Tough market conditions mean that companies which have invested
heavily in customer relationship management (CRM)systems in recent
years are now under pressure to show a real return on that
investment. To do this they need to improve the way they analyse
and act upon the customer information they collect. The goal is to
react to customer requests more quickly while maintaining the
integrity of the decision-making process.
Hence the new buzz-term. "Real-time CRM analytics" refers to the
methods used to exploit this information (typically, a combination
of reporting, Olap and data mining tools). But increasingly
businesses need to use customer intelligence in a more timely and
personalised fashion in order to support customer interactions.
The concept brings together the need to respond quickly to changing
market conditions and to exploit customer data to maximum effect.
It is vital in highly competitive markets such as financial
services, retailing and telecommunications, where companies are
increasingly using CRM analytics to understand customer
preferences, buying patterns and trends, and to identify those with
a high value.
Meanwhile, CRM application suppliers are facing tough questions as
to how their products can really help companies to survive an
economic downturn.
It is not only market conditions that have changed, however.
Organisations are also being forced to make decisions in "Web
time". The typical customer now has a multitude of contact points
with any one organisation: for example, through the Web, a retail
store and a call centre. The challenge facing companies is to build
an integrated view of the customer, to understand how each separate
touchpoint relates to the others and to use the resulting customer
intelligence to better understand and service them.
Retail companies, for example, need to understand their customers'
in-store buying habits so that they can service them in the most
effective way when the same customers purchase online. However, the
situation becomes more complex, when the customer then telephones
to amend an existing order. Having a complete view of the
customer's buying patterns and purchases makes it easier to offer
the most appropriate level of service.
Real-time CRM analytics typically works by processing an incoming
customer request against a set of predefined business rules and/or
data mining algorithms to determine the best course of action to
take or the best recommendations to make. The resulting "answer",
which is typically derived from the analysis of real-time
operational data and summarised historical decision support data,
is then passed back in real time via the necessary channel to
front-office staff or the customer.
Real-time analytics often combines CRM software with infrastructure
components such as enterprise application integration technology
and datawarehouses, and tools for Olap and data mining that can
analyse real-time data.
The concept of real-time analytics relies on having a consolidated
view of the customer across all lines of business and customer
contact points. This is a vital for companies wishing to offer the
correct level of service relevant to a customer at a particular
point in time. The challenge comes in solving the complexities of
data integration and also in understanding the relationship and
interdependencies between different communication channels used
within an organisation.
Effective use of real-time analytics, therefore, presents
organisational as well as technical challenges. For example, the
use of real-time decision support implies a significant change in
responsibilities and priorities for the staff involved.
Front-office staff may find themselves being shifted from a focus
on customer service to a more sales-orientated focus, using
real-time analytics for cross-selling and up-selling.
This would pose significant cultural challenges for many
organisations. You will need buy-in from front-office staff in
order to ensure success.
The investment that many IT organisations have made in
datawarehousing tools, CRM applications and data integration
projects in recent years means that the goal of decision-support in
real time is now feasible. Closing the loop between the data
collection, the analysis of information and live customer support
is an attractive proposition.
But the main barriers to success - integration of data, IT
infrastructure and decision-making processes - have proved to be
some of the most intractable in IT. How well we deliver on the goal
of real-time analytics will tell us much about the real level of
maturity of our systems and our managerial structures
Eric Woods is research director at analyst firm Ovum