Heres another example of looking past average numbers and diving deeper into the data to get customer insights.

Software Pundit had calculated customer lifetime value (CLTV) to be around $200. This figure was calculated as an average of the entire customer base. Major inputs into the calculation were average order value (AOV), order frequency, gross margin, and churn.

While digging into our churn data, we realized that we had a material percentage of one-time purchasers, and if a customer purchased a few times in the first few months, they basically never churned. Given that churn is a major input, we decided to segment and recalculate our CLTV, said Bruce Hogan, CEO, Software Pundit.

The team discovered it had a material percentage of its customer base with a CLTV around $20 and a material percentage with a CLTV closer to $1,000.

This insight had two significant impacts on our marketing, Hogan said. First, it increased the amount of money they were able to spend acquiring customers, provided the team could determine the customers weren't one-time purchasers.

Second, they ran a series of lifecycle marketing experiments focused on getting one-time purchasers to repeat purchase at the early stages of their lifecycles. Through A/B testing, they found a few tactics that nudged shoppers to repeat purchase, and for a small fraction, this turned into a habit that increased CLTV.

For example, they sent emails with coupons offering a 10 to 20% discount on subsequent purchases. Of the tactics they tested, the coupons resulted in the biggest absolute increase in repeat shoppers. However, most shoppers who used the coupons did not become habitual buyers after the coupons were no longer sent.

Another effective tactic was product recommendations. The companys data science team identified the products that were most often purchased in customers second and third orders. When first-time buyers returned to the site, they would get advertisements for these products, in addition to email promotions and social media targeting. This tactic had a higher ROI than the coupons but did not have as large of an overall impact.

It's critical to segment CLTV. You're better off having an accurate average CLTV than not having a trustworthy figure. However, there's a good chance that this figure doesn't actually describe the CLTV of any individual segment in an accurate way. By segmenting your CLTV, you can unlock more dollars for acquisition marketing, and uncover experiments that will increase CLTV, Hogan said.

How do you analysis the case and CLV of the Case.