you want to keep most
  • Develop your existing base with products and services that best meet their needs
  • Offer differential service based on value

So how do we calculate it? Well, the complexity of the calculation varies widely between different organisations. Generally, telecommunications companies are the most advanced users – they’ve been calculating CLV using advanced Data Mining techniques for many years. Others are analysing three main parameters:

  • Constant margin (the contribution that is left after deducting variable costs, such as retention spending)
  • Constant retention probability per period
  • Discount rate

However, all users will have different retention rates based on their demographic and behaviours. For this reason, the use of Predictive Analytics and statistical modelling have become common place in the pursuit of more accurate CLV predictions. The role of the Data Scientist has now become paramount and large organisations are investing heavily in new technology and building teams of these data geeks to store and process the data required.

Thankfully, cloud software is bringing this calculation out of the hands of the data geeks, and into the hands of the marketers – where it’s needed most.

In most cloud software that is used to calculate CLV, advanced analytics are used to arrive at highly accurate calculations. The software usually consists of a standard way to load collected data, then automatically aggregate, analyse and enrich the uploaded information. No need for the team of data scientists and an easy way to turn all that big data into strategically relevant information.

The theory driving the use of CLV is that for long-term business success, companies should focus less on quarterly profits and more on maintaining healthy, long-term relationships with their customers. CLV encourages this shift by placing a concrete value on business-customer relationships, allowing companies to see how their profits are affected by their rapport with their clientele. For this reason, CLV is currently used mainly by relationship-focused companies; however, the concept could easily be expanded to allow other types of businesses to acquire more valuable consumers, retain their most profitable clients, and grow existing customer value.

So whichever route you use to calculate CLV, your company can ensure that its marketing budget is allocated to targeting customers who will spend more in the future, be more loyal and bring in greater, more lasting profits. What a great use of all that big data!

 

By Glen Westlake, CEO of Kairos Analytics Ltd. 

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