There was a time way back when — about 12 months ago — when digital marketers were happy using analytics to understand customer behaviors. Now they've upped the ante: they want to use predictive analytics to understand future behaviors, a decidedly more difficult task. But that's not stopping vendors from trying.

This morning, Bluecore, a data marketing platform for e-commerce brands, releases a new predictive analytics offering. It's designed to analyze behavioral and product data and predict who’s ready to buy (and who's not) as well as what products they are likely to want.

Bluecore’s Difference

According to Fayez Mohamood, Bluecore co-founder and CEO, there are differences between the new Predictive Audiences application and those already available on the market. This one ingests and analyzes customer data, and is directly integrated into campaign workflows across all channels, he said.

The objective is to help marketers address customer churn, assess a customer’s lifetime value, and predict and anticipate the way customers will react to marketing messages. 

“For Bluecore, acquiring and analyzing data and then enabling marketers to take immediate action has been core to our DNA from day one. The goal of delivering Predictive Audiences is to further expose the value of this data to marketers without adding any of the complexity that traditionally surrounds data science, and machine learning,” he told CMSWire.

“By applying machine learning to Bluecore’s unique data set, we have the ability to help marketers engage audience segments across channels and based on insights they would otherwise never have access to, ensuring money isn’t left on the table or even worse, causing customers to churn and never return.”

Bluecore: A Relative Newcomer

Since it was founded in 2013 in New York City, Bluecore has already raised $28.22 million in four rounds from six investors including FirstMark Capital, Corazon Capital and Felicis Ventures. 

Mohamood said the platform has more than 180 customers across more than 250 apparel, electronics, automotive and other consumer brands. It's growing because it fills a need, he said.

“Marketers lack the tools and unified data sets needed to execute on their retention and loyalty strategies. Instead they are forced to rely on manual segmentation work that is both difficult and ineffective. As a result customer segments are created from arbitrary rules that don’t consider the nuances between different groups such as VIPs or discount buyers that behave differently and have different product affinities and communication channel preferences,” he explained.

Predictive Audiences

He said Predictive Audiences improves marketing performance quantitatively, but also gives the marketer the ability to think qualitatively about how to engage specific audiences of customers, without second guessing how the audience is defined. With Predictive Audiences marketers should be able to:

  • Accurately predict customer lifetime value
  • Predict the probability a customer will make a purchase and when it will be made
  • Identify new customers, and new products they will be interested in
  • Understand whether customers are more likely to open an email, click on an ad
  • Proactively retain "at-risk" customers and win them back

Mohamood said marketers will be able to use Predictive Audiences anywhere they want to engage their customers, regardless of channel. He cites as an example a dynamic audience segment of those customers who only buy on discount and who need to be marketed to differently than full-price buyers, within an integrated campaign of email, display, social and onsite.

This responds to what he identified as the principal problems facing digital marketers at the moment, notably:

  • Engaging customers consistently across touchpoints
  • Lack of automated tools with a heavy reliance on data engineers and analysts
  • The noise created by the many solutions that claim to solve marketer’s problems and being able to distinguish the solutions that work and those that don’t

From a wider perspective, this responds to a pain point that Mohamood said is becoming more and more obvious in the enterprise.

"What we hear from customers and in the marketplace with greater and greater frequency involves problems marketers being able to extract value from the data they already have. Blue Nile research determined in a recent report that 73 percent of those surveyed said extracting actionable insights from their data is difficult or very difficult,” he said.

Marketers realize they have to become more data-centric to win in a fiercely competitive e-commerce marketplace. Unlocking the value of this data in a way that doesn’t introduce new technical hurdles or overwhelming time commitments is something folks continue to strive for.”

Bluecore plans to integrate its platform with existing components of the marketing stack to reach new verticals and to deliver value in new channels at scale.