Insight-driven sales transformation
By embedding consumer insights into their merchandising processes, retailers can
boost both like-for-like sales and profitability while creating smarter merchants.
Over the past five years, traditional large
retailers—such as supermarket chains,
drugstores, and big-box specialty retailers—
have found growth elusive. In most major
markets they are facing intensified
competition, particularly from discounters, as
recession-era shopping habits have become
entrenched. Opening new stores is no longer a
surefire way to grow, in light of market
saturation and the boom in e-commerce (see
“Making stores matter in a multichannel world,”
page 4). Same-store sales growth, or “like for
like” growth, has been flat or declining for
most large players across all major Europe
Amid this punishing environment, how have a
handful of retailers outperformed the
competition and achieved substantial like-forlike
sales growth? In our experience, they have
succeeded primarily by developing a deeper
understanding of consumer and shopper
behavior and embedding these insights into
the way they manage every product category.
In other words, they have implemented an
insight-driven sales transformation.
In this article, we describe an approach that has
helped leading retailers kick-start such a
transformation. We call it the “category
accelerator”: it is simultaneously a thorough,
data-driven category-planning process and
an intensive capability-building program for
category managers. Retailers in the grocery,
drug, and do-it-yourself sectors that have used
the approach have achieved a sales uplift of
3 to 5 percent and a net margin improvement of
one to four percentage points in 6 to 18 months.
Three steps to transformation
As they seek to increase like-for-like sales,
retailers encounter a number of common
challenges. One is wide variability in
performance and execution among product
categories, in part because each category
manager does his or her job independently of
and differently from others. They use different
tools and techniques, and some rely on data and
insights more than others. Another common
challenge is a lack of coordination of
improvement initiatives; pricing actions, for
example, are often disconnected from visualmerchandising
changes. In such cases, retailers
miss out on capturing the full potential of an
integrated category-wide (not to mention
store-wide) transformation.
The category accelerator addresses all these
problems in a systematic, sustainable fashion. In a
nutshell, it is a program for creating insight-driven
category plans for all of a retailer’s product
categories, using a standardized process supported
by a dedicated team of experts. The three main
steps of the approach involve building the core
team, creating best-practice content, and
developing insight-driven category plans.
Set up a cross-functional team of ‘navigators’
and analysts
The first step is to establish a cross-functional
core team focused on delivering quick wins. The
team should combine category-management
expertise (in the form of high-profile,
experienced merchants) and analytics expertise
(data analysts, often hired through targeted
external-recruitment efforts). Retail leaders may
initially balk at the idea of pulling top merchants
from their day-to-day tasks, but it is an essential
sacrifice for both perception and impact.
The team, which initially will have approximately
four to eight members, should be
situated in a dedicated space—an environment
designed to encourage new thinking, foster
creativity, and facilitate rapid implementation.
Having a separate room for the team may seem
trivial, but it is a fundamental success factor.
It helps the team get away from a business-asusual
mind-set.
The merchants play the role of navigators
who coach and challenge category managers
throughout the process, while the analysts are
responsible for mining transaction and loyaltycard
data and translating those data into useful
insights for category managers (see sidebar,
“A sampling of opportunities in big data”).
This arrangement sidesteps a common pitfall
of sales transformations: having an analytics
team that works in isolation from the
commercial team and thus generates unusable
or irrelevant insights. Instead, the analysts
work with category managers to make sure
that decision-support tools are intuitive and
accepted by end users, and that the insights
are accessible to everyone who needs them—
not just to a select group of “superusers.”
Retailers should resist the temptation to
incorporate the team back into the business.
Once it has built buy-in and momentum
42 Perspectives on retail and consumer goods Summer 2014
through quick wins, the team should broaden
its focus, bring in more navigators and
analysts, and become a permanent unit. For a
large grocery retailer, this core team would
typically consist of 10 to 20 people, split evenly
between navigators and analysts.
Create a comprehensive series of modules
Among the core team’s initial responsibilities
is to develop a series of modules, covering all
commercial levers, to serve as the main
content for sessions with category managers
(Exhibit 1). The integration of levers—in
contrast to the typical siloed approach
whereby each initiative is managed
independently of others—is part of what makes
the category accelerator a powerful force.
Each module should contain standardized,
best-in-class tools and methods that will help
category managers perform consistently highquality
analyses of commercial decisions,
manuals that explain how to use the tools, and
sample outputs and templates. The materials
Exhibit 1
Retail and Consumer Perspectives 2014
Sales Transformation
Exhibit 1 of 2
The modules of the category accelerator cover all commercial levers.
Objective: Check relevance of
store clusters and provide
guidelines for creating
cluster-specific assortments
Objective: Understand the
category’s performance across
all channels and identify main
areas for improvement
Objective: Set pricing and
promotional action plan to
optimize customer value
perception and profit
Objective: Optimize inventory by
setting up-front targets, evaluating
performance, and building action
plans (eg, exit strategy)
Objective: Set a COGS goal and
create action plan to improve
negotiations with key vendors in
the short and medium term
Objective: Create customercentric
planograms for each
store cluster
Objective: Develop an integrated category plan, including an estimate of sales and margin impact and action
plans with critical milestones/owners
Module 1: Customer-decision tree Module 2: Clustering Module 3: Category overview
Objective: Develop action plan
for own brands, including
new-product requirements and
margin improvement
Module 5: Value Module 6: Own brands
Objective: Refine assortment using
customer-decision trees, category
performance, and competitive
insights to highlight potential SKU
additions and deletions
Module 4: Assortment health check
Module 7: Inventory management Module 8: COGS1 Module 9: Merchandising/operations
Objective: Build customerdecision
tree, which will
guide category analysis
Integrated category plan
1Cost of goods sold.
Insight-