MODERN BUSINESS
these graduates?
Each answer fills out the story, either
making it stronger or triggering more
questions. A good analyst uses the
data along with their imagination,
curiosity and experience to conjure
new scenarios and see if the data rules
them in or out, or they use the data to
prompt new possible stories.
Business storytelling specialist Paul
Smith, author of Sell with a Story,9 was
once a market analyst for Procter &
Gamble. The Pampers business called
him in to get the data together and run
a strategy session for them. Now Paul
was familiar with the dominant story in
this business: if you want to generate
more profit, you need to increase
volume of product. (Figure 4)
The data did show this strong
correlation, but only up to 1984. After
that, there was a marked change in
the pattern Smith was seeing, with
no discernible link between profit
and volume. He had to start testing a
range of alternative stories to explain
what had happened. Had the change
occurred when competitor KimberlyClarke launched Huggies?
Figure 4
Was it when commodity costs got out
of control? Paul chased down each
hypothesis and in the end discovered
that it was when the market reached
full penetration. (Figure 5)
Paul tells the story this way:
“Before we launched disposable diapers
in the early ’60s, everyone used cloth
diapers. But it’s not like once disposable
diapers came out, everybody switched
from cloth immediately. It took years
for that to happen. In fact, it turns out it
took exactly 21 years.”
“By 1983, the market for disposable
diapers had essentially reached 100
percent of households with kids who
wore diapers, and cloth diapers had
almost entirely vanished from the
marketplace. Up to that point, everyone
making disposable diapers had rapidly
growing sales numbers, and the rapidly
growing profit numbers to go with them.
The cloth diaper makers, of course,
were going out of business.”
“What that means is that the disposable
diaper business in the United States
went from a ‘developing market’ to a
‘mature market’ in 1983. And apparently,
we (Procter & Gamble) failed to notice
it. We’re still following the same basic
‘sell more’ strategy we’ve been using
during the developing market period.”10
Analysis is a battle of stories in a very
Darwinian fashion. The one with the
best fit with the data wins. It’s the job
of the analyst to explore the many
possible stories that might explain
what they are seeing.
The connection between cause and
effect, how-ever, doesn’t have to get
down to root causes. Marketers have
discovered that if you can uncover a
reliable correlation, then you can make
decisions
– I’m sure this sends the scientists
nuts, but for a business it can be a
practical approach. For example,
large retailers collect masses of
data around loyalty cards, such as
purchases, dates, times, geographies,
shopper demographics and so on. An
analyst can explore this data for strong
correlations, and once they are found,
predictions can be drawn.
The following scenario, told by Charles
Duhigg in The New York Times,11 is
from the retailer Target. A woman aged
Figure 5
October 2016
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