Practically every marketer today knows how to calculate customer lifetime value (CLV).
The specific CLV formula to determine what a customer is worth based on things like repeat purchases and price per purchase may deviate from brand to brand.
The general method for how to calculate customer lifetime value to identify profitable customers, though, is essentially the same at just about every organization.
Some marketing teams today calculate CLV with — and, in turn, make critical business decisions from — manually updated spreadsheets. These spreadsheets feed into a customer revenue prediction model, which only uses some of their internally sourced data from siloed systems in their stacks.
This isn’t exactly the ideal means to evaluate existing customers and associated metrics associated: customer acquisition costs, revenue earned, average order value, and the like.
In fact, there’s a far more efficient means to calculate customer lifetime value — a model that can enable you to use CLV in conjunction with all other buyer insights.
How to calculate customer lifetime value: A refresher for marketing professionals
As noted, there are distinct CLV models used at companies worldwide. Having said that, the general customer lifetime value formula (not to mention those for calculating retention rate and churn rate) is the same at just about every business you’ll come across today:
The bare-bones customer lifetime value definition is the revenue a company can expect to generate from a given person over an extensive time period — a buyer or subscriber’s “life expectancy.”
Purchase history for specific products and services and online and offline customer behaviors factor into a marketer’s ability to measure and improve CLV over time.
What’s more, CLV informs other departments within a company as well: from how B2B sales reps approach acquiring new customers to how B2C customer support teams prioritize ticket responses.
All in all, customer lifetime value represents arguably the most important metric for businesses of any kind and in any industry today. It’s also one one that requires constant monitoring by marketing.
Simply put, the more attention paid to improving customer profitability, the better.
Why predictive modeling in a CDP matters
Knowing how to calculate customer lifetime value is essential today. But doing so in a quick, efficient manner that allows for measuring customers’ profitability in real time equally vital.
That’s where predictive modeling and a CDP come into play.
The role of artificial intelligence (AI) in marketing today continues to grow at a remarkable pace. It’s easy to see why the tech (i.e., machine learning) is being used more often:
It helps everyday marketers immediately, efficiently, and automatically calculate predictive scores like customer lifetime value based on a vast volume of user data.
What’s more? In a CDP, you can easily calculate and attach CLV scores to an individual level profile to build segments based on high- or low-CLV customers, for example.
With data coming directly from a profile database, you don’t have to worry about pulling manual lists before building CLV models.
With AI Workbench, BlueConic users have access to out-of-the-box, machine learning models that tell them buyers’ propensity to purchase, when they might churn, and — that’s right — lifetime value.
By incorporating a customer’s historical transaction data, AI Workbench constructs a predictive model that calculates CLV based on:
- Probability a customer will buy again: With historical purchase information and behavioral data (e.g., frequency, recency, and intensity of digital activity) incorporated in your CLV model, you can accurately gauge which customers or clients are likely to buy or subscribe again down the line.
- Likely number of purchases made in the prediction period based on past buying behavior: Similarly, the machine learning predictive models deployed in AI Workbench can use the same data sets to determine how many times an individual or segment of customers are likely to buy over time.
BlueConic customers can, in turn, get a CLV score that they can attach to individual-level profiles. By enriching profiles with predictive scores, you can get the customer insights you need to not only retain customers, but also cross-sell and upsell them with personalized and individualized offers.
Similarly, you can use these insights to provide bespoke customer service and customized offers tailored to their unique interests, actions, and behaviors.
What you can do to boost CLV scores and customer loyalty for your buyers
Now, how you elevate the average CLV score for all customers — and, in particular, those for your highest-value buyers — will certainly differ from the strategy other companies employ.
If a furniture retailer aims to increase customer lifetime value for those who order $1,000-plus of home furnishings once every 12-24 months, its predictive CLV model for those buyers will be different than one for a retail business whose customers buy lower-cost items (e.g., those less than $50) more often.
Why is this the case? Because the former company isn’t likely to get business from an individual making a sizable furniture purchase more than once a year, in all likelihood, whereas the latter organization may see repeat customers return to buy again within weeks or even days of their most recent purchase.
Factoring in your business model with your CLV formula
As BlueConic’s Customer Success Manager, Ali Paradiso, noted on our AI-for-marketing webinar, you can tweak your CLV model to match your sales cycle and your products’ and services’ price points:
- If your brand has a typically long sales cycle and low-frequency products, your predictive customer lifetime value model will likely require a longer timeframe in order to capture data necessary to make an accurate calculation. For example, you might want to use purchases over weeks or months instead of days for your model, then ensure the chosen observation period is large enough to observe multiple purchases in order to deliver the most accurate calculation.
- Conversely, if your business has a generally short sales cycle and high frequency of products sold, you could use daily or weekly purchases to more accurately calculate CLV.
The more you understand your business model, the better your machine learning predictive modeling efforts will provide relevant, detailed, and timely insights about your entire customer base you can use to continuously enhance your overall customer lifetime value.
Creating new customer segments based on CLV models
Once you have a wealth of info about your customers’ value to your organization — order frequency, purchase price points, propensity to buy, etc. — you can develop dynamic customer segments based on CLV scores and other key data points to help with cross-channel personalization.
It’s no secret there are far greater marketing ROI opportunities by targeting or creating premium experiences your most valuable customers — repeat buyers, long-time subscribers, and luxury-product purchasers – than your least valuable customers: one-time buyers who’ve shown little intent to buy again (or at least in the near future).
With CLV scores you can schedule to update in BlueConic profiles and segments that adjust in real time, you’ll always have a clear distinction among your audience groups, be able to prioritize your efforts, and ensure delivery of the best messaging to each segment.
In recent research, Salesforce discovered retail shoppers who receive personalized recommendations via brands from whom they’ve bought from before accounted for only 7% of their overall call-to-action clicks. However, those recommendations drove one-quarter of orders and revenue for said deals.
If you know the previous buyers who seem most likely to purchase from you again and are able to target said individuals with pertinent offers at opportune times (e.g., following their most recent site visit), the cumulative customer lifetime value for those individuals is likely to bump up appreciably.
Time to prioritize advanced customer lifetime value models — and use a CDP
If you have martech in place that helps you track customer lifetime value and other core marketing metrics for which your primarily responsible, that technology can likely give you some useful insights about who the most prized buyers are in your database and deserve your marketing attention.
But predictive modeling through a solution like ours can help you and your team extract even more value from your most important buyers (and perhaps even the lesser-priority ones for whom you can test discounts and deals) and interact with them in real time as their behaviors change.
Simply put, by incorporating machine learning functionality into a CDP like AI Workbench, you can up-level your targeted marketing activities and digital strategy as a whole.