OBJECTIVE
Estimate the retention
and future spending amount of customers.
DESCRIPTION
In the previous chapter I explained the
principles of CLV and its calculation. However, one of the problems was the
estimation of the retention rate (for which the simplification was to apply the
average retention rate of similar customers) and future spending amounts (we
assumed that the average spending amount of each customer will not change in
the future). Despite the facility of the implementation of this approach, it
can be much too simplistic and fail to estimate CLV reliably.
There are several
methods for estimating retention and spending amounts, but some of them can be
far too complex. The method that I will propose has a good balance between accuracy
and implementation simplicity and is based on customer segmentation and
probability.
The first step is to
take the customer data of year -2 and segment them based on their value and
their activeness. For the value we can use the amount spent in a specific year
(which is a mix of the average amount spent per purchase and the frequency of
purchases), and for activeness we can use the recency of the last purchase (for
example the number of days between the last purchase and the end of the
analyzed year). For activeness we can also use a mix of recency and frequency. In
the second step, we have to define a certain number of customer segments. The
segmentation technique can be either a simple double-entry matrix or a statistical
clustering technique. We can for example end up with six clusters:
- -
Active
high value
- -
Active low
value
- -
Warm
- -
Cold
- -
Inactive
- -
New
customers
The idea behind this
technique is to estimate the retention and spending amount using the
probability of a customer remaining in the same segment or changing segment and
by applying to this customer the average spending amount of the new segment. To
calculate the probability of moving from one segment to another, it is
necessary to segment the customers into year -1 and create a transition matrix (transition
among different segments from year -2 to year -1) in which probabilities are
calculated for each combination of segment groups.
Transition Matrix of Customers’ Segments
With the probability
transition matrix we can simulate how the segments will change in the future and
maybe realize that we are dangerously reducing active customers in favor of
inactive ones and that we need to acquire a slightly bigger number of each kind
of customer to avoid a decrease in profits. In any case with this matrix we can
simulate several years ahead and estimate how many customers will still be
active. We can also estimate their value by multiplying the average value of
each segment by the number of customers of the same segment in a specific year
(year 0, year +1, year +2, etc.).
In the proposed
template, I have added an estimation of new customers acquired each year to
simulate the total number of customers and their value a few years ahead.
However, to calculate the CLV of the current customers, this value should be
set to 0 and then the total value of each year discounted by the discount rate.
TEMPLATE
