Intelligence: Into the mind of the customer
Data warehouses help marketing managers and planners study customer
patterns, their buying trends and behaviours, and provide a tremendous
amount of business intelligence, writes MOHAN BABU
Information technology has aided marketers in planning their product
strategies for decades. A slowdown in the market has lead to a renewed
interest in the field of Customer Relationship Management (CRM), Data
Warehousing and Business Intelligence (BI), which are offshoots of the area
of marketing and customer relationships, using information technology. Most
businesses have realised the importance of retaining customers, especially
since reams of data exist to prove that managing and servicing existing
customers is cheaper than pursuing and running after new ones all the time.
Even basic research on marketing trends has proven that it costs more to
acquire a new customer than to service an existing one. Even from a
customer’s standpoint, there is very little reason to switch loyalties
often, especially if things are going comfortably with the existing vendor
and the level of service is good. Switching involves changes and disruptions
in service levels that most regular customers try to avoid. However, if the
service being provided by the existing company is shoddy and someone else
promises a better product or deal, most of us would switch.
Marketers have long realised the importance of repeat businesses and have
devised innovative ways to retain customers. One of the most popular loyalty
programmes of all times was the Airline Frequent Flyer programme designed by
airlines in the seventies. At its simplest, it works like this: new
customers enroll with the airlines and are allotted a customer id or
frequent flyer number. After enrolling, the customers/travellers are
expected to use the number every time they book a flight. They get a point
or ‘credit’ for every mile travelled on the airline. The airlines accumulate
the points and offer rewards, which are redeemable after a predefined number
of miles/points are collected. Rewards include perks like free tickets and
upgrades. Given the popularity of airline programmes, other businesses like
hotels, restaurants, car rental agencies and supermarkets too devised
similar loyalty programmes to attract and retain customers.
There is another major attraction for businesses to encourage loyalty
programmes: sophisticated data mining techniques are available to help
companies study buying patterns, customer preferences and trends. This is a
really useful tool for businesses trying to forecast demand and for managing
inventory and supply chains. For instance, large supermarkets regularly use
data warehouses, built to receive inputs from various sources, including
loyalty programmes. Data warehouses help in studying customer patterns,
buying trends and behaviours and provide a tremendous amount of BI to
marketing managers and planners. This leads us to the next topic, which is
of real interest to technologists: the interfacing and design of systems
involved in BI, data mining and warehousing.
Large companies spend millions on BI tools and technologies to glean more
information about their customers. They use such information to design,
develop and package products and solutions tailored to their clients’ needs.
Such information also helps companies in cross-selling products and
services. For example, supermarkets have discovered that people generally
buy milk, eggs and cheese together. Therefore, they generally stock cartons
of eggs and sampling of new cheese products near the aisles where they stock
milk. This way, customers who go to pick up milk are subconsciously
encouraged to also buy eggs and try out newer kinds of cheese, thus
increasing sales for the supermarket too. A win-win proposition?
Behind the scenes, IT operations for BI involve design of complex data
warehouses, data mapping and messaging architectures. BI also involves the
use of complex analytics and algorithms, designed to help marketers drill
through the abundance of data generated during regular business processing.
Most large organisations have created data warehouses that store much of
their historic data. This is done for two main reasons: firstly, most
regular databases are optimised for access by business applications.
However, data mining requires databases to be designed according to the
“star schema” for ease of access by analysts.
Secondly, most database administrators (DBAs) are hesitant to allow
non-application (adhoc) querying on their databases, and prefer the use of a
clone of production database most of the time. Therefore, data warehouses
try to blend the best of both worlds: design to facilitate access by BI
analysts incorporating data from regular production databases. I do not wish
this column to be a primer for BI and data warehousing, therefore readers
who are more interested in these topics are advised to find information on
the Web or from one of the several journals. However, my aim at this point
was to draw the reader to get a glimpse of the link between the
business/functional need for BI and some of the few key technologies
Although, the area of BI is well-established in the West, Indian
businesses—at least the mainstream businesses—are yet to capitalise on the
power of such analytical technologies. Indian companies and governmental
organisations have recently started computerising on a large scale. As newer
databases and application systems are designed and implemented, systems
architects need to keep in mind that data analysis and mining are going to
emerge as a requirement from business leaders at the top. For instance, the
Indian Railways and Airlines have been computerised for at least a decade.
The bosses at the top are probably going to request the use of databases for
information on planning, forecasting scheduling future growth strategies.
Maybe they are already doing so? It is just a natural next step ahead.