Adding Customer LifeCycles to the Mix
(a.k.a. CLM / Customer LifeCycle Management and CRP / Customer
Relationship Planning, if you didn't know. This is getting silly.)
Jim's Note: This is your second model for comparing the potential
value of customers. You are building on
the first model by adding the element of time,
You may have noted in the beginning of the previous example I specified
the ads you were comparing should have "run
at the same time, for the same duration." Do you know why?
Customer LifeCycles. It's not fair to compare the customer Recency
percentage of an ad that ran 90 days ago with an ad that ran 30 days ago,
because customers tend to leave you over time. You learned this in
the previous model. If one ad has more
time to "lose customers," then comparing them would be unfair
or biased by the element of time.
This tendency of customers to leave over time has different names
depending on the business model - some call it attrition (credit
cards), it is an element in churn (cable, long distance, wireless),
and in retailing and database marketing it is often called defection.
You can compare ads having different start points, as long as
they're not so far apart that seasonality comes into play (comparing ads
that ran in July with those that ran during November, for example).
This is done by matching up the LifeCycles - a Recency analysis at the
same point in the cycle.
If you want to compare the potential value of an ad running 30 days
ago with one running 90 days ago, you have to look at the Recency of the
90 day ago ad 30 days after it ran to take out the LifeCycle effects. Using
the previous example, if Ad #2 ran 90 days
ago, you would want to find out what percentage of people who clicked on
Ad #2 took action 30 days after it first ran. If you always
run your analysis referencing the start date of an ad (or any other
variable you are measuring), and measure for equal time periods after the
start date, you will eliminate most of the LifeCycle effect and can
compare the results fairly.
So what about these LifeCycles, is there a way this information can be
tracked and used? Well sure; using LifeCycles can solve the little
problem we left at the end of the previous tutorial page. Thinking about the
and #2 example, what if you repeated the
Recency query for each ad (made at least one purchase in the past 30 days)
every 30 days for 6 months? What would you get?
You would have a series of measurements looking at the potential value of
the customers generated by the ads over time. You would be able to
chart the defection patterns of Ad #1 and Ad #2 customers.
Why is this important? Because if you want to get at the true
value of the customers generated by the ads, you have to measure their
value over the LifeCycle. You might be surprised. Take a look
at the chart below from our
Top to bottom on the left side of the chart is the percentage of
customers making a purchase in the past 30 days; left to right at the
bottom of the chart are the months each Recency analysis was performed
since the start date of the ad campaigns. Both the Ad #1 (blue) and
Ad #2 (red) lines start at the percentages we came up with in the Recency
of purchases analysis on the
Recency Percentage Over Time
Ad #1 Starts with Higher Recency,
but has Shorter Lifecycle than Ad #2
If you look at the chart above, you can see that Ad #1 (blue line)
starts at 20% of customers having purchased in the past 30 days, and in 6
months, falls to less than 5%. Ad #1 also seems to be headed even
lower in Recency; these customers are losing even more potential value as
time goes on.
Ad #2 (red line) starts at 15% of customers having
purchased in the past 30 days and falls into month 3, but then starts
rising in later months, ending up higher than it started, and is still
rising. Customers from Ad #2 might end up having greater potential value than customers from Ad #1 over the longer term.
So it could be
that, after looking at the LifeCycle of customers from Ads #1 and #2, you
may find even though Ad #1 looks best based on Recency at a point in
time, Ad #2 creates higher potential value customers when Recency is
looked at over time.
In looking at the
LifeCycles, we have perhaps come up with a clue to the behavior we saw in
the Recency of visits analysis on
the previous page. It would appear that the customers from Ad #2 may
take a little longer to make a purchasing decision, but become more
valuable customers over the long run. This is a very common
occurrence in customer behavior and if you are not tracking it, you won't
know it is happening, leading to poor decisions about the profitability of
your ad campaigns.
This is a picture of the customer LifeCycle at work, and you can
conduct this type of study with ads, products, areas of the site, survey
data, demographics - any type of customer information you can get
Recency data for.
Next in Tutorial:
Who Needs LifeTime Value?
advanced version of this model
Maximizing the profitability of promotions using LifeCycles is
detailed in the Drilling