Accenture Interactive – Point of View Series
Winning The Lotto
A fresh look at customer segmentation
Winning The Lotto
A fresh look at customer segmentation
No one doubts the value of good
customer segmentation. The
insights it provides can identify
market opportunities, improve
the targeting and relevancy of
communications, inform product
and service development, and also
guide overall investment strategies
across the customer portfolio. With
digital technologies fueling big
data, and tech-savvy consumers
opting out of mass-marketing
campaigns, segmentation
strategies need to be more robust
and compelling than ever.
In today’s increasingly fragmented
marketplace, being able to identify new
market opportunities and capitalize on them,
through personalization and relevance—
both in terms of needs and timeliness—are
paramount. Customer segmentation is
recognized as an essential capability for
a business that wants to deliver tailored
offerings and communications that generate
and meet consumer demand. Why then, are
so many companies struggling to achieve
the value that segmentation promises?
Why are segmentation strategies rarely
embraced enterprise-wide or able to drive
more impactful initiatives? And how can
organizations gain deeper insights into the
marketplace and customers to ensure that
their marketing initiatives will deliver the
anticipated return on investment (ROI)?
The lotto effect
First, consider a complex “wrinkle” in customer
segmentation which often goes unnoticed.
Take a typical customer segmentation effort
today. A company wants to identify customer
segments with significant growth potential
for a particular product. It has captured 50
customer attributes such as age, gender,
location, account tenure and other customer
level information. From this relatively small
set of attributes, the company wants to
identify the “best” six attributes to base the
segmentation upon—a surprisingly complex
endeavor. Selecting the best six out of fifty is
akin to playing a lottery—50 numbers… pick
six… with more than 15 million ways to lose.
With the advent of big data creating many
more sources and attributes to consider, the
challenge only grows. For example, doubling
the size of that same dataset doesn’t just
double the possibilities; in fact, it causes the
possible options to grow from 15 million
to over a billion, a growth of over 6000
percent. Traditional analytic methods such as
clustering require the person developing the
segmentation, who is often a statistician, to
determine the best set of attributes facing
these odds. It’s no wonder that segmentation
based on traditional approaches often lose
their relevance within six months.
In addition, there is also the issue of what
does “best” mean? Paradoxically, the lotto
effect, while seeming like an added obstacle,
in fact provides the opportunity to search
for the optimal attributes based on business
objectives. Imagine a more advanced
approach to customer segmentation
development— one that handles infinite
numbers of attributes, and factors in the
business objectives at the start of a project
to ensure that the resulting solution is
aligned to those objectives. Leveraging
artificial intelligence technology, next
generation customer segmentation is
now a reality.
Customer attributes from
to
Find the ‘best’
From
million
to over
billion
possible options
Up to
6000%
growth
A new way to play the game
3. W
here to find them?
In order for the solution to be actionable,
the segmentation must be developed
so that it can be applied to a customer
database or target customers in a
mass-marketing campaign.
Artificial intelligence technologies can be
adapted specifically to address these issues.
By using advanced data mining techniques
these technologies are able to find optimal
solutions based on business objectives,
insight quality, and actionability. This
provides a unique approach that bridges
advanced analytics to business goals, while
in turn enabling strategies that drive
significant market impact.
M
AN
UA
AN
NS
Segmentation
IO
AT
Analytics
U
L
NS
A
EV
EV
VS
TIF
TIF
AL
IC I
IC
IAL
INTEL L IGEN CE OP
INTEL L IGENCE
TI M
OPT
IM
One dimension
Segmentation Focus
Outcome
One dimension
Segmentation Focus
IO
AT
U
AL
Objectives selected
by the Business
Objectives selected
by the Business
Outcome
Segmentation UA
L
Analytics
Attributes selected
by an analyst
Attributes selected
by an analyst
Next Generation
Approach
L
IZ
IZ
ER
2. W
hat to offer and what to say?
Each segment needs a rich, unique, and
comprehensive profile to inform product,
offer and messaging strategies.
M
ER
1. W
ho to focus on?
The resulting segments must be highly
differentiated across core value
dimensions such as revenue, cost,
profitability, tenure and other aspects
to guide investment strategies.
Traditional Methods
AR
One of the key values of leveraging artificial
intelligence technology for segmentation
development is the flexibility it provides. This
is critical because the segmentation strategy
needs to address three fundamental issues
while simultaneously balancing customer
dimensions and business objectives.
Figure 1: Segmentation: Traditional versus Next Generation Approach
AR
Too often, segmentation efforts result
in a solution that is too focused on a single
dimension of customer information, such
as attitudes, value, or behaviors. Advanced
segmentation, however, should have a
balance across all the various customer
dimensions, while also being actionable.
Most importantly, customer segmentation
needs to reflect business goals (see Figure 1).
Segmentation
Analytics
Segmentation
Analytics
Even with the best intentions, objectives
often evolve as segmentations are developed,
for example, when additional clarity is gained
during the review of a preliminary solution.
Instead of manually “tweaking” the
segmentation strategy through traditional
methods, artificial intelligence technologies
can be fine-tuned to ensure the resulting
solution is optimal for the new set of
objectives. The end result is a solution that is
both driven by the business objectives and
empowered by analytics.
Outcome
Optimized Segmentation
with 360° View
Outcome
Optimized Segmentation
with 360° View
To understand the full potential of artificial
intelligence technologies, consider the
following example using Accenture’s
proprietary solution, OptiCluster. Taking a
highly-rated previous customer
segmentation project carried out using
traditional methods for a large North
American retailer, OptiCluster was used to
determine how the results would differ. The
solution was to provide customer insights
and guide merchandising strategies. In all,
there were 240 attributes, including
Equally important is the fact that the
identified individual customer segments
have rich holistic, yet unique profiles.
Gaining this level of insight into customer
segments is critical to developing offerings
and messaging strategies. In the optimal
solution from OptiCluster, every profile
dimension showed significant improvement
in clarity. In fact, when looking across all
profiling attributes, the indices from the
customer segmentation that OptiCluster
found had increased by more than 80
percent. The more rigorous a segmentation
approach, the stronger the foundation is
for a more personalized brand experience,
as it allows marketers to make their
offerings and messaging highly relevant
to the target audience.
Original Results
120
110
Average Cost
As illustrated in Figure 2, OptiCluster helped
in understanding “who to focus on” by
differentiating segments across the value
dimensions of revenue and cost. In the
client’s original solution, only one fairly
small segment showed any significant
difference from the other segments. By
contrast, OptiCluster found a solution
with significant differentiation across the
segments, including much larger superior
and poor performing segments. As a result,
the client would be able to fine tune its
marketing and merchandising strategies
through better profiling, targeting,
messaging, and offerings development.
Figure 2: Impact of OptiCluster on Segmentation Results
100
90
80
70
60
Segment Size
50
125
150
175
200
225
250
275
300
Average Revenue
OptiCluster Results
120
110
Average Cost
shopping behaviors, customer
demographics, and profitability metrics.
However, the goal was to find a measurably
better solution by simply using two types
of attributes: customer demographics
and shopping preferences. This avoided
any bias that profitability metrics can
impart on a solution.
100
90
80
70
60
Segment Size
50
125
150
175
200
225
Average Revenue
250
275
300
Hitting the jackpot
While traditional analytic approaches
generally focus on the question of what
to offer and the key messages to use,
the limited dimensional segmentations of
demographics, attitudes, or behaviors, end
up sacrificing the question of who to focus
on. The absence of a value dimension such
as profitability, revenue, and Net Promoter
Score (NPS), makes it very difficult to
develop an investment strategy for each
customer segment. It also makes it nearly
impossible for marketing to win support
for the segmentation strategy across the
enterprise. While other business units
may find segment profiles of interest (for
instance, what are Gen Y’s attitudes
towards our brand?), this isn’t sufficient
to change behaviors. However, if the
business sees a substantial financial impact
for addressing the needs of a customer
segment, then the segmentation can
provide the necessary leverage to drive
alignment and mobilization to address their
needs (such as set up a web chat customer
service capability). The results are
potentially huge.
Case Study 1
Case Study 2
One electronics retailer was able to
identity a half billion dollar loss it had
incurred in a single year from a segment
that was not identified using traditional
segmentation methods. In the absence
of a value dimension within its previous
solution, the company had decided to
invest equally across the customer base.
This significantly eroded its higher value
customers. Once an advanced customer
segmentation analysis was completed,
the company was able to develop an
effective strategy to address the issue.
As a result, the company was able to
not only take action to recover from the
loss, but also better manage customers
going forward. By making necessary
operational changes to call center
dashboards, interactive voice response
services (IVR) and performance metrics,
the company provided personalized
services aligned to customer segments
and their associated value.
In another case, the optimized
segmentation for a financial service
provider revealed strategic information
about two key segments: while a unique
segment—only five percent—of its
customer base accounted for 50 percent
of the company’s profits, another
segment was eating into 10 percent of
company profits. With the additional
insights provided by a segmentation
that balanced value, demographics,
preferences, and behaviors, the company
was able to refine its marketing
messages to the target audiences. In
addition, the company was also able
to fine-tune its customer acquisition
strategies to improve its longer term
customer portfolio. The result? A massive
600 percent ROI by the second year.
This approach to segmentation proved
so useful that the company executives
made it an integral part of their strategic
planning in subsequent years.
Beating the odds
Segmentation strategies will vary for each
company depending on their brand, market,
and priorities. By leveraging artificial
intelligence technologies, analytics can now
be aligned with unique business objectives
to determine the optimal segmentation.
Through the more precise narrowing of the
“best” attributes, segment profiles will be
both richer and more comprehensive. And
marketing strategies based on next generation
customer segmentation will be more effective
in personalizing and targeting offerings and
messaging to relevant customers.
With the advent of big data and more
advanced analytics, customer segmentation
techniques based on traditional approaches
will fast become obsolete in today’s multidimensional world. Not only do companies risk
wasting their investment on the segmentation
effort, but more importantly they risk being
misled on where value exists in the market
and among their customers.
To learn more about next
generation customer segmentation
strategies, contact:
Jeriad Zoghby
[email protected]
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