Stemming from origins in CRM, Customer Lifetime Value (CLV) is used widely to measure the projected lifetime value a customer may 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 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.
Nearly two years ago Sitecore conducted a survey asking advertisers and vendors to grade the importance of CLV data for their business. Three quarters (76%) of the respondents 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 CLV can supercharge a business, incorporating micro studies from a few of our clients at Affiliate Window.
1. CLV can provide a better all-round view of your customers
As marketers we see channels as PPC, affiliate, email, search and display, yet customers don’t see channels: they see 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 better picture of customers and our brands.
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. In order to track customers in this way, we need to define each individual and standardise their identity, perhaps via an anonymous account number, which could be one way to bring about uniformity across channels.
2. CLV could become the most important performance metric
Focusing on the affiliate channel for this point, it is clear that CLV is a topic on the radar for many advertisers in 2016. 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 targeted cost per acquisition (CPA). Drilling down to the specific value publishers drive for a programme, and for specific product groups, is one way advertisers could address this.
3. CLV enables more accurate customer segmentation and 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’s on a macro level across channels or on 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.
4. CLV focuses marketing activity on quality at a better ROI
Taking points one to three into account and incorporating them into 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, thus improving 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 ROI.
5. Utilising CLV data can spark change and innovation
Reconfiguring marketing activity around raising CLV can be a great way to shake strategies on 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.
Conclusion
Coming full circle and piecing elements of the CLV puzzle together, we need to find ways to “better use data” that enable to us to measure CLV, but also allow insights to filter into day- to-day marketing activity. This will require visualisation of data, much like the Hyatt case, and careful consideration of tracking data points.
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 from 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 interactive, transactional and demographic aspects that not only create a fuller and more accurate picture of our customers, but also helps make our relationships with them more valuable.
