Teleflora + Custora: Using CLV to Drive Sustainable Growth


We recently hosted a webinar with Tommy Lamb, Teleflora’s Director of Loyalty and Retention. Tommy, who has previously held positions at Dermstore, Lucky Brand, and BCBG, walked us through the ways his team is leveraging customer data to drive sustainable growth. Check out the full video below.

Custora’s CEO, Corey Pierson, kicked off the webinar by discussing why customer-centric metrics such as customer lifetime value (CLV) are essential indicators of the health of retailers’ customer databases, and why customer retention is becoming more important than ever.

Tommy then shared four case studies—churn prevention, VIP programs, and two cases of advanced segmentation. In each, he used Custora’s predictive algorithms to select the best audiences at key moments in the customer lifecycle. He explained how he determined which opportunities to pursue, how he began testing offers and creatives, and how he used holdout groups to measure the success of each campaign.

Fill out the form below to get the recording.

Why “average” retention rates can lead to 50% error in CLV

Pitfalls in calculating CLV – why using an “average retention rate” can lead to grossly inaccurate CLV numbers

Customer Lifetime Value (CLV), the total profit a business will make from a new customer, is a valuable metric.  It puts a theoretical cap on what a firm is willing to spend to acquire a new customer.

CLV is predictive – it’s a projection of what a new customer will spend over time.  As a result, there are  “good” and “bad” CLV numbers – the closer the prediction is to what actually happens, the better.

Unfortunately, some common techniques used to calculate CLV produce results so inaccurate they’re basically meaningless.  In this post, we’ll explain why one common pitfall – using an “average retention rate” – can lead to CLV predictions with margins of error over 50%.

The most common CLV formula taught in textbooks is:

CLV Formula

Here, m represents the monthly payment, r the month-over-month retention rate, d the discount rate, and t is the time period.  The formula is basically a proxy for, “on average, customers stick around for X months and pay Y per month when they’re here.”  There are too many problems with this equation for a single post, but for now we’ll focus on the retention rate, r.  Retention is usually the most significant driver for CLV, but when we try to make a calculation, we have to decide – what value should we choose for r?

The common mistake – use an average retention rate

Figuring out the “average retention rate” is a common, but logically flawed, first step.  For example, one might look at the overall customer base and count how many people leave, or churn, in a typical month.  This type of thinking can have a disastrous impact on CLV accuracy.

The issue is that blending customers into an “average” significantly distorts reality.  If you gain two customers – one with a retention rate of 100% and another with a retention rate of 0% – you can imagine how the situation would play out.  The first customer would pay forever, and the second would leave right away.  However, if you first average the two retention rates together, you’ll have two customers with a 50% retention rate.  Both will be gone within a few months.  Order of operations is critical.

How average retention rates can undervalue CLV by over 50%

Imagine we have two groups of customers who sign up for our business – 100 customers in Group Awesome, where customers have a month-over-month retention rate of 90%, and 100 customers in Group Sad, whose customers have a 10% monthly retention rate. In this sample business, customers pay us $10 per month until they leave.

If we follow the “average retention rate” school of thought, every customer has a month-over-month retention rate of 50% (the average of the 90% group and the 10% group).  To calculate expected revenue per customer, we need to simulate how this group of customers will drop over time.  Each month a customer is “alive,” we’ll earn $10.

Customer attrition over time with average retention rate

Rather than looking to total lifetime revenue, for the purposes of this example we can simplify things and focus on what customers will spend over a year.  Paying $10 per month while they’re active, this group of 200 customers will spend a total of $3,984.38, or $19.92 per customer.  The number is low for a $10/month business because very few customers stick around for long.

Next, as an alternative, we’ll let both groups run their own course instead of taking the average retention rate.  As we look at the same type of graph, we start to see the why this is so important.  The 100 Group Sad customers will be almost entirely gone after a month, but many of the 100 Group Awesome customers will continue to pay for quite a while.

Customer attrition over time with two groups The total revenue from the same 200 customers paying $10 while they’re active is now $8,285.70, or $41.83 per customer.

Using the average retention rate undervalues the 1-year value by over 52%.

We can extend this line of thought into a more complicated (and perhaps realistic) scenario.  Let’s assume we have three groups of 100 customers who join our business: Group Awesome (95% month-over-month retention), Group OK (75% monthly retention), and Group Sad (55% monthly retention).  Again, we’ll compare using the average retention rate for all 300 customers – in this case 75% – to playing each group out on its own.

Customer attrition over time with average retention rate

The 300 customers will spend $11,619, or $38.73 per customer.

Customer attrition over time with three different groups

The 300 customers will spend $15,278, or $50.93 per person.

Once again, the “average retention rate” approach undervalues the 1-year value by over 23%.

The reason this impact is so profound is that, when we calculate CLV, retention rates are compounding.  There’s a big mathematical difference in taking the average before compounding r compared with taking the average CLV after compounding each individual’s unique r.

Summing it Up

For CLV to be meaningful, it must be accurate.  Unless your business happens to have customers who are all identical, the techniques we use to calculate CLV need to respect that different customers have different retention rates.  The discussion should be one that describes these differences – not one that focusses on aggregate attrition.

The retention rate is just one place where things can go awry when calculating CLV.  We’ll touch on more common pitfalls in some upcoming posts.