Learn how to identify your highest-value customers and grow profitability.
Most marketers are familiar with the concept of customer lifetime value (LTV), but many might not be aware of all the ways LTV models can be used to optimize marketing decisions. In its simplest form, customer LTV can be defined as the monetary value of a customer relationship, based on the present value of the projected future cash flows from the customer relationship. Customer lifetime value is an important concept in that it encourages brands to shift their focus from short-term profits to the long-term value of their customer relationships. Customer lifetime value is an important metric because it also informs the upper limit that should be spent to acquire new customers. Beyond customer acquisition, customer lifetime value can also be applied to customer marketing and loyalty efforts.
At DELVE, we’ve identified 30 use cases for applying customer LTV modeling to both customer acquisition (awareness, consideration, purchase) and loyalty to help marketers make better decisions.
Applying LTV to Customer Retention Strategy
In the COVID era of eroding brand loyalty (McKinsey notes that up to 40% of consumers are switching brands), the need to grow and retain the existing customer base is very important. As with customer acquisition, there’s also a substantial cost associated with the process of retaining existing customers (i.e., by giving discounts, targeted offers, etc.).
So does this mean that brands need to retain every customer?
Not so fast.
In every business, some customers create more economic value by being loyal while others are just one-time buyers. By identifying these groups and targeting the highest-value customers, brands can allocate their limited marketing resources to where they can generate the highest ROI.
LTV Models Work for Any Industry
What’s great about LTV modeling is that it’s applicable across a variety of industries: e-commerce, financial services, communications and more. Once the model is prepared, it can help marketers uncover incremental revenue opportunities they may be overlooking.
The best part?
Many brands are still slow to adopt LTV models—which offers early adopters the opportunity to gain competitive advantage.
A typical LTV model generates three main data points for each customer:
1) Probability that the customer will make additional purchase(s)
2) Expected cart value for the customer’s next purchase
3) Expected revenue the customer will generate over time*
* However, the LTV model approach also can be applied to identifying potential profitability over shorter periods of time—such as the remaining part of a year or a 12-24 month time horizon.
These simple data points open the door to a realm of near-endless possibilities.
Because LTV models use proprietary company data, they won’t be affected by changes in cookie tracking. After successful integration in a data warehouse, it could be an investment that pays for itself in a matter of weeks and a source of serious dividends for the years to come.
Let’s take a closer look at the 30 use cases of the LTV model, segmented by the conversion funnel stage. Each use case will be a direct or indirect source of incremental revenue.
Applying LTV Models in the Awareness Stage of the Buyer Journey
LTV models can be used to identify and target high-value audience segments for marketing campaigns, improving your ability to:
1. Identify customers with high lifetime value and structure Google/Facebook ad platforms to find lookalikes
2. Increase bids for generic keywords for customers with high expected revenue
3. Exclude customers with low expected revenue from video campaigns
4. Adjust bids in programmatic campaigns to gain more loyal customers
5. Measure the LTV of newly acquired customers and adjust cost per acquisition accordingly in your media strategy
6. Reduce programmatic media cap for low-LTV customers to save cost, while increasing programmatic investment on high-LTV lookalike audiences to maximize revenue potential
Applying LTV Models in the Consideration Stage of the Buyer Journey
LTV models can be used to better understand the buying behavior of high-value audience segments, improving your ability to:
1. Understand site behavior broken down by customer segment
2. Identify which product categories are popular among frequent buyers and recommend them to infrequent buyers
3. Know your audience better by examining the differences between high and low expected revenue segments
4. Identify inactive high-LTV customers and send them an offer designed to win them back
5. Introduce an incentive for customers with medium expected value to opt in to your newsletter
6. Research the frequency of purchases for high-LTV customers and adjust the frequency of marketing emails accordingly
7. Discover patterns in frequency when the most important products are bought, and use those insights to design discount actions
8. Create different onboarding strategies for customers with predicted high LTV
Applying LTV Models in the Purchase Stage of the Buyer Journey
LTV models can be used to used to optimize offers shown to audience segments for initial purchases, improving your ability to:
1. Use alternate recommendation engines that optimize for conversion rate—not revenue—for customers with low probability to buy, aiming to convert more of them to the higher LTV segment
2. Employ a prediction model to see which new customers today are likely to become high value customers in the future—and invite them to your loyalty program
3. Add a special promotion to the abandoned-cart reminder for customers with low probability to buy, maximizing (at the expense of margin) their chance of conversion to frequent buyers in the medium to long term
4. Differentiate recommended products between customers with low and high baskets
Applying Customer LTV Models to Customer Loyalty
LTV models can be used to optimize offers shown to audience segments for repeat purchases, improving your ability to:
1. Send a retention offer to past high-value customers who have low probability to make another purchase
2. Give a freebie to certain customers and learn how it influences long-term brand loyalty
3. Send a gift to your 10 most loyal customers
4. Design a focus study and invite high-LTV customers
5. Find users with low likelihood of making the next transaction, discern the characteristics they share, then employ well-thought preventive measures
6. Introduce a separate mailing strategy to loyalize customers with low probability to buy
7. Exclude high- and medium-LTV customers from promotional emails (about recently bought categories) for a few days after their purchase
8. Adjust marketing strategy to long-term customer value and optimize beyond the present day
9. Redefine your loyalty program to reflect your findings (for example, including high-frequency buyers with low basket value)
10. Measure the impact of free shipping for long-term customer loyalty, and pick the minimal basket value that maximizes long-term customer LTV
11. Analyze which revenue streams are more reliable than the others
12. Run endless A/B tests to optimize the cost of your loyalty program and maximize the long-term upside
The Bottom Line
Markets are becoming increasingly competitive and business disruptions are now common in the normal business cycle. All of this adds up to less predictability—and the need for brands to work smarter, not harder. Marketers have a great opportunity to apply data analytics to decision making, helping to gain first-mover advantage and accelerate profitable growth.
Starting with a basic concept such as customer lifetime value modeling is a great place to start. Applying customer LTV models across the entire buyer journey—including retention and loyalty—can drive measurable gains that are “low hanging fruit” in the quest to improve marketing ROI.
Ready to get started? Talk to a DELVE expert: begin@delvedeeper.com.
Michał Wrąbel
Director of Data Science