Increasingly competitive business and consumer marketplaces make it imperative for companies not only to attract customers, but also to retain them especially that small percentage of highly profitable customers. Retention strategies for valued customers generally focus on financial and/or service-level incentives to promote loyalty. Because few companies enjoy the economies of scale (or investment capital) to sustain competitive differentiation on price alone, many businesses seek to maximize customer value by building loyalty through brand and service differentiation. This approach places a premium on the quality of every customer contact as each interaction serves to either build brand or destroy it.
Personalization offers great promise for improving customer interaction quality, but this promise can only be realized if intelligent interactions are enabled in real time across touchpoints. Real-time database marketing presents unique integration and automation challenges around data consolidation, customer analysis, treatment logic and operational integration. To achieve intelligent interactions, robust predictive modeling must be integrated with historical analysis and dynamic marketing logic across all customer data sources. The resulting customer insights and treatment strategies must then be delivered quickly and consistently across all touchpoints in the context of each customer interaction. Failure to consider the context of each customer interaction results in predictable personalization at best. Only by using real-time customer context information coupled with robust analytical CRM can you achieve predictive personalization and realize the desired business benefits.
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