Category Archives: Models

Customer Analytics: Lifetime Value

Customer Lifetime Value (CLV) is an often over used and over simplified term people use to describe the amount of value a customer generates for your business.  Take this example from Kissmetrics where they average together “expenditure” and “visits” into their variables and then take a random retention rate (r) to calculate the LTV.  From this simple example, they come up with a range of $5k – $25k ??? that’s over a 5X difference and then they average it together??!?! Yikes!

bad ltv

kiss metrics

When I first joined AVG Technologies back in 2010 a customer life time value estimate was generated in a very similar way by simply dividing the total annual revenue by the monthly active users.  Unfortunately, this was a bogus metric as it really over simplified customer lifetime, monetization, and the true cost of acquisition.  Especially considering the fact we had over 110 Million users worldwide.  At its best, the CLV answers in one simple number all of the most important questions about your customers:

Where do my customers come from? How many are making their way through the acquisition and on-boarding funnel to become an active user? and at what cost?

What is the average lifetime of my customers? What impacts their churn behavior and what dimensions are important to segment by?

How many transactions are conducted in the lifetime of my customer? what is the average order value?

Over time, we developed a methodology to extract clickstream cohorts with the channel attribution information and join it to the customer record data.  We then conducted linear regression over a multitude of dimensions to identify the key variables that impact the churn or monetization of a customer.  For more information, please contact me directly @whatisanalytics

At its worse, a bad CLV can lead to over spending on acquiring users; a death wish in the startup space or underestimating the total value a marketing campaign or new product introduction could be generating.

Other great references to look at:


Customer Analytic Models: Cohort Analysis

There are many analytics models to choose from that have been developed over time by financial analysts, marketers, and product managers.  Here is the first of five core analytic models that are essential to making data informed decisions.

At the top of the list is Cohort Analysis, it has been around a long time and is prevalent in medicinal, political, social, and other sciences.  Lately, there has been a resurgence of this form of analysis and how it relates to web and product analytics (Jonathan Balogh, Jake Stein, and others).  There are many excellent explanations of cohort analysis, so I won’t spend too much time explaining the concept.  Overall, Cohort Analysis is the practice of segmenting a group of people by a dimension.  Whether time, geography, demographic, product, or otherwise, the ultimate goal is to see how one group compares to another.

With this simple model, we are able to measure how a marketing campaign, new feature introduction, or an unknown variable causes change in customer behavior.  For example, churn, retention, conversion, or customer referrals are critical for driving growth.

You can focus all of your attention on driving downloads through Search Engine Optimization (SEO), SEM (Search Engine Marketing), and Call to Action (CTA) improvements, but if you cannot retain these customers then its pouring money down the drain.

Lets take a typical online acquisition funnel as an example:

Acquisition, Funnel, Conversion

Typical Online Acquisition Trend

In this example, we can see our website traffic and downloads are steadily increasing, but our active users are staying flat.  Is there an issue with our download to activation process? Do new customers try our product and then leave? or are there older customers who are now churning away?  There is no way to tell without doing a cohort.

How to setup a cohort in simple to understand terms:

  1. Filter by date range (Depending on your volume you can choose between one day to one week or the first 100k activations).
  2. Collect customer behavior, demographics, or any other important dimension over the next 30 to 60 days or until you reach a steady state (churn rate stabilizes).
  3. Assess your cohorts, segment, compare, and calculate the churn and retention rates.
Cohort, Churn, Retention

Simple Cohort Example

In this example, we can see clearly India has a steep initial fall off but then levels out, Germany has a more steady decay, and the US having the least churn of the three.  From here you can see the issue is early in the customer on-boarding, as well as, a significant country behavioral difference.  We can focus on better product and marketing design for those first few days and even narrow our attention to the first few hours.  Additionally, we may want to limit our marketing spend in India until we resolve the high churn rate.

Cohort Analysis is a simple and powerful model to dig deep into your data to find the root cause of an issue and make data informed recommendations.  Next week, we’ll take this concept further and see how we can find indicators of churn with linear regression and correlation.