One strength of customer lifetime value is also its weakness: the simplicity of the data needed to calculate it.
For a strong CLV model, all you need is RFM: recency, frequency and monetary value of customer transactions. In other words, you just need a customer ID, the date of purchase, and how much they spent.
Most companies track customer transactions as a necessity; a company that doesn't track how much of each item it sells won't stay in business for long, and almost all databases containing this type of information are queryable to collect the information needed for CLV.
Why is this a weakness? Getting to the next step of providing valuable insight for departments across the organization can be more difficult due to the complexity of combining the data across different domains and sources.
In this post, I hope to encourage you to take on the work of combining this data. The use cases are exciting and have the opportunity to drastically shift your organization toward using a customer lens for their decision-making, providing a large advantage over companies stuck in the era of product-focused decision-making.
Below are seven secondary data sources that you should consider merging with your customer lifetime value data for next-level insight.
1. Customer segmentation
If your marketing department has put in the work to create a customer segmentation for your company, I highly recommend investigating the CLV of these segments. You'll quickly add another actionable use case for the segmentation model and help the company make better decisions about how to acquire, communicate with, and develop each segment of their company's customer base.
2. Loyalty program
Loyalty programs are often tiered, meaning that customers with higher value to the company enjoy higher-status.
One drawback of loyalty programs is that they look retroactively at customer data. Customers only earn status after a history of high spend. However, using the estimated customer lifetime value from your CLV model, you can quickly identify the high-potential customers who should belong to a high-tier program but who may not yet. You can proactively encourage these high-potential customers (and spend more doing it than you would on average customers) to join your loyalty programs.
Similarly, loyalty programs come with the cost of the bonuses offered to loyalty members. Often, companies ballpark how much they should spend for these bonuses and are extremely conservative. Loyalty programs can be perceived by high-tier members as too watered down to truly be incentivizing or to make them feel valued. By incorporating CLV, you can better understand the value of high-tier members, and see if your bonuses can be strengthened to truly delight your high-tier customers and turn them into lifelong customers.
3. Advertising
We've discussed ideas for using customer lifetime value for advertising success in a previous post. To reiterate, your CLV model is enormously valuable to your advertising department because it will much more accurately help inform the long-term value of advertising campaigns instead of their relying on faulty short-term metrics like cost-per-acquisition or return-on-ad-spend.
Similarly, advertising teams are often best positioned to understand the different acquisition channels of customers and hopefully collect data on which customer came from where. By assigning CLV to customers in this dataset, you can aggregate at the acquisition channel level to see where the highest-value customers are coming from. Depending on the richness of this data, you can also start to investigate geographies, device type, and other potentially interesting breakdowns.
4. Website clickstream
Depending on where your website's clickstream data lives - perhaps in Google Analytics or Adobe Analytics - there is an untapped well of insight to be learned by combining this data with your CLV model.
Which pages of your site encourage the highest-value customers?
Which pages are driving them away?
Is there an interaction effect between the campaign you use to drive customer to your site, the section they browse, and their lifetime value (often different sections appeal to different audiences)?
Do your high-value customers try to find information differently than your low-value customers?
Are automated chatbots sufficient for the needs of your high-value customers, or is it worth having a more personalized channel for them to reach out through?
Your CLV model holds the answers to these, and many more, valuable questions!
5. Sales
Often salespeople approach their leads with limited information; perhaps they know basic demographic information like location, age, income, and have some idea of how they might use the product being sold.
Arming your salespeople with expected CLV of the potential or existing client provides a powerful new tool. When considering the CLV of similar clients, salespeople can better estimate the level of effort and resources to invest in a particular sale.
6. Product development
How can a company leverage customer lifetime value to improve product development efforts?
Consider that a company's high-CLV customers are also the company's biggest promoters, sharing their love of the product or service with other friends, family or colleagues. The reason this happens is that the product is particularly well-positioned for these customers, and solves a specific problem in exactly the way that's needed.
Low-CLV customers are similar to high-CLV customers in that they have a need for the product or service, but differ because their propensity for purchasing it is lower. This can be for various reasons, from household or workplace economics to personal preference to their specific use case.
Generally, the things that high-CLV customers want today will become those things that low-CLV customers want tomorrow, and this rarely goes the other direction. (Consider the adoption curve in technology products as a good example... I thought Apple's AirPods looked silly for the first few years before buying and loving them myself!)
Knowing that the desires of high-CLV and low-CLV customers will differ, but that the things high-CLV customers value will translate to the things low-CLV customer value, it's important that your product development efforts focus on the wants and needs of high-CLV customers. Identify the products and features that high-CLV customer love and invest in these. If you try creating products for the infrequent, unengaged customer, what's the chance you'll build something your top 1% of customers will enjoy and continue to tell their friends about?
In short, design products that your high-CLV customers love and your low-CLV customers will love them too.
7. Analytics data hub
This last idea is a more generalized concept. One of the most important assets for a company is its data, and data is only valuable when it is shared across departments within a company.
There are endless possibilities when it comes to leveraging CLV to improve your business, many of which I'd never think of, but your teams might!
Invest in making your CLV available through an internal analytics database for other teams to work with, and the data scientists and analysts with newly gained access will blow you away with their creative ideas (and have a great time doing it).
The more crossover, the better
The ideas shared in this post are only a few of the many ways that cross-domain data development can bring significant value to your company, far greater than the "sum of the parts" when the data is isolated across different siloes.
Consider the value of cross-domain data development as a network effect; more interconnected nodes make the value of the whole system far greater. Each investment in uniting data across siloes will yield huge benefit to your organization, not to mention for the specific use cases where layering in CLV can give you a competitive edge.
Getting your first CLV model up and running based on your transactional data is a monumental first step, and once you've gained insight from this model, look for other ways to improve your business by weaving CLV into other data available throughout your organization.
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