These tools, when combined, provide for interactions through all communi- cation channels—e-mail, call centers, mailing brochures, advertisements, newsletters, and so on. These are all touch pointswith customers and prospects.
Every channel can engage the customer with a dynamically personalized and compelling experience by leveraging marketing, sales, and customer support.
Each interaction is an opportunity to gain knowledge about customer prefer- ences—and to strengthen the relationship. I touched earlier on the perils of cus- tomers and sales targets receiving inconsistent messages. The problem is that each of the communication channels may unknowingly use their own customer intelligence data to interact with a customer without realizing that a different message may be being delivered to the samecustomer via another communica- tion channel. A common CI system provides the foundation to integrate with and feed all of an organization’s different communication channels. This then provides a consistentpersonalized message from the receiver’s view—consis- tent across all channels.
customer interaction can be via any communication channel, possibly inbound or outbound. Additional event triggers may include lifetime events, such as marriages or school graduations, that may be anticipated or reported from other sources. In short, CI/CRM tools provide a single, unified view—a comprehen- sive, cohesive, and centralized view of a customer—that can dynamically adjust based on feedback.
Good CI and CRM helps organizations make smarter decisions faster. A work flow or business process without the ability to measure, analyze, and improve its ef- fectiveness simply perpetuates a problem. In sum, CI and CRM allow end-to-end functionality from sales lead management to order tracking—potentially seam- lessly. CI includes data warehouses that are used by analytical applications that dis- sect the data and present it in a form that is useful; and CRM executes CI’s plans.
Returning to Figure 17.1, the process cycle then flows continuously as a fig- ure eight. The feedback about customer behavior—whether in response to a mar- keting campaign or as monitored consumer preferences—crosses back into the analytical CI domain, where that data is again gathered and analyzed for the next reformulation of strategies. Dozens of campaigns and strategies of different magnitudes can continuously and concurrently cycle with this CI/CRM process.
Each campaign may target a unique promotion to a particular niche market.
To summarize, the main codependencies between CI and CRM are these:
CI systems are analytical and need to extract from the front-office opera- tional CRM systems data useful for analysis (e.g., customer, transactional, and third party data). Ideally, CI drives CRM.
CRM systems need to surface the intelligence generated from the analyti- cal CI systems to be more effective and actually make use of the derived intelligence.
162 INTEGRATING PERFORMANCE MANAGEMENT WITH CORE SOLUTIONS
Data Mining Analytical Tools Discern the Relevant Information
Customer relationship management (CRM) systems are intended to aid an or- ganization in optimizing value and satisfaction for its customers through the methods that the organization uses to communicate with them, sell to them, and service them. Through integration, customer intelligence and customer relationship management (CI/CRM) allow marketing, sales, and service em- ployees to coordinate as they plan, gather data, track events, and organize ccc_cokins_17_151-172 .qxd 1/14/04 10:30 AM Page 162
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Data Mining Analytical Tools Discern the Relevant Information (Continued)
themselves from presales to postsales for both prospects and existing cus- tomers. Unfortunately, typical operational CRM software tools lack the nec- essary analytical rigor, defaulting to an emphasis on collecting data and displaying standard sales reports (e.g., sales pipeline) or popular marketing parameters (e.g., customer’s last purchase date, sales amount, and purchas- ing frequency).
Progressive CI/CRM tools with powerful data mining functions provide much more business intelligence. The analytical CI system facilitates the inte- gration of the front-office, customer-facing CRM systems to ensure coordina- tion and sharing of a consistent message. That is, data from multiple communication channels can be consolidated to create CI for each customer or microsegment, and then the analytical CI system can formulate and push the appropriate intelligence into the front-office CRM channels.
For example, imagine that a company enjoys a surge in sales. What really made the difference? A price reduction? A new display? The timing being a holiday weekend? A new advertisement? A competitor’s price increase? With excessively clustered segmentation and broad-brushed averages, marketers can be deceived by inferring causal relations with coincidences. But with to- day’s data mining software and transactional detail, marketers can discern cross-effects. Simple statistical regression analysis models examining price/volume change elasticity relationships can be analytically upgraded with mixed modeling techniques that explore dozens of variables. These can generate predictions about the impact of a specific marketing event on spe- cific products or services at a particular store or branch.
By analyzing more granular microsegments and subpopulations that re- veal relatively greater differentiation, not only can more predictable out- comes be forecasted, but the relevant variables may not be necessarily demographic (e.g., age, income level, gender) but rather nontraditional (e.g., the source of the initial purchase, time of day when purchased).
Averaging can introduce problems. For a new product rollout, a re- tailer’s national advertising and promotion budget should not be distrib- uted evenly to accounts across the nation. Obviously, you shouldn’t try to sell diapers if your store is located across the street from a Toys ’R Us retail store. One answer does not work everywhere. With data mining tools, you can minimize missing opportunities to stock products or launch promo- tions at a subset of stores or to a subset of customers (not just demographi- cally segmented). Conversely, you can prevent overstocking where there is less likelihood of demand.
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