Establishing Lifetime Value

A few thoughts on how to approach Lifetime Value for eCommerce, in an actionable way.

What’s in a value of a customer? Sometimes open to debate, the discussion is often advanced the right analysis.

This was an interesting, numerical read on Lifetime Value by KissMetrics using Starbucks as a case study (click to expand).

The equivalent can (should) be done for eCommerce whether your revenue is monstrous or tiny.  All that matters is consistent data that you can trust.

 

ESTABLISHING LIFETIME VALUE

Let’s start with the Why.

Determining a LifeTime Value (LTV) benchmark for your eCommerce helps establish profitability, acquisition and margin thresholds for owned, earned and paid efforts.

(For another thorough and a remarkably still relevant read, check out Avinash’s classic LTV post.)

To get beyond the abstract, here’s a Data crunch to begin (or to send on to your analytics person/team).

The goal: Rolling 3 year averages to establish “MVC” (Most Valuable Customers) and Average Customer (AC) – here’s what we ran. After some data hygiene work:

1. For all customers who made their first purchase in 2015, what was the # of purchases, at what Average Order Value, and how many repeat purchases/what value in the same year? From the same customer set, run the same analysis for their Year 2 (2016), Year 3 (2017) and Year 4 (2018) purchases?

2. For all customers who made their first purchase in 2016, what was the # of purchases, at what Average Order Value, and how many repeat purchases/what value in the same year? From the same customer set, run the same analysis for their Year 2 (2017), Year 3 (2018) purchases?

3. For all customers who made their first purchase in 2017, what was the # of purchases, at what Average Order Value, and how many repeat purchases/what value in the same year? From the same customer set, run the same analysis for their Year 2 (2018)?

ADDITIONAL SEGMENTS

Because Averages are limiting when providing insights, looking at subsets of LTV can be more telling. Start by collecting data on:

  1.  Customers whose first purchase was a discount. (Discount Buyers LTV)
  2.  If activity is disproportionate in November/December as with most eCommerce, customers whose first purchase was a holiday season purchase. (Holiday Season Buyers LTV)
  3.  For Most Valuable Customer analysis, first determine what the top tier of Average Order Value is and then segment Lifetime Value for those buyers. (Most Valuable Customers LTV). Additionally determining Lifetime ROAS for those acquired by digital campaigns will be helpful here to inform digital campaigns).
  4.  eBlast repeat purchasers (frequent opens and click throughs, which result in add to carts & purchases). (Engaged Email LTV)
  5.  Single category purchasers vs Cross-category purchasers. (Product/Category specific LTV)

 

CHURN, REPEAT, MARGIN, CAC & LTV (OH MY)

After establishing these metrics, a bottom-up model can be created to be more accurate and reflective of current and predicted future customer yield.

Expand on the Segments, adding Repeat Purchase frequency, Average Order Value and Conversion Rate %

As you can see from the “CYCLE Data” table above, in this case Lifetime Return on Ad Spend allowed for a (far) more aggressive threshold for Customer Acquisition Cost; leading to wider targeting, higher velocity and scale through high-ROI channels.

A longer term view of your customer activity can drive growth decisions. Just start.

Leave a Reply

Your email address will not be published. Required fields are marked *