The Data Analytics Blog

Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.

All Posts

Closing The Loop With Customer Lifetime Value Insights

November 6, 2015 at 10:18 AM

With marketing budgets increasingly stretched to cover the myriad of channels and touch points out there today, CMOs might feel a little uncertain about whether they’re focusing on the right areas for their acquisition strategies. But even if your marketing spend does deliver positive on-boarding results, it’s still only the first step in a much longer journey with your customer. With the cost of acquiring new business usually five times more expensive than retaining existing customers, and with over 60% of revenue coming from existing customer bases, it only makes sense to build watertight retention strategies that not only retain customers, but preserve the best among them.

Customer Lifetime Value (CLV) is the predicted gross economic value of an individual customer over the entire duration of their relationship with your products and/or services. It is a very useful metric to have for segmenting, managing and propositioning your customer base more effectively, and gives your business insights into its most profitable customers. Also, it allows your business to align marketing, operational, and product strategies more accurately with the needs of the different segments within a given market.

CLV helps reduce churn rates and increase wallet-share

When applied over the entire “lifetime” of a customer’s relationship with your brand, data analytics are instrumental in maximising both the time and profitability of individual customers or market segments. Analysing customer behavioural triggers garners insights into how to reduce churn rates, increase wallet-share or maximise the lifetime value of your customers.  In a report on how data is driving lifecycle management, industry analysts Forrester Research, define customer lifetime value as “[The] Customers’ relationship with a brand as they continue to discover new options, explore their needs, make purchases, and engage with the product experience and their peers. Key to this definition is that customers’ relationships with brands should include new discoveries of value-adds and “reminders” of why they chose your brand in the first place. But this means knowing what your customers perceive as value - and this is where your data comes in. By focusing on real-time customer-based metrics, you get an understanding of factors that influence brand sentiment, profitability, fluctuating needs and customer expectations, and cater to these changing factors rapidly.

Knowing the value of your customers makes managing them easier

Historically, businesses would view Customer Lifetime Value through the lens of campaign executions. This alignment too often overlooked the behavioural patterns and other influences on buying decisions. It also denied marketing and sales departments the right “angle” from which to engage those customers who are already buying their products, with the result being ineffective – and often intrusive - brand messaging that had no bearing on a specific individual. This shot in the dark approach may have been the only strategy available to us in the past, but with the volumes of personal, statistical, transactional and historic data we have access to today, strategies can be built around insights garnered from real-time data that allows for closer alignment and personalisation down to individual customers. Modern analytics also allows us to identify our potentially most valuable customers and focus on those segments which will yield greater returns in the course of their relationship with your brand.

Read our blog on Customer Retention strategies and why its time to get personal

Applying the Pareto Principle to your lifecycle management strategy

The Pareto Principle states that 80% of the effects come from 20% of the causes. In business, this can mean that 80% of your revenue typically comes from 20% of your customers - and it is almost always from the ones who have been with your brand for a considerable length of time. This makes it so important for us marketers to focus more attention on the customers who are spending their money on our brands and listen to what our data is telling us about. For subscription-based business models whose revenue growth is only realised over longer periods of time, as opposed to those based on once-off sales, this is even more pertinent. In the age of the consumer, an iterative approach to customer lifetime value that invests the time and effort to speak to – and not at – your customers will pay dividends and close the loop on our marketing and sales efforts.

Principa has been at the helm of making data work wonders for years now and we’re only started thanks to opportunities availing themselves daily through the medium of technology. We apply data analytics disciplines and methodologies across a wide spectrum of industries and continue our adventures into wonderful world of data. To learn more about how we can help you build better business strategies with your information assets, visit our solution pages.

using data analytics for customer engagement

Image credit: 

Luke Turnbull
Luke Turnbull
Luke Turnbull was the Head of Customer and Lead Analytics at Principa, until the end of 2017, after which he returned to his home country of New Zealand. He worked in the financial services industry since 1995, during which time he worked in process, strategy and operational design across a range of organisations in New Zealand, the United Kingdom and South Africa. Luke had been with Principa for 9 years and led consulting engagements with Principa’s local retail clients across the customer lifecycle, with a particular focus on customer engagement and lead generation.

Latest Posts

How to choose the correct collections chatbot

Principa has a wealth of experience in building and deploying chatbots for the financial services industry. Our custom-built solution is flexible and fully customisable which allows your bot to assume your brand’s persona. We can also seamlessly integrate with existing systems. Click here to find out more. 

Model validation and adjustment

The time is NOW for model validation and adjustment. One of the major premises used in credit scoring is that “the future is like the past”. It’s usually a rational assumption and gives us a reasonable platform on which to build scorecards whether they be application scorecards, behavioural scores, collection scores or financial models. That is reasonable until something unprecedented comes along. You can read about this black swan event in our previous two blogs here and here.

Payment holidays – what did everyone do?

Payment holidays have been used throughout South Africa and around the world to help alleviate the economic stress during the COVID-19 lockdown. In this blog we look at some of the steps taken internationally and by some of South Africa’s major lenders (specifically in the consumer space).