Using targeted marketing to improve the shopping experience

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Women shopping.

Over the past decade and a half, successful web retailers have been able to tailor their marketing toward the individual consumer, providing a level of personalization all shoppers—both offline and on—have come to expect as standard across the industry.

Shoppers have come to expect the tailored marketing that algorithms can deliver to them when shopping online in physical stores. However, that kind of personalization is only possible with sales staff that knows the customer and the merchandise very well. Even in the e-commerce space, customers often are frustrated by an overwhelming number of irrelevant search results that steers them away from their intended purchase

This disconnect between expectations and reality for consumers can mean significant loss of potential revenue for retailers. Three companies, however, have found a way to fix what’s broken in retail, opening up access to relevant products and information for both shoppers and sales staff. The first step for each: partnering with IBM Watson.

The fix for e-commerce

Brian O’Keefe, CEO of Sellpoints, a retail channel sales platform, uses Watson to help fix the problem of irrelevant search results. “Consumers have a very short attention span,” says O’Keefe. “They expect to get from A to B very fast,” especially when engaging online. Sellpoints services, built on Watson’s platform, help make that a more efficient process.

For example, take a straightforward online search like “stylish running shoes.” Normally this would return a bevy of irrelevant results—like stiletto heels or boots—based on an incomplete or poor reading of the key words. Such results force shoppers to manually filter. The more they have to click, the more likely they are to drop out, at a rate of 40 percent for each click. It’s known as the “half-life of online shoppers.”

With its ability to interpret natural-language searches, Watson is able to help eliminate half-life. By reducing search to a single click, Watson and Sellpoints increase the odds of purchase by 300 percent and boost the number of shoppers receiving relevant results 60-fold.

And as Watson continues to learn, it delivers better and better results. “We’ve seen the capabilities of interface improve dramatically over last eight months,” says O’Keefe.

Bringing the data advantage into physical stores

A consumer and business intelligence platform for brick-and-mortars, eyeQ looks to bring the personalization of a high-quality online shopping platform to physical stores. In order to deliver personalized marketing to in-store shoppers and gather precious consumer data, eyeQ places interactive touchscreens in retail locations at shoppers’ points of decision.

Until eyeQ started working with Watson, it was limited to the profile information it could capture from in-store kiosks, using facial intelligence to help identify age, gender, attentiveness and emotional response. For return shoppers, kiosks gathered data on browsing history and loyalty.

Personalized marketing, however, only works with when there’s enough data captured to provide insights into how consumers want to be engaged. When eyeQ layered the Watson Personality Insights API onto its algorithms, it was able to take advantage of cognitive computing to assess individual personality traits among consumers.

With a shopper’s Twitter handle, for example, eyeQ and Watson can identify an individual’s propensity to spend and, in turn, recommend products within a specific price bracket. The addition of that insight tips the odds in favor of purchase by 70 percent. “The Watson APIs make it straightforward to implement cognitive computing into any application today,” says eyeQ CEO Michael Garel.

He’s especially excited by Watson’s potential for personalization beyond the product. For example, using cognitive computing, it’s possible to match shoppers’ personality traits to specific sales associates. Customers have a better experience, and associates become more effective.

Getting the right information at the right time

Red Ant brings the power of big data into the physical retail space. Its product, SellSmart, is a “thinking application” that capitalizes on the power of the Watson cognitive system. These apps go beyond traditional consumer data like age, gender, location and recorded purchase history, exploring unstructured information like social media and online reviews to gain a deeper understanding of how consumers engage with certain products.

Red Ant CTO Dan Hartveld describes SellSmart as a “voice-activated sales trainer that uses the power of Watson to bring big data to the shop floor.” Watson’s ability to tap into and interpret unstructured data puts all the information sales associates need at their fingertips.

For example, should a shopper at a shoe store ask a sales associate for help distinguishing one brand’s running shoe from another—specifically, what long-distance runners have to say about the two brands—the associate can use SellSmart to instantly access reviews and other data, leveraging Watson’s natural-language abilities to find the most accurate results.

That kind of real-time result translates into a measurable ROI. “Empowering employees with information they need quickly has been shown to increase sales conversions by nine percent and heavily reduce one-to-one training requirements,” says Hartveld.

Without the advantage of knowledge at their fingertips, salespeople literally end up sabotaging their sales. In fact, 73% of sales people report that they have sent customers away when they don’t have the knowledge needed to answer their questions. Even more troubling, 43% have admitted to lying to customers every week to cover up their lack of understanding of the products they sell.

Retail service providers don’t have the time to waste implementing complex data products, which is why Watson was the ideal solution for Red Ant’s needs. “Watson has been built with modern agile development in mind,” says Hartveld. “We were able to get from design to prototype in just five days, and bring a full product to market in less than three months.”

When people find what they want without the frustration of information overload, they’re more inclined to buy. Watson’s cognitive computing system and natural language capabilities make targeted marketing and accurate search a reality online and off, improving the retail experience for shoppers and boosting sales along the way. Best of all, it only gets sharper over time.

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