Maximize Conversions With Predictive Analytics

March 1, 2016
Contributor: Chris Pemberton

Focus on customer triggers to improve your results.

Laundry detergent provides a vivid example of how predictive analytics can drive conversions. In a new twist on the old problem of “ran out of soap — need to run to the store,” a sensor determines that laundry detergent is running low and automatically reorders through an online subscription service. In essence, this depletion-triggered buying journey, enabled via sensors (the Internet of Things), anticipates a customer need to determine when and how to reach them for greatest effect.

“Focus on what triggers a conversion to anchor your predictive powers.”

Buying journeys start with a known trigger — an impulse, an urgent need or a whim. Showing up at the trigger is the key for brands who want to increase conversions and awareness.Applying predictive analytics to customer interactions can maximize awareness and conversions, noted Andrew Frank, research vice president and distinguished analyst, Gartner for Marketing Leaders. Focus on what triggers a conversion to anchor your predictive powers.

Use data to predict trigger points

Traditionally, marketers relied on factors such as seasonal buying patterns and product-based purchase cycles to predict buying triggers. Marketers then used promotions to stimulate action. Today, marketers use data to detect trigger points in a growing number of ways.

When customers search for a product or service, this often signals a need or trigger point. Marketers can use search-engine marketing to target by their customers’ intent. Another approach is to scour social feeds for signs of intent, even latent ones that could influence consideration sets ahead of an anticipated trigger point.

Focus on what triggers a conversion

The trigger point is the key to predicting lifetime value, noted Mr. Frank. A life event such as marriage or moving might trigger an initial impulse to buy. For a more considered purchase, such as buying a car, shoppers will have a set of options that need to be narrowed down in a research phase. This may trigger reaching out to friends and trusted sources for input. In the case of the auto-replenishment laundry detergent example, depletion and low supply levels were the trigger.

Emerging data sources such as the Internet of Things provide a host of new signals to anticipate needs and predict trigger points. As subsequent trigger points become more predictable over time, knowing which factors influence the loyalty choice becomes a critical marketing advantage.

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