Analytics in Multichannel Marketing - Data Square
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7 Uses for Analytics in Multichannel Marketing

By Heather Fletcher, AdWeek

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 seven best uses of predictive analytics in multichannel campaigns, provided by:
  • Erick Brethenoux. executive program director of predictive analytics at Armonk, N.Y.-based IBM;
  • James Kobielus, senior analyst at Cambridge, Mass.-based research company Forrester Research;
  • Devyani Sadh, CEO of Wilton, Conn.-based Data Square, a business intelligence solutions provider;
  • Marcus Tewksbury, strategic solutions principle of marketing at UK-based marketing software company Alterian; and
  • Zabin.

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.” Caesars has refined its customer data to this single view and thus is able to “determine an ongoing level of re-investment for each player,” according to Zabin. “The predictive score is combined with each player’s profile and offers are delivered across multiple channels—direct mail, email, mobile.” When a customer responds, Caesars updates and recalculates that profile. Customer behavior allows the casino giant to determine which offers—restaurant, hotel, slots or a show—will appeal to which customer and how often to send them, he adds. That way, communications can match the fact that a particular customer only travels to Vegas once every couple years.
Kobielus says once marketers build the single customer view, you should use it on all customer-facing applications, including marketing, sales and customer service.

2. Determine promotional effectiveness not only by channel, but also by narrowly defined customer segments.

Zabin says, “Different promotional tactics (e.g., coupons, discounted prices, sampling, special displays, feature ads, on-pack stickers, special packaging, events, etc.) tend to elicit different response rates based on the characteristics of the target segment. After all, different customer segments have different price sensitivities. Sending a 15 percent discount coupon to a consumer who only buys that company’s product loses money, since that consumer is unlikely to buy a competing product. Sending a 15 percent discount to a brand-switcher is a whole different story.”

3. Notice which customers are already maintaining a relationship with a marketer in more than one channel.

Sadh notes that when marketers maintain multichannel contacts with the same customer, it can increase campaign effectiveness because “multichannel users tend to be more loyal,” according to her NCDM 2010 presentation: “Leverage Analytics and Campaign Strategies to Deploy a High ROI CRM and Database Marketing Practice.

4. Let Social media data inform your multichannel strategies and be part of predictive models.

Kobielus says: “Incorporate social network analysis variables into these predictive models in order to assess how sensitive acceptance rates are to hidden, latent, non-obvious connections among people, groups and organizations. … Integrate these predictive models with complex event-processing middleware in order [to] tie targeted offers to real-time variables such as customer geospatial coordinates, portal clickstreams and sentiment analysis on social media messages.”

5. Drive experience by learning about customers, instead of using that information for “promotion and stimulation.”

Once marketers learn—through consumers’ interactions with the brand—what interests them, they can provide value. Tewksbury says “Analytics have to work toward driving an experience, instead of pushing outbound messages. The crux of predictive analytics is understanding how the customer will respond to your offers,” he explains. “But today’s consumer tends to have a negative reaction toward outbound marketing, which means you need to use analytics to consider how to create an engaging and relevant experience for the customer in the future.”

6. More and more, B-to-B companies can learn targeting tactics from B-to-C businesses.

Tewksbury says, “Both B-to-B and B-to-C organizations are gathering more information about their target consumers than ever before. There is also a convergence in the marketing tactics [used by] B-to-B and B-to-C, with a lot of B-to-C functionality creeping into B-to-B to capitalize on the growing volumes of information available. Although B-to-B is more focused on lead nurturing and event management, the same principles apply to B-to-C: Gather information, apply insightful analytics, act upon them and apply what you learned to future content or communications. These are classical database marketing tactics simply practiced through more channels today. The lesson to remember is that today’s customer owns the relationship more than organizations do.”

7. Remove unresponsive customers from campaigns.

Brethenoux says, “Once there is a prediction of which customers are most likely to respond to an offer, eliminate the customers who are least likely to respond from the mailing lists to stay within the budget.”

Sadh adds to watch for profitability: Predictive analytics can be extremely helpful for audience selection for expensive channels such as direct mail and phone. This is particularly true for situations where the available universe is large and only a portion of the universe needs to be targeted.