The 7 Best Uses for Predictive Analytics in Multichannel Marketing

Las Vegas-based Caesars Entertainment Corporation may never say “Et tu, Brute?” to its data. Betrayal may never happen because the gambling empire exemplifies how direct marketers can grasp customers’ multichannel touchpoints with predictive analytics and spin single views of the customers into gold, says Jeff Zabin, research director at Evanston, Ill.-based research firm Gleanster.

Zabin says other marketers can learn from Caesars because the company “is incredibly sophisticated when it comes to direct marketing predictive analytics tactics. Activities skew towards direct mail, but also incorporate mobile and email as part of a multichannel strategy.” The calculations based on demographics and behavior are so specific they can be realized in mobile offers made while guests are on the casino floor or through daily email offers during a Vegas visitor’s five-day stay.

Marketers hoping to rise to Caesars’ level by the Ides of March can follow this advice on the best uses of predictive analytics in multichannel campaigns, provided by:

1. Obtain a single view of the customer by focusing on customer data integration, Zabin says. “This is a prerequisite to predictive analytics and a critical success factor in multichannel marketing effectiveness. To increase response rates and propagate a single view of the customer across the organization, companies need to first integrate customer data, which includes resolving discrepancies in the spelling of customer names and various numerical identifiers. Customer data integration provides the foundation for deploying decision management systems that enable companies to deliver highly relevant customer experiences, services, messages and offers across multiple channels and touchpoints.”

Heather Fletcher is senior content editor with Target Marketing.

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  • http://BRDeshpande BR Deshpande

    Predictive analytics adoption is increasing among large companies, no doubt by the ROI promise. How can SMBs take advantage of this technology? Their problems are also similar to large corps, except for scale.

  • http://DevyaniSadh,Ph.D Devyani Sadh, Ph.D

    There are a number of applications of predictive analytics that SMBs can benefit from. For example, if the size of the prospect universe is large and SMB’s need to pick the best candidates, predictive analytics can result in good ROI. Identifying the best cross-sell candidates or determining optimal investment levels for customers is another good application for SMBs.

    However, it is important for SMBs to conduct a preliminary ROI analysis prior to investing in predictive analytics and they should probably stick to less expensive models. Key factors to consider include a) size of the available universe, b) size of the targeted universe, and c) profit potential from each buying customer.