welcome to the databricks solution
accelerator demos
your fast preview of how to apply these
pre-built notebooks based on best
practices to solve common business
problems
today we will be looking at the solution
accelerator for customer lifetime value
customer lifetime value just like all
customer analytics is a key area of
focus for the lake house
most organizations are coming to the
realization that not every customer is
equally profitable
and it is important for them to
understand who their good customers are
and who they're not so they can adjust
investments in the good ones and bring
everybody to net profitability
the standard tool for this is an
estimation of customer
lifetime value simply put the customer
lifetime value is an estimate of the
value that we expect to obtain from a
customer
whether that's in terms of spend or
margin for each have some number of
future periods
typically three to five years not
knowing whether the customer will stay
engaged for that time period we have to
incorporate a retention estimate into
our considerations
and of course we have to apply discounts
for future revenues
summing this all up we arrive at a
potential sum that we expect to obtain
from a customer for the period of
interest
now the challenge we have in most retail
organizations is that we do not have
contractual relationships with our
customers
we instead have to take a look at the
pattern of engagement that the customer
establishes
and from there estimate retention and
monetary value components to factor into
a customer lifetime value
or clv the work for this was done by a
series of researchers
back in the late 1980s and then
popularized in the 2000s
and now packaged today as a very popular
open source library known as lifetimes
we'll use that library and databricks to
help us estimate clv in the solution
accelerator demo
here we're looking at part one of a
two-part series where we tackle
retention and the value components of
clv
our blogs have our perspective on
retention and value estimation and links
to resources that are helpful as you
explore what fits into your organization
we're going to jump right into the code
that's available inside the attached
notebooks
these notebooks are built off of
publicly available data sets and show us
how we can go about estimating customer
lifetime value
here we're looking at transactional
history for individual customers
we have the customer's unique identifier
here and we have the dates on which the
customer was engaged
from this information we can make a
series of estimates which we scroll to
down here
of the frequency recency and term of
repeated engagement over the lifetime of
the customers for this period
from that we can go through a process of
building our models again
examining the patterns that individual
customers establish with us
relative to the overall patterns in the
population we spent quite a bit of time
in here exploring the nuances of the
models that are produced
and how to understand whether they're
applicable to individual businesses
but let's come down to this one image at
the bottom that really captures what
this model is doing
here we're looking at retention and what
we can see is that with each engagement
as represented by these red lines we're
gaining more and more confidence in our
customers overall retention
though as time proceeds as we move
between engagements there starts to
become some doubt as to whether these
customers continue
to be engaged but again with each
interaction our understanding of the
customer shifts
and the speed with which our competence
degrades changes as well
now that's the first part of our
retention model in the second part of
our blog we tackle the value part of
this exercise
again we use the same data set we're
going to come down here and spend some
time focusing on the monetary component
we're going to use sales amount because
we don't have information about the cost
of these goods so we can't look at
margins
but some organizations might do that
based on this we're going to come in to
add to our set of metrics a monetary
value metric
which captures the amount of money that
was spent and secondary engagement
so not the primary but the follow-up
engagement from that we can build a very
similar model that we saw before
and when we bring together both our
retention and our monetary value model
which i get to down here we have the
ability to estimate a clv
now here i'm doing a 12-month clv but we
can certainly look further out
in this table we can see how much we
expect to obtain from each individual
customer over the next 12 months
this is super useful but a little
difficult for most organizations to
employ
so we can go an added step to show that
you can take this model
convert it into a function so that you
can simply write select statement
and use your model as a function passing
in the pre-computed values to then make
your clv estimations as part of a query
for organizations being able to produce
this output and then take it over to
customer relationship management systems
is a great way to start adjusting
engagements with customers to increase
overall engagement and net profitability
over the lifespan of each individual
customer
it also works best on the lake house
ready to get started with customer
lifetime value click on the link in the
description below to go right to our
full write-up for this solution and gain
a deeper understanding of how the
databricks lake house uniquely solves
for the challenges associated with going
from batch to streaming and bi to ai
or visit the databrick solution
accelerator hub to see all our available
accelerators as well as keep up to date
with new launches