No gift in last 12 months
53.5% quantity
22.5% cash
Gift in last 12 months
10.5% quantity
12.5% cash
£1–49 value
26.0% quantity
33.0% cash
£50+ value
10.0% quantity
32% cash
One gift
64% quantity
35% cash
Two or more
36% quantity
65% cash
ALL DONORS MAILED
100% mailing quantity
100% cash received
Number of prior gifts
Last gift in same season
13.5% quantity
11.0% cash
Last gift in other season
40.0% quantity
11.5% cash
Worst NTs*
8.5% quantity
24.0% cash
Best NTs*
1.5% quantity
8.0% cash
Worst regions
1.0% quantity
2.5% cash
Best regions
0.5% quantity
5.5% cash
*NT: geo-demographic neighbourhood type
570 The Marketing Book
cent would end up in 20 per cent of pockets how-
ever society attempted to regulate matters. To
Pareto, whether all men are created equal or not,
they certainly don’t end up that way.
So it is with customers. Every direct
marketer knows that some customers are much
more valuable than others. Every astute direct
marketer knows who the valuable ones are. The
really smart direct marketer has a system for
forecasting who the valuable ones are going
to be.
Why is this so important? Let’s consider
two examples.
Customers who cost money
Typically, 75 per cent of new customers gained
by a home shopping business will have lapsed
without providing enough business to recover
the cost of recruiting them. All of the profit will
be contributed by the remaining 25 per cent.
If the company learns which are the best
sources of good customers, it can work to reduce
the 75 per cent of loss-making intake. If it fails to
learn, the 75 per cent will become 80 or 85 per
cent, ensuring that the company loses money.
Again, typically, a bank will lose money on
at least 80 per cent of its private customer base
at any one time. By devoting special attention
to the remaining 20 per cent, it can expect to
satisfy more of them and so keep their custom.
If it fails to differentiate between its good (and
potentially good) customers and its loss-mak-
ing customers, it is the good customers who are
most likely to defect.
Figure 22.1 shows a real life example of
segmentation of charity donors based on their
response to the last appeal made to them.
Figure 22.1 Analysis of postal donors to charity