Why real-time CRM analytics is hot

Tough market conditions mean that companies which have invested heavily in customer relationship management (CRM)systems in...

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

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