- Written by Kevin Edwards on
Calculated as the total revenue a customer is expected to provide over their lifetime, or perhaps more astutely their profit, the importance of Customer Lifetime Value (CLV) lies in it being one of the best metrics to foresee the future profitability of a business, whilst also tying profit to the cost of customer acquisition.
What is Customer Lifetime Value and what is it used for?
Stemming from origins in CRM, Customer Lifetime Value (CLV) is a key marketing metric used to measure the projected lifetime value a customer could provide to a business. Calculated as the total revenue a customer is expected to provide over their lifetime, or perhaps more astutely their profit, CLV is one of the best metrics to foresee the future profitability of a business, whilst also tying profit to the cost of customer acquisition.
In more traditional marketing methods, CLV can be mapped clearly through the customer lifestyle stages from reach and acquisition, right through to the conversion, retention, churn and loyalty. However when CLV is turned to online channels, and especially to the multi-channel and cross-device world of digital purchases, the picture gets muddy, and CLV turns from a straightforward marketing activity, into a conversation on data science.
Why is Customer Lifetime Value (CLV) important?
Nearly two years ago now, eConsultancy and Sitecore conducted a survey asking advertisers and vendors about the importance of Customer Lifetime Value data for their business. 76% of the respondents (of which 470 were clients) cited CLV as a key concept for their organisation, but only 42% were able to measure it. The impetus to use CLV data is clear, but data was one of the greatest obstacles, with 52% of the respondents citing “better use of data” as a method to improve CLV analysis.
With these challenges in mind, we’ve taken a look at five key reasons signifying the importance of Customer Lifetime Value (CLV), incorporating micro studies from a few of our clients at Awin.
CLV can provide a better all-round view of your customers
It’s perhaps an age-old adage, but as marketers, we see channels when thinking of PPC, Affiliates, Email, Search and Display; and the roles they have in serving to acquire or retain customers – however, customers don’t see channels but a brand. Although different channels are predisposed to certain roles, we all too often act in a siloed fashion, when we should be building a grander picture of customers and a brand.
CLV data demands us to look at multi-channel journeys and how channels interact, as we assess a truer reflection of lifetime value. It may only be possible to develop insights on CLV one step at a time, but even small-scale projects, such as measuring the impact of incentivisation for repeat cashback purchases in affiliates, should form a link in the wider chain. To track customers in this way we need to define each customer, and standardising identity perhaps via an anonymous account number, could be one way to bring about uniformity across channels.
Importance of Customer Lifetime Value as a performance metric
CLV could become one of the most significant performance metrics. Focusing on the affiliate channel for this point, it has been clear even building the research for this article, that CLV is a topic on the radar for many advertisers’ 2016 strategy, however, typically this falls into the category of “nice to have” rather than a key business objective.
Although harder to calculate, CLV is a truer reflection of the profit a customer will bring to the business and can dynamically inform marketing budgets and revise the target Cost per Acquisition (CPA). Drilling down to the specific value publishers’ drive for a programme, and specific product groups is one-way advertisers could address this.
CLV enables more accurate customer segmentation, and consequently targeted marketing
One of the key benefits of collating CLV data alongside rich demographic information is the ability to segment a customer base, whether that be on a macro level across channels or a bespoke partner level, in turn allowing better prioritisation of resources and more timely communications. For instance, looking at typical publisher types that customers use to renew a purchase (such as a mobile contract), can help inform when the optimal time frame would be to retarget these customers, such as 12 months after purchase.
It’s vital that CLV and other data points are aggregated together, rather than viewed individually, to help build a more robust picture of consumer trends. There is potential for any new data source to be used as part of the CLV picture, and therefore it’s important to always sense check and contextualise the results, ideally cross-checking against other areas of the business.
It’s also important to stress that at heart, CLV is about relationships between brands and their customers, and we must ensure that customer service is also at the heart of a segmentation approach. Targeting a customer subset can then strengthen customer loyalty, whilst simultaneously improving the overall value of a customer.
CLV focuses marketing activity on quality at a better ROI
Taking points 1 to 3 into account and incorporating them to wider marketing strategies should naturally lead into driving quality, and encouraging better customer loyalty. If we know that customers will churn after a certain period, we can approach our marketing communications at the right time, armed with the right reasons for customers to stay which will improve retention rates.
Knowing which factors impact churn, and how loyalty will pan out by how a customer is initially converted to a brand, could be a way to assess this, with the vision of attracting more loyal customers that deliver a better CLV and in turn ROI.
Utilising CLV data can spark change and innovation
Reconfiguring marketing activity around raising CLV can be a great way to shake strategies on the acquisition and customer retention, whilst also building a longer-term focus.
For example, customer loyalty can be rewarded in a multitude of ways, and using a clearer picture of the customer lifecycle and repeat purchase data, we can step beyond a simplistic new and existing commission model, focusing on a wider approach to a customer’s interaction with a brand. Such an approach could look at how AOV differs by entry route, one or two years in performance, typical affinity products, or what it takes to turn a one-time purchase customer into a loyal shopper or even a brand advocate.
Coming full circle and piecing elements of the CLV puzzle together and why it is important, we need to find ways to “better use data” that enable us to measure CLV but also allow insights to filter into day-to-day marketing activity. This will require visualisation of data such as the Hyatt case and careful consideration of tracking data points that allow smooth analysis that is efficient as it is insightful.
We also need to be careful to let our marketing experience lead CLV rather than the data itself. In a TED talk from January this year, Data Science expert Sebastian Wernicke showed how data can be used to find a hit TV show. He pitched Amazon Studios versus Netflix and their seemingly similar approaches to trying to launch a high-rating show. Amazon let their decision be informed almost entirely by data, which lead to the creation of a 7.4 rating for “Alpha House” by Amazon, whereas Netflix achieved a 9.1 rating for “House of Cards”, incorporating their unique experience that couldn’t be shown in the data.
To summarise, collating CLV data is a great start and an insightful process, but without the context and human analysis, it may fall short. Living in today’s big data world, we need to ensure that CLV data and contextual analysis go hand in hand. To aid this process we need to strive to democratise our data where possible throughout the digital purchase. Funnel combining the interactional, transactional and demographic aspects that not only create a fuller and more accurate picture of our customers but also help make our relationships with them more valuable.