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Five predictive imperatives for maximizing customer value – for the Digital CMO

by Scott Hoffman on May 5, 2011

Five predictive imperatives for maximizing customer value – what every Digital CMO and Marketer should aspire to apply predictive analytics to enhance customer relationship management (CRM). This is a long read, but very important – especially for the digital CMO. (Full Report from IBM pdf format)

The five predictive imperatives

Based on extensive experience with a wide range of organizations, IBM has identified the following “predictive imperatives” – best practices used by leading organizations to maximize customer value with predictive analytics.

1. Base your customer strategy on predictive profiles
2. Predict the best way to win the right customers
3. Predict the best way to grow customer relationships
4. Predict the best way to keep the right customers longer
5. Use predictive intelligence at every customer touch-point

1: Base your customer strategy on predictive profiles:

Detailed, accurate predictive profiles are the essential foundation of any customer strategy and CRM initiative. To understand your customers better, use analytical tools to create customer segments, and then create predictive profiles of each segment. These profiles, when deployed enterprise-wide, enable your entire organization to focus on activities that are most likely to generate the highest returns.

Identify key customer segments
You can define customer segments based on behavioral information drawn from operational systems and on attitudinal information obtained through market research. The two approaches complement each other, enabling you to gain a more accurate customer understanding and develop more effective strategies for each customer segment. Some suggested way to segment your customers are:

  • by value builds an understanding of who your most valuable customers are
  • by behavior helps you know who is most likely to purchase your products or services, so you can use marketing funds more effectively
  • by demographic and other supplemental data provides additional information that can be used in predicting customer behavior
  • by attitude adds another dimension to your customer understanding. One of the best ways to understand customers’ attitudes is to ask them through survey research.

2: Predict the best way to win the right customers

Create a prediction-based customer attraction strategy
Use predictive profiles to determine what types of customers you want to attract. Then create a cost-effective attraction strategy that includes separate plans for each customer segment.

Optimize your customer attraction strategy with response modeling
Fine-tune your customer attraction plans by using response modeling to predict which marketing programs will generate the highest response.

3: Predict the best way to grow customer relationships

To maximize customer growth and increase customer lifetime value, your organization needs to know not only what customers are most likely to want, but also when and how they will want it delivered.

Create a prediction-based customer growth strategy
By using predictive profiles, product-affinity models, segment-migration models, response models, and even survey research, you can generate predictive intelligence about your customers.

Discover product affinities
Customers often purchase products and services together, or in certain sequences. By analyzing their “market baskets” – products and services purchased at the same time – you can offer customers appropriate additional products at just the right time.

4: Predict the best way to keep the right customers longer

Studies have shown that customer acquisition can cost five to 12 times more than retention, and that improving its customer retention rate by just five percent can increase an organization’s profitability by from 25 to 100 percent.

Create a prediction-based customer retention strategy
Keep your best customers longer by creating attrition models, and then use these models to determine which customers are at risk of defecting.

Create predictive attrition models
Understand which customers are most likely to leave for competitors and, more importantly, why. By applying data mining techniques to data about your customers, you can develop profiles of customers who are valuable and customers who have previously defected. Then you can develop strategies to keep your valuable customers from leaving.

5: Use predictive intelligence to drive customer interactions at every touch-point

Monitor and manage customer value
Management gurus tell us that we cannot manage what we do not measure. This is certainly true of customer relationship management. Profitable customer relationship management requires precise, timely measurement of the factors that affect customer success, and your bottom line. This effort requires a combination of historical and predictive technologies: predictive analytics, to identify customer targets for acquisition, up-selling, or cross-selling; and historical analysis, to monitor the results of marketing campaigns and sales programs.

This report was posted on in Q4 of 2010 on the IBM site. A copy of the full report can be downloaded by clicking here.

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