Consumers may be duking it out for Nintendo Wiis in
shopping aisles this year, but overall the battle for holiday
spending dollars will be between the retailers. With
consumers’ awareness of a softening economy, retailers
will be forced to into stiff competition for consumer dollars,
according to the National Retail Federation.
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Sahir Anand, an analyst at the research consultancy
Aberdeen Group, says this competitive
landscape makes business intelligence critical. It
represents the work of turning the massive customer and
transaction data warehouses into useful guidance on
everything from how to attract and deliver a great customer
experience to what merchandise to stock and in what
Anand talked to Associate Online Editor Diann Daniel
about what retailers should be doing with their business
intelligence tools for this essential business season.
CIO: First off, do you think business intelligence
is a must for today’s retail companies?
Sahir Anand: I think if you want to improve
customer retention and sales, in all channels—store, Web,
catalogue—at some stage you’ll have to adopt business
intelligence tools because the transactions and data are so
complex nowadays. Without business intelligence tools, it’s
going to be a lot of gut-feel decisions in terms of how you
plan your merchandising strategy to fulfill customer demand.
And that just doesn’t work well.
Take the example of the holiday season. Business
intelligence is important for any seasonal sales, but it’s
crucial for the high traffic, high-volume Christmas holiday
season. Business intelligence combines data and shows patterns
and trends to give customer insight, which can then be used to
guide sales, marketing and other key areas. Most importantly,
BI delivery tools provide the reports and dashboards that are
required for retail performance management.
The important thing for any retail segment, especially for
the specialty retail segments as they drive a bulk of the
holiday sales, is to systematically identify the products your
consumers want, and especially to identify new products they
may want and make sure that you deliver those new products. In
order to make sure you have no gaps between what you offer and
what consumers want, you need to identify and forecast trends,
and look at the demand pattern over time. Business intelligence
integrates the complicated customer, location and product
information and enables you to see what patterns are
This more effectively helps you plan for the holiday
Using business intelligence to establish a merchandise
strategy must begin well in advance, at least a year. You need
to start planning the best merchandise fit for customers during
the key season by looking at comparative trends, internal
transaction data and syndicated data, such as consumer
demographics from companies such as Nielsen.
Avoiding Overstock Sales Starts at the Supply Chain
CIO: Last year, many stores ended the
holiday season with leftover stock and were forced to mark down
a lot of merchandise. Why is getting that merchandising
strategy right so difficult?
Anand: Retailers tend to focus on the front
end at the expense of the back end. They effectively plan for
how their stores, website and catalogue are going to look. But
the back-end that involves reviewing and analyzing customer
data for taking effective merchandising decisions is always an
issue. It’s a race against time in the case of several
retailers as their data intelligence strategy is not in place
or the response to BI is too slow (non-real-time).
Back-end focus is the key towards a successful sales season.
Retailers must focus on the entire retail supply
chain—product sourcing, procurement, the order-to-time
cycle. They must identify which products will actually sell,
and create an entire lifecycle management strategy right from
product development to delivery nine to 12 months before a key
selling season. And business intelligence plays a huge role in
it, because it identifies the problem spots for the company in
that entire lifecycle management. It also identifies
opportunities to improve, in terms of the sourcing of the
products, and actually mapping that sourcing with consumer
Problem: Dirty, Distributed Data
CIO: What is the current state of the retail
industry’s use of business intelligence and what are some
of the challenges?
Anand: Aberdeen research shows that 71
percent of retail companies are using business intelligence,
primarily to manage customers, plan business strategy, and for
merchandising. Still, only about half of companies are using BI
for merchandising, so I think that trend has to improve. The
enormous volumes that retailers have to plan and execute can be
The main challenge that companies face using business
intelligence is that their data is not clean enough for
analysis. Forty-nine percent of our survey respondents listed
dirty data as a top challenge when initiating or implementing
business intelligence or predictive analytics. Another
associated challenge is that the data is scattered throughout
the organization; it’s not centralized. Coming up with good
sales analysis, space planning analysis, assortment planning
analysis, market basket analysis—all of which can help a
company create a good merchandise stack for holiday
season—requires integrated data. This is isn’t possible
a centralized data strategy, and it isn’t valuable if
the data is dirty.
Data issues are a problem for a number of companies
throughout manufacturing, hospitality, and the other service
industries. It’s not just retail companies that have this
issue: Data at many companies is decentralized, it’s fragmented
in different locations.
CIO: How can organizations fix this
Anand: First off, the mandate has to come
from the top-level management, it must have top-level executive
support. Top-level executives in companies that are doing
business intelligence well—Walmart, Best Buy, Staples,
Target and other top retailers—make clean data a
Once a data centralization strategy is in place,
organizations can start with data cleansing projects to improve
the data quality. It’s a slow process but it’s a crucial one.
So for example, you eradicate duplicate data for location,
products and customer data such as having one person’s name in
the system in different ways, such as Robert C. Parker and Bob
Customer information duplication is a especially a problem
since the consumer is interacting with different faces of the
organization—Web, store and so on—so information in
captured in different ways.
The other thing is to limit the fields that you typically
look at when you’re analyzing customer data and product
location data. There are so many fields that organizations
have. Some of them are totally redundant. So you should have
some core fields where you have the basic demographics and then
you have customer transaction size and market basket size. You
can then apply algorithms for cross-relational data analysis
and do constructive business intelligence.
Take Best Buy, which does business intelligence very well.
They used to have immense disparity of sources and fragmented
reports. There was no centralized data repository for them, no
clear path for clean information to be correlated for analysis
and ad hoc reporting of department or business process
performance were the norm. But they cleaned their data,
integrated those disparate data sources, and built an excellent
national scorecard that measures enterprise performance across
different retail processes. They’ve seen effective results come
out of it.
And the scorecard is just one example of successful BI usage
by Best Buy. Another area where they’ve succeeded is looking at
the marketing and overall company intelligence to guide how and
what to sell to the various segments of their customer
CIO: Much of business intelligence looks at
historic data. How do you predict the new items customers will
Anand: Forecasting the new items your
customer will want is crucial for most retailers, especially
for the holiday season.
The key is looking at the research on key new product adoption trends, and using predictive analytics to determine just what your customers will want. I can’t overstress how important this is. When the iPod wave started, the companies that embraced it immediately saw success and had continued success because the customer got accustomed to seeing that particular merchandise at that particular store.
Companies that did not embrace it were the followers and they
lost out. If those followers had looked at the
competitive landscape and identified that in advance, they
would’ve been able to fulfill customer need. Now customers will
simply go elsewhere.