The CLV Revolution: Transform Your Ecommerce with Customer Value Optimization

many KPIs focus on what the company gets, rather than what the company gives.

One reason why an acquisition-only approach will fail is because it doesn’t have your customers’ unique journeys in mind, or how the way they define “value” changes over time. The acquisition-only approach is too busy bombarding people with a stream of “buy-now” messages, offering specials and discounts they might not care about.

Acquisition marketing alone does not work long term for all business models. If what you sell gets bought multiple times, it may work for short bursts, but in the long run, it’s called “churn and burn” for a reason—you will burn yourself out.

The CVO methodology is a working process that helps e-commerce professionals and businesses deliver value across every stage of the customer’s journey: acquisition, onboarding, retention (prevention), and reactivation.

the PECTI framework. It helps you invest resources as wisely as possible, as plot campaign initiatives. PECTI is a hybrid between two CRO prioritization frameworks: PIE (Potential/Importance/Ease); and TIR (Time/Impact/Resources). PECTI takes into account 5 different criterias, rated from 1 to 5: potential, ease, cost, time, and importance.

you must understand another vital metric: the CAC Payback Period. In a nutshell, the CAC Payback Period is the time it takes for your company to earn back your customer acquisition costs.

The fact is CLV is not a fixed reality. Impacting it is well within your reach if you know where and how to look inside the numbers.

The most critical aspect of this step is expanding your view of the dominant numbers. Until you start to monitor and measure key metrics related to CLV, and build a game plan towards improving them, you will continue to over-optimize the two or three dominant numbers you’re used to.

The main difference between clustering and segmentation is that clustering uses unlabeled data to find hidden patterns by using an unsupervised machine-learning algorithm.