Six Reasons To Use Predictive Analytics

Six Reasons To Use Predictive Analytics

Six Reasons To Use Predictive Analytics

Data volumes continue to grow. Customers can be reached—and lured away—through an ever-expanding range of channels. And batch-and-blast marketing continues to fall out of favor. The growth in predictive analytics solutions helps address all of those needs, and the technology is keeping pace with demand for equally varied reasons. “It's no one or two innovative breakthroughs, but rather a natural progression of more adopters, lower cost, and more momentum,” says Dean Abbott, co-founder and chief data scientist at SmarterHQ.

Whether it's gaining better understanding of changing market dynamics, or zeroing in on the best next message for every customer in your database, predictive analytics can deliver. If you've been on the sidelines so far, here are some reasons to get on board.

To make better use of data. Don't run from high data volume. Corral it. Focus on quality. And let predictive analytics dive into the deeper recesses to help model the ongoing customers responses to your marketing activity. When you understand how customers are likely to react in real-time, not just through quarterly reporting, you can start making more powerful strategic shifts in spending and messaging. “This is particularly powerful in markets such as hospitality and travel, where the frequency of purchase is lower and your data has historically been less complete,” says Dave Scamehorn, VP of marketing analytics at Olson 1to1.

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To evolve beyond RFM (recency, frequency, monetary) models. RFM models have a lightweight predictive component because they focus on messages that will move customers from their current box up to the most recent purchase category. The main limitation is that the data is always one step behind. “Recency and frequency are purely past behaviors,” says Leslie Fine, VP of data and analytics at Salesforce.

Predictive analytics can do more. Instead of just focusing on the offers that generate sales, you can model the behaviors and exposures that keep customers from sliding into inactivity. 

To understand customers better than your competitors.  According to Forbes Insights data, only 15 percent of marketers use multi-channel/multi-touch attribution data in their predictive models, or approximately one-third as many as use website data and demographics. That's a prime opportunity to create a competitive advantage.

To refine your understanding of customer lifetime value (CLV).

 



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