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Services Management

Notes companies already have components of CRM systems, such as sales automation systems that
record customer-contact information. Data can come from a variety of sources — call centers,
point of sale transactions, web click stream data, back-end databases, and even faxes and phone
records. Data from these channels is integrated into a customer-oriented data mart or data
warehouse, a knowledge base that continuously captures customer data. While some integrated
CRM solutions do provide this capability, companies still need the tools that will take this
composite data and paint a picture about their customers. Companies like Siebel, PeopleSoft,
SAP, Pivotal, Oracle, and Sales Logix provide software on CRM.
A customer-relationship analytics system is a set of tools that are run against this data to perform
business intelligence functions — reporting, analysis and data mining. Such tools can help
marketers visualise, through online graphics, patterns and relationships in customer behaviour
and trends. A number of variables can be measured through this data analysis engine, including
net profitability, return patterns and order-fill rates.

Example: A company may find that a particular customer segment to which it has been
aggressively marketing also has a high return rate for products – thereby diminishing the
profitability of this effort. It may also find that its best customers are being treated the worst
because it is not able to find out who its best customers are and may not be able to judge the level
of attention their business merits.
To have a successful impact on your CRM program, an analytics system needs to be speedily
speedy accessible, and user-friendly. While customer-relationship analytic systems may employ
sophisticated tools running against data stored on high-end systems, it is important that the
end-results are user-friendly and accessible. If end-users have difficulty using a system, or
cannot pull up the data they need within a few seconds and navigate down through the
information toward a solution, they will abandon the application and its benefits will not be
realized. In fact, this historically has been the main reason for the failure of sales force automation
systems over the years – sales representatives, usually too focused on the business at hand,
refused to find the time to learn to use what they perceived as a difficult or cumbersome system.
The solution might be to have highly-skilled analysts who use complex statistical tools for their
analyses. The sales force will receive results of the analyses in a business-focused presentation
that addresses the implications for each action.

Many companies are removed from their end-users because they sell through third-party
distributors and retailers. For these companies, customer-relationship analysis can look at various
data feeds and provide a valuable picture of customer trends.
Importance of Customer-relationship Analytics

Customer-relationship analytics is part of a growing effort to apply measurable and actionable
analytics to key parts of the business. Business performance management applications now
cover a range of key performance indicators, including sales, marketing, finance and
manufacturing. The ability to apply analytics to customer relationship management opens up
opportunities to dramatically improve these relationships. In today’s highly competitive
environment, businesses need to better understand their customers, which ones are the most
profitable, and how to best retain those customers. Though companies are investing millions of
dollars in CRM systems, they are only generating data and failing to tell the company what the
data means. Customer-relationship analytics helps companies make sense of customer needs,
help companies manage these relationships more intelligently and help predict the future. Such
knowledge provides a crucial competitive differentiation for companies seeking to gain market
share and reduce operational costs.

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