Lifetime Value (LTV) has always been a great tool for prioritizing marketing investments. The concept is simple: imagine you have two customers, John and Dave. John is a frequent shopper, shops high margin items and produces $2,000 annual profit for the company. Dave, on the other hand, shops once in a blue moon, is not loyal to the brand, frequently calls the Call Center and produces $200 annual profit for the company. Having this knowledge, it makes sense to spend more marketing dollars to acquire and retain a customer like John instead of Dave. Customer Lifetime Value is a North Star metric that guides investment decisions for the Chief Customer or Chief Analytics Officer.
One of the most brilliant examples of LTV is a use case from a telecommunications company that uses it to assign “heart” scores to each of their customers based on their LTV. Customers with five hearts are most valuable and get the most attention and care. On the other hand, customers with one heart are recognized as largely unprofitable customers. The telecommunications company continuously learns from these profiles and avoids wasting marketing dollars acquiring customers with the lowest heart scores. Each customer is scored throughout the life cycle, from acquisition to onboarding to maturity. Churn offers for a given customer are determined based on how many hearts that customer has.
- Make sure you have a well-defined LTV metric.
It’s puzzling to see how many companies still look at just “number of new customers” or “cost per account” to determine success for their customer acquisition programs. CPA in absence of LTV is a myopic metric that leads to suboptimal decisions: it only looks at customers at a singular point in time What if a big percentage of new customers acquired churns within first 90 days? Is that the outcome we want to create?
- Make sure the analytic methodology is robust and does a good job of attaching a value number with reasonable degree of accuracy.
You don’t need to use the most sophisticated survival modeling methodologies by customer segment here, but the underlying methodology needs to be robust in differentiating between low value and high value customers.
- Use it to drive marketing investment decisions to create impact.
Without action, there is no impact. Make sure LTV project doesn’t end up as a fancy PowerPoint deck on the CEO’s desk.
- Develop LTV in a phased fashion.
Get a basic, robust version of LTV out there and start testing it and increasing the sophistication over time through machine learning techniques such as survival modeling for determining expected life of a customer.
- Tie the development of LTV to specific use cases that will increase revenue and profits for the business.
The project is almost guaranteed to fail if you run it as pure technical project without strong connection to business problems or use cases that the C-Suite cares about.
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- Don’t overcomplicate it.
I see examples of companies that spend months to arrive at the perfect methodology and miss opportunity to create quick wins.
- Don’t just apply to existing customers.
LTV is a concept that could guide the investment decisions across the entire customer lifecycle. Some of the most impactful LTV solutions I have seen were focused on new customer acquisition
- Don’t put precision ahead of ability to use to make better customer investment decisions.
Who cares if LTV of a given customer $1,400 or $1,405? The reality is, nobody really knows. The ability to use to make better decision and put the customer investment decisions is more important. Using 80% solution creates better outcomes than not using any solution.
- Don’t ignore the referral value.
For some businesses, the referral could be quite significant, and this will continue to be an important value dimension for many businesses where word-of-mouth matters so much for expanding the customer base and driving growth. Referral value typically correlates well with direct transactional value.
- Don’t forget about the longer-term impact.
Most of the LTV metrics are based on short-term profit potential of customers like the expected spend over the next 12 to 24 months. This is a great place to start but how about if the longer term (downstream) impact of these decisions? A great example for this is a customer who buys diapers on a multi-category ecommerce site. In the short term, the ecommerce company may not make a lot of profit on the sale of diaper which happens to have low margins but the diaper purchase could be the catalyst of establishing a habit for repeat purchases which could bring lots of profit to the business.
Ozgur has 20+ years of experience in building data driven solutions for many Fortune 500 companies including AT&T, P&G, Citi and AARP. Ozgur is currently Chief Solution Officer for BLEND360 and leads the development of data driven solutions for BLEND360’s clients by integrating data science and digital technologies. BLEND360 was founded in 2002 and has been recognized as one of the fastest-growing companies in the US in 7 out of past 8 years.
Ozgur is also a founding partner of Whitegate Capital Partners, which is a capital and growth firm focused on Data, Analytics and Technology markets, engaging as major investors and advisors.
Prior to that, Ozgur was the CMO for Hepsiburada, which is named as one of top 5 fastest growing e-commerce companies in Europe and one of the top 25 e-commerce sites globally. Previously, Ozgur was the General Manager of Data Solutions Group at Merkle, a global data-driven, technology-enabled performance marketing agency, which was the largest independent agency in the US for CRM, digital, and search before being acquired by Dentsu.
Ozgur was the recipient of multiple awards within Merkle, including Exceptional Client, Operational Excellence, Database Marketing Excellence and the Chairman’s Award, which is the highest recognition within Merkle. Ozgur has spoken at various Marketing Events globally. Ozgur holds a B.S degree in industrial engineering and an MBA degree with a focus on marketing from The University of Georgia.
I would love to hear from you if you have any additional thoughts or comments about LTV. I would be interested in hearing about your experiences and major challenges you encountered in developing LTV for the organization.
This article first appeared in www.marketingmagnified.com
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