What Is MOM ? (the Mother of all Models) And What Does it Do?

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When it comes to making decisions, today’s executive has access to roughly 1,000 times more data and 1,000 times more powerful analytic and computational tools than was available in 1993, according to Don Peppers, founding partner at Peppers & Rogers Group. With 2.5 quintillion bytes of data being generated across the world each day and predictive analytics capabilities becoming more powerful than ever, the adoption of evidence-based decision-making among corporate leaders is becoming more widespread.

This data eruption has formed a deeper pool of information regarding customers’ interests, needs, behaviors, and transactions from a variety of touchpoints. This granular level of detail enables senior executives to make more informed decisions about their customer engagement and marketing strategies, including the next best action to take with each customer and developing customized sales campaigns for individual customers.

While having such granular level of information about customers is critical for marketing and sales success, oftentimes, the Big Data dilemma creates a snag for sales and marketing executives. These individuals are in need of effective capabilities that allow them to collect and analyze data from multiple touchpoints in real time. Our proprietary data model we call the Mother of all Models (MOM) is designed to collect results from numerous data models to help sales leaders and other stakeholders make the most effective decisions on sales strategies. If you think about it, the Mother of all Models is a lot like what an actual mom does: When there’s a family squabble or discussion, Mom has the final say.

MOM is the key driver behind the use of an intelligent engine that can analyze patterns in customers’ behavior. By gaining a deeper understanding of customer needs and behaviors, sales and marketing leaders can then take the next best action with customers that is relevant and attuned to their needs.

By building customer and prospect data models that combine channel, response, and acquisition/conversion data, MOM makes the final recommendation based on the following criteria:
  • Next best touch
  • Timing of the next interaction
  • Product preference
  • Lifecycle placement and scoring

For client partners, having these recommendations and being able to act on them can result in lifts in revenue and greater sales rep efficiencies. One global delivery services company was able to obtain a 120 percent lift in revenue, a 127 percent increase in conversions, and a 28 percent improvement in rep productivity.

When you think about it, every company has a competitive advantage over their rivals—each company possesses unique data about their customers that no one else has. Sales leaders who take advantage of the power of predictive analytics to develop a profound understanding of customers’ and prospects’ transaction histories (products offered and purchased), channel behaviors, lifecycle status, comparisons against industry data, etc., can accurately identify and focus on the ones with the greatest likelihood to purchase. Then knowing how to deliver the right message to the right customer at the right time over the right channel allows sales teams to execute effectively on sales strategies and deliver real business results to the enterprise.