Information Management.
Doi:
© 2014 Elsevier Inc.
All rights reserved.http://dx.doi.org/10.1016/B978-0-12-408056-0.00003-5
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.
Revenues. Future revenues are largely based on recent past revenues. With
a few years of data and more sophistication, regression of past revenue forward
serves to determine future revenues.
Length of the Relationship. Retention modeling can be used to understand
leading indicators for customer drop off. Calculating CLV for different estimated
customer lifetimes shows the value of keeping the customer for longer periods;
this shows the potential CLV. Most organizations that do this valuable exercise
are amazed at the potential CLV of their customers and how it grows over time.
The goal becomes keeping the customers with highest CLV as long as
possible and reverse engineering the attributes of those high CLV customers
for use with marketing behavior, thereby increasing overall CLV. Retention
modeling usually accompanies CLV modeling.
Expenses. The major difficulty in computing CLVs is not in computing
customer income. It’s on the expense side of the ledger. It can be difficult to
determine how to allocate company expenses to a particular customer, but it’s
immensely worthwhile.
Choose key characteristics of each customer quartile, determine
unique characteristics of each quartile (age, geography, initial usage), match
new customers to their quartile and assign average projected spend of that
quartile to new customers.
Defining the relevant and various levels of retention and value is an
extension of customer profiling. These are customer profiling variables like
the ones above except they are addressing the need for more immediate
preventative action as opposed to predicting the volume of future profit.
Information Management24
Also, regardless of churn3 potential, the determination of the point at
which customers tend to cross a customer stage in a negative direction is
essential to analytics.
Customer profiling and customer stage modeling should combine
to determine the who and when of customer interaction. Actions are
dependent on the company but could be a personal note, free minutes,
free ad-free time, and/or free community points.
In addition, in markets where customers are likely to utilize multiple
providers for the services a company provides, the company should know
the aspirant level of each customer by determining the 90th percentile of
usage for the customers who share key characteristics of the customer (age
band, geography, demographics, initial usage). This “gap” is an additional
analytic attribute and should be utilized in customer actions.
This is simply a start on analytics, and I’ve focused only on the
customer dimension, but hopefully it is evident that many factors make
true analytics:
● Analytics are formed from summaries of information
● Inclusion of complete, and often large, customer bases
● Continual recalculation of the metrics
● Continual reevaluation of the calculation methods
● Continual reevaluation of the resulting business actions, including
automated actions
● Adding big data to the mix extends the list of attributes and usability
of analytics by a good margin
3 When a customer becomes a former customer through an act of attrition or inactivity, as determined
by the company.
Big Data and Analytics
With the ability to explore previously unrealized correlations between certain
metrics and/or attributes, big data—and the combination of big data and
relational data—greatly increases the effectiveness of analytics. While big data
enhances analytics with additional, albeit very granular, data points, it also
opens up the possibilities for analytics, taking them into the realm of minute
fine-tuning.
If we’re in the business of analytics and analytics are required, it stands to
reason that eventually it’s analytics comprising all data, especially the mammoth
big data, that will create the leading businesses of tomorrow.
You’re in the Business of Analytics 25
PREDICTIVE ANALYTICS
Analytics is a business strategy that must be supported with high
quality, cross-platform-border data, as just discussed. The data is formed in
order to make predictions about the business. We use “Predictive Analytics”
to refer to the class of analytics focused on creating a better future for the
company, from grand process change to individual customer interactions.
If done well, predictive analytics help companies avoid business
situations analogous to being struck by a bus. Business situations, however,
are usually less dramatic and much more nuanced than avoiding a moving
vehicle. And, unlike the bus, a company will often not even know there
was a situation worth avoiding.
Therein lies the fate of many analytics—in order to prove its worth, you
need to build trust in your predictions. Occasionally, I have let predictions
Consider the field of telematics (i.e., automobile systems that combine
global positioning satellite (GPS) tracking and other wireless communications
for various purposes: automatic roadside assistance, remote diagnostics, etc.),
which has serious traction in the auto insurance industry, most famously by
Progressive Insurance. If a consumer opts in by placing a sensor device in their
car, which feeds its data to the insurance company, they can save money on
their insurance. These devices capture fine movements of the car and the car’s
location, both of which decrease the odds to very minimal that the insurance
package would be less than profitable.
Many using big data analytics to personalize products for customers—such
as Netflix, which can recommend movies that model a selection pattern and
Amazon, which offers customized recommendations for purchases based on
buying habits. Another example is an electric company that offers personalized
energy management alerts and recommendations based on smart meters,
enabling customers to be in the appropriate plan for them.
Health insurance companies routinely analyze customer health records,
correlating granular statistics about patient conditions to outcomes. Green
energy systems can increase output with minute adjustments to energy
conversion devices like wind turbines. Social media is mined for customer
preferences and best times, locations and wording for posting to social
networking services. And, of course, for better or worse, governments monitor
citizen activity by tracking communications and movements. Most video,
phone, and internet activity, with many pixels, sound waves, and fine cursor
movement being recorded in subseconds, is big data.
Information Management26
of minor doom pass with a client in order to build trust (“We had 142
churners in Maine this month, just like the model predicted. Now can we
apply the model nationwide and prevent the churn?”).
Predictive analytics are key to the prevention of loss by fraud, churn
and other unwanted outcomes—the equivalents of being hit by the bus.
The Analytics Approach
1. Control data systemically that is detailed, accessible, wide-ranging, and
well-performing
2. Focus on a business problem
3. Choose a modeling technique
4. Build models to translate the data into probable business actions, with
associated probability of action
5. Avoid the undesired future with effective business action
6. Evaluate effectiveness
7. Refine the model
8. Repeat
Companies that do predictive analytics without attaching a probability to events are
seriously impeding the profit potential of predictive analytics.
Predictions should be communicated as a probability distribution. This
turns model output such as “likely to be fraudulent” into “75% likely to be
fraudulent.” This can better correspond to a range of actions appropriate
to the event (fraud) and the percentage (75%).
In terms of “avoiding the undesired future with effective business
action,” these are the next steps that turn all of the data analysis and
preparation and the building of the model into business.
BUILDING PREDICTIVE ANALYTIC MODELS
Predictive analytics are applied in the process of determining
business events that are likely to occur and be actionable. The probability
threshold of “likely” differs from event to event.
Company profiles also come into play. Actions take cycles and a
fast-moving company driving a strong top line is going to care about
something different than an established multinational company with
You’re in the Business of Analytics 27
a large customer base and low margins. The former may only prepare
for relatively low probability events that come with a particularly
bad outcome or try to only enact change that leads to high increased
profitability. The latter companies are more risk averse and will seize every
opportunity to move the needle even slightly.
A predictive modeler might produce a result that indicates a customer
is likely to churn, yet the model might not indicate how likely it is to
happen or whether the company should care about this.
There is a set of company reactions to any likely unwanted business
event or business opportunity. These range from highly invasive actions,
like terminating the credit card, to simply changing a metric about the
customer that may, one day, lead to a more invasive action if compounded.
And, of course, there’s “do nothing.”
Example 1 Customer Lifetime Value
Customer lifetime value is a means to an end. It supports operations as
a data point to justify taking other actions, such as whether to market
to a person/company, how to support the customer, whether to approve
financing, whether to challenge a transaction as fraudulent, etc.
Example 2 Churn Management
When a customer appears likely to churn, companies are increasingly
turning to customer lifetime value and other predictive analytics to
temper the instinct to rush to salvage the relationship. The operative term
is “churn management,” not “churn prevention.”
Regardless, proactive intervention to salvage the relationship, if so
desired, is a multidimensional decision.
High
probability
to churn
Low
probaility
to churn
Low CLV High CLV
No intervention
Low
intervention
High
intervention
Figure 3.1 Decision model with churn management.
Information Management28
Example 3 Clinical Treatment
Caregiving organizations want to provide the best care at the lowest cost.
To reach this balance, multiple procedures for the patient are considered
based on probabilities of efficacy. This efficacy is formed from transaction
patterns and, increasingly, big data read from monitoring.
Example 4 Fraud Detection
Predictive analytics is used to determine the potential fraudulent nature
of a transaction. Here again, we find that analyzing a transaction without
bringing to bear a customer profile built on summarized and recent
transactions can lead to false assumptions and actions. Increasingly, a
customer profile is required input for any model performing fraud detection.
But on a larger and broader scale, another trend is bringing more data
into the predictions, and that includes web-scale data and other big-data
environments. For example, customer usage burdens on support can
contribute to expenses in the CLV calculation.
Example 5 Next Best Offer
Descriptive modeling classifies customers into segments that are utilized
in a large variety of marketing-related activities. These segments should
be formed dynamically in conjunction with campaigns and should
correlate to the various activities of the campaign. Rather than marketing
to everyone determined “likely to purchase,” a “probability to purchase”
should be produced and used with other factors that make the effort
worthwhile to the company in the long run. A factor like the customer’s
income might increase the company’s interest in encouraging the
customer through a smartphone or tablet app alert.
A related use of predictive analytics is in decision modeling, which
might focus on the next customer interaction and whether it should be
proactive and driven by the company (like extending an offer) or reactive
(like responding to a financing application).
Proving the need for multiple dimensions in predictive analytics is like
proving I should not have stepped in front of that bus. It’s sometimes hard
to demonstrate what you have prevented.
ANALYTICS AND INFORMATION ARCHITECTURE
You are going to learn quite a bit in this book about the various
types of data stores that are legitimate for corporate data. As you travel
You’re in the Business of Analytics 29
through the data stores, you may wonder where to place your analytic
data and where to actually do the analytic processing. Analytic data is
increasingly interesting everywhere processing is done. It will be important
to make the analytic data accessible to every data store if the data is not
actually in the data store.
In the chapter on master data management, I will make a case for
Master Data Management (MDM) to be a primary distribution point,
perhaps in addition to being the system that originates some of the
analytic data.
As far as analytic processing goes, the goals of all the processing that
goes on throughout the enterprise is only enhanced with analytic data.
Many enterprises already acknowledge that they “compete on analytics.”
Many more will join them.
Analytics are used to assess current markets and new markets to enter.
They are used in determining how to treat customers and prospects, in
very detailed ways. They are used for bundling and pricing products and
services and marketing products and services. And clearly they are used to
protect a company’s downside, like fraud, theft, and claims.
You may have a workable supply chain that gets a product to the store
(or whatever passes for a store in your business), have low prices, and
tactically everything may seem to “work.” That is not enough today. Today,
the supply chain must be very efficient, prices should be set based on a firm
grounding in analysis, and customers must be known at an intimate level.
The use of analytics comprises the major area of competitive focus for
organizations in the foreseeable future.
ANALYTICS REQUIRES ANALYSTS
A host of articles and books have promoted the idea that nearly
full company automation is possible, supported by analytics, which are
also automated. This automation rivals the intellectual properties of
the business analyst who currently translates these analytics into the
achievement of company goals. These systems will purportedly exhibit
behaviors that could be called intelligent behavior.
The question is not whether or not information systems supported by
analytics reason. Of course, they will not. Nor is the question whether or
not systems will be able to create the illusory effect of reasoning. They will
also clearly get better at it. Information management displays apparently
intelligent behavior when it automatically alters in-process promotions to
Information Management30
be rerouted to prospect profiles that are responding to the initial mailing.
When information management uses analytics to reroute procedures to
best-of-breed providers, it displays intelligence. Additionally, when it uses
analytics to automatically change pricing in response to demand, it displays
intelligence.
However, good engineering cannot yet take the place of the skills and
experience of the business analyst. The essence of human reason is the
aptitude to resolutely manipulate the meaning of the inputs encountered
to create perceptibly favorable situations and arrive at a basic cognitive
orientation. The development of this ability within the experienced
business analyst makes him or her more adaptable to the business
environment. This is what business analysts do—they reason. Analytics don’t.
Determination of the best fit of data for broad organizational needs
is another multidimensional reasoning function many business analysts
provide to an organization. Business and data requirements are seldom able
to be completely coded. Requirements misfire frequently.
At a minimum, and where many programs are today, business
intelligence simply provides access to corporate data more efficiently
and occasionally does some automated cleaning of that data. While an
analyst’s role in manually accumulating disparate corporate data can be
diminished with good information management, the higher value-added
role of reasoning cannot be. There are, clearly, non-analytical, operational
functions being served up to automation, such as industrial manufacturing.
Computers are better at fast calculations than analysts, but that’s not
reasoning. There is no scalability from syntactical computation to the input
manipulation, abstraction, and perception—the functions that comprise
reasoning—based on continued innovation in information management
and advances in computational power alone.
The chances of successful analytics efforts significantly correlate with
having people with the right characteristics for success on the project. This
success goes far beyond technical skills. Analytics work best when they
foster organizational communication.
Putting together the data, the processes, and the people around analytics
has created the most successful businesses in the world. Welcome to the
business of analytics.
You’re in the Business of Analytics 31
ACTION PLAN
● Survey your use of information—are your users using only base
information or is it pre-summarized and enhanced to draw real,
actionable meaning out of it prior to user interface
● Ensure analytics are a part of corporate strategy
● Enlist the support of business representation in determining the
analytic calculations and predictive models
● Determine the analytic data that will be useful to your business
strategy
● Understand the calculations on base information necessary to bring
the data to full utility
● Understand where the base information resides today and if it is in
a leverageable place
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