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Information Management.
Doi
I am now going to present a caveat to the premise of Chapter 1—that you
are in the business of information. While undoubtedly that is true, it is a
form of information that is prominent enough to replace information in
the mantra, and that form is analytics.
Basic information operates the business and it is available in abundance
to accumulate, publish, and be available from the myriad of data stores I
will discuss in this book. Basic information provides rearview-mirror
reporting and some nearsighted ability to look forward and get ahead the
next few feet.
When it comes to seeing the business landscape to make process
change or to strive for maximum profitability derived from customer- and
product-specific catering, you need forward-facing data. You need to
know a future that you can intervene in and change. You need to know
the future that will not actually happen because you’re intervening and
turning it in a more profitable direction.
The lack of precise forecasting, caused by constant change, may
leave the analytics process in doubt. Yet, you must build up trust in the
analytic process through trust in the quality data, the right models, and the
application of those models in the business.
WHAT DISTINGUISHES ANALYTICS?
Many approach analytics as a set of value propositions to the
company. However, from a data use perspective, the definition of analytic
data relates to how it is formed. It is formed from more complex uses
of information than reporting. Analytic data is formed from summarized
data providing information that is used in an analytic process and yielding
insightful information to be used in decision making.
Addressing the propensity of a customer to make a purchase, for
example, requires an in-depth look at the spending profile—perhaps by
time slice, geography, and other dimensions. It requires a look at those
You’re in the Business of Analytics
CHAPTER THREE
Information Management22
with similar demographics and how they responded. It requires a look at
ad effectiveness. And it may require a recursive look at all of these and
more. Analytics should also be tied to business action. A business should
have actions to take as a result of analytics—for example, customer-touch
or customer-reach programs.
There are numerous categories that fit this perspective of analytics.
Customer profiling, even for B2B customers, is an essential starting point
for analytics.
Companies need to understand their “whales” (most valued customers)
and how much they are worth comparatively. Companies need a sense of the
stages or states a customer goes through with them and the impact on revenue
when a customer changes stages. Customer profiling sets up companies for
greatly improved targeted marketing and deeper customer analytics.
This form of analytics starts by segmenting the customer base according
to personal preferences, usage behavior, customer stage, characteristics, and
economic value to the enterprise. Economic value typically includes last
quarter, last year-to-date, lifetime-to-date, and projected lifetime values.
Profit is the best metric in the long run to use in the calculations.
However, spend (shown in the bullets below) will work, too. More simple
calculations that are simply “uses,” like purchases, of the company’s product
will provide far less reliable results.
The key metrics to use should have financial linkage that maps directly
to the return on investment (ROI) of the company. Where possible,
analyze customer history for the following econometric attributes at a
minimum:
● Lifetime spend and percentile rank to date (This is a high-priority
item.)
● Last year spend and percentile rank (This is a high-priority item.)
● Last year-to-date spend and percentile rank
● Last quarter spend and percentile rank
● Annual spend pattern by market season and percentile rank
● Frequency of purchase patterns across product categories
● Using commercial demographics (Polk, Mediamark or equivalent),
match the customers to characteristic demographics at the block
group1 levels
● If applicable, social rank within the customer community
● If applicable, social group(s) within the customer community
1 Subset of a city; a geographic unit used by the United States Census Bureau.
You’re in the Business of Analytics 23
These calculations provide the basis for customer lifetime value and
assorted customer ranking. The next step is to determine the attributes
for projected future spend. This is done by assigning customers a lifetime
spend. Lifetime spend is based on (a) n-year performance linear regression
or (b) n-year performance of their assigned quartile,2 if less than n years of
history is available.
2 A quartile is 25% of the customer base. You could do more divisions (quintile) or fewer (decile).
The point is a few, manageable profiles.
Customer Lifetime Value: The Prima Facie Analytic
CLV = Present Value (future profits (revenues minus expenses) from customer in
n years)
There are three major components to the formula: revenues, length of the
relationship (n), and expenses.
