Using analytics to drive knowledge management for better CRM. (Customer Relationship Management).

Many companies have already implemented CRM and are now looking for ways to get the most out of their investment. Much of the hope for realizing ongoing and fixture business value from current technology investments lies with analytics. Analytical CRM applications provide all the necessary functions companies require for measuring, forecasting and optimizing their customer relationships. With the flood of information that companies collect, store and have available today, the challenge is getting the tight information to the right people at the right time to maximize the efficiency of customer interactions. By using analytics to boost the efficiency of knowledge management, companies have a powerful new tool that uses customer information to extend and deepen their relationships with customers.

Turning Analytics Into Opportunities

With analytics as an engine to interpret all of the relevant customer information companies already collect from different sources, including customer reaction to marketing campaigns, customer shopping method preference and customer questions received through the interaction center, raw data can be better shared across the enterprise to improve customer satisfaction levels. Analytical models can be created to help companies evaluate their relationships with customers to answer the questions that will derive valuable insights from the extensive amount of data collected.

As an example, an online retailer might discover that it was losing sales because of out-of-stock products. Using analytical insight gained from its CRM system, the company learned that customers would be willing to buy these products if they knew when they would be back in stock. Working with already collected customer data, the company might implement an e-mail program to let interested customers know when items marching their interests became available. Further down the road, the company might find that since the e-mail initiative began, thousands of customers have requested to receive email updates, thereby increasing interactions between the online retailer and its customers. And as a result, the analytical discovery led to increased customer satisfaction, loyalty and sales.

When applied to knowledge management, analytics helps companies better understand customer needs and preferences to identify recurring patterns.

Analytics helps -- allowing companies to deepen their relationships with existing customers through personalized communication; identify most profitable customers and optimize cross-selling and upselling possibilities; and maximize customer loyalty so that if a valuable customer shows signs of leaving, the situation can be recognized and easily identified and countermeasures can be implemented in time to prevent the loss.

Analytics In Action

Personalized Marketing.

Of the analytic capabilities in use today, customer valuation is a critical component. When tied to knowledge management, customer valuation can help companies concentrate their limited resources on their best and most valuable customers. Taking into account customer lifetime value, which is a forward-looking view at customer profitability by segment, customer valuation identifies the true value of a customer and ensures proper allocation of resources per customer segment.

Analytical CRM helps companies understand the information collected through customer interactions to profile their best customers and create campaigns specifically designed to deepen those relationships. For example, in the chemical industry, moot products can be viewed as commodities and many of their customers have varying tolerances for the quality of the chemical products they receive. Chemical companies are beginning to realize that costs can be saved based on a customer's tolerance for lower grade quality products, known (know as "off-spec") products, and the flexibility in the timeframe they have set for delivery. By raking these and other factors into account, a chemical company can discover which customers require the highest quality product with the tightest timeframe and thereby have the highest profit potential. By customizing service and product offerings based on customer value, these chemical companies ate able to ensure that they are driving maximum profitability from their customer base while improving satisfaction of their most profitable customers.

Cross-Selling/Upselling Customer Satisfaction.

Analytical models can also be created to forecast the type of information, based on customer behavior patterns identified from past data, that customer-facing employees will need to make the most of customer interactions. In many situations, there are several possible solutions that will meet a customer's needs and the knowledge gained through analytics can help direct the agents to the right solution for each customer interaction. For example, when a customer contacts an interaction center for the second time with a product-related question, the analytical model rakes into account the customer's profile and history, prompting the agent to consider alternative information that might not have been available without the help of analytical insight. The computer software industry is notorious for having multiple and frequently ineffective solutions to address technical issues, which often leads to customer frustration. Analytics allows companies to collect data on which solutions arc suggested to customers, as we ll as the success rates of those solutions and can then join that information with customer transaction data and known customer profile information such as skillsets, other installed software applications, etc. Therefore, when a customer contacts the call center with a problem, the model would run smoothly and quickly provide the agent with relevant recommendations for the customer. The agent would then be able to provide a personalized solution. In addition, a second analytical model could be used to determine the likelihood that the customer would buy additional products and services such as software upgrades, complementary products, maintenance agreements, or professional services. This gives the agent an opportunity to turn a service call into a profitable interaction.

Churn Management.

Used to identify customers at risk of leaving, churn management is a new functionality of analytical CRM, allowing companies to leverage collected customer information to place a value on specific customers based on their likelihood to leave. Analytical CRM indicates which customers are inactive and in danger of straying and helps develop tactics to proactively combat the risks of customer loss. For example, three months before a cell phone contract is scheduled to end, the service provider would benefit to know which customers are likely to cancel their contracts. This would help the company contact only those customers at highest risk of canceling their service and avoid the risk of alerting content customers that they now have the opportunity to terminate their contracts. With this type of well-directed customer selection, the costs of the campaign are minimized and the maximum number of customers can be retained.

As the above example shows, churn management can effectively be used in industries with high customer turnover such as telecommunications and financial services industries, but other industries are beginning to discover the benefits of this analytical CRM process as well.

Knowledge management interpreted through analytics can dramatically improve CRM. By more precisely interpreting and sharing information, companies are better able to identify and segment specific customer groups. With the opportunity to increase sales and profitability through cross-selling opportunities, analytics can also improve customer retention and help prioritize the most profitable customers. By tying analytics closely to knowledge management, companies can equip the right people with the right information to meet individual customer needs, resulting in optimized resource allocation and ultimately, improved customer satisfaction.

As a product director at SAP (www.sap.com) global marketing, Christopher J. Eldredge is responsible for all product marketing related activities for mySAP Customer Relationship Management (CRM) Analytics.

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