PYMNTS : AI + Consumer Behavior Data = Sales Growth

“With the popularity of Amazon, Pandora and Netflix, today’s consumer has the expectation that retailers will know them personally,” said Craig Alberino, president of Grey Jean Technologies. “When they walk into the store or browse online, they expect an experience tailored to their unique needs, desires and wants.”

Grey Jean Technologies marries artificial intelligence with customer and payment data to generate accurate predictions of consumer behavior that ultimately focus on driving sales figures. The New York City-headquartered company uses its proprietary AI-powered recommendation engine called Genie to quickly merge existing data sources with more than 500 consumer behavioral attributes to identify what patterns connect consumers with specific products. These consumer insights are used by marketers and executives to engender ways to enhance interaction with consumers and ultimately improve sales.

“The right recommendations serve as a valuable discovery mechanism that connects customers with the content and products they actually want,” said Alberino. “ECommerce personalization helps retailers meet their customer’s needs more effectively and efficiently, making interactions faster, easier and more satisfying — encouraging repeat purchases and creating loyal customers.”

Back in 2015, conversations about the business began to swirl between the two founders, Cosmas Wong and Alberino, as a way to apply their combined experiences of working with Big Data in the financial and retail sectors. Grey Jean launched in May 2016 at the Shoptalk Conference, where Genie was unveiled, along with the work the firm was doing with its first two clients: Hiro Sake, a handcrafted premium spirit company, and Namco Pool, one of the largest dealers of swimming pools.

“Predicting what shoppers will buy next has long been a dream for retail marketers, but very few have had the technology to pull off that vision,” said Alberino. “While many advancements have been made over the last 20-plus years, true one-to-one, real-time personalization is still a rarity in retail, due to its historical struggle with lack of data, followed by too much unorganized data.”

Alberino said that, through advances in artificial intelligence and machine learning, retailers have the ability to harness the power of their data to predict consumer purchase behavior and more effectively target those customers based on their unique preferences and behaviors. And, over time and through more data, Genie’s predictive algorithms inspire better recommendations, which, in turn, is an increased benefit to retailer clients that are looking for a slew of outcomes.

“Genie has demonstrated a 72 percent accuracy rate in predicting a next likely purchase at the category level. This accuracy enables our customers to improve personalization and micro-targeting but also take actions that will be the most effective with each consumer,” said Alberino. “This results in more conversions, higher redemption of coupons and promotions; increased visit frequency, foot traffic, time in store and basket size; and greater brand affinity.”

Alberino gave the example of one of its new clients that has already achieved some quick success. Pure Green, a New York City-based juice bar, had Grey Jean provide insights into its customer base as it sought to expand the number of its retail locations. First, there were three locations, but the hope is for 30 by the end of 2018.

“Leveraging Genie has allowed Pure Green to have a greater understanding of their consumers’ demographics, preferences and behaviors, helping them identify who their target audience really is and, subsequently, where they would have the most success with a new storefront,” said Alberino.

Ultimately, more time and more data only helps this type of technology. The company said that, each passing year, Genie’s algorithms get stronger and more refined. And it helps that more people are shopping digitally, where AI can easily live.

“We plan to stay ahead of the curve by continually integrating new attributes and data points that keep Genie the most accurate recommendation engine for predicting consumer purchase behavior,” said Alberino. “We also want to help shape retailers’ understanding of AI, which is currently used in many ways within the market.”

While some retailers currently use AI technologies — such as natural language processing — for sentiment analysis, Alberino said that this application alone will not give the full picture of what retailers are looking for in terms of consumer shopping behavior. While it may be helpful to know how a consumer feels about an ad or product, retailers also need to understand what drives purchasing.

“Focus on using artificial intelligence to drive actions, rather than just delivering insights,” said Alberino. “We actually help retailers take an action with their customer, rather than just presenting their data in new ways and leaving them wondering what to do with it.”

How artificial intelligence is transforming retail personalization

ai-jpg__640x360_q85_crop_subsampling-2Retail personalization is certainly not a new concept, but there are many new advances in personalization helping retailers create deeper relationships with shoppers through meaningful, relevant and contextual experiences.

Personalization through the years

Ten years ago, Amazon and Netflix were the poster children for personalization. Amazon was commended for its ability to show different home pages for different customers based on their past clickstream paths or previous purchase behaviors. Other retailers’ personalization efforts simply greeted returning customers by name or enabled them to save website preferences. Then there were those that took a one-to-many approach, such as versioning their site for entire segments of visitors.

These rudimentary approaches are now considered table stakes for today’s retail marketers. Consumers are far savvier than they were ten years ago. They not only want personalization, they expect it!

Today’s retailers are rising to the occasion. Retail personalization is having a major resurgence, thanks to the proliferation of big data, as well as the implementation of machine learning across distributed platforms.


Stitch Fix’s AI success

Some retail organizations have built entire businesses around AI. Take Stitch Fix, for example. The styling subscription service uses AI to tailor clothing and accessories to busy women’s personal tastes, budgets and lifestyles. Stylists work with a team of more than 60 data scientists to choose tailored items for each shipment. By applying machine learning to the process, the computers become smarter as they handle more and more data.

This strategy of marrying humans and machines has been wildly successful for Stitch Fix. Over 80 percent of clients return within 90 days for a second order, and a third of clients spend 50 percent of their clothing budget with the subscription service.

While we’ll continue to see businesses in and out of the retail space launch with AI at the core, this approach certainly won’t make sense for all retailers. Instead, established retailers can apply AI to different business units. Recently, Macy’s announced its customer service unit is testing a “mobile companion” tool using AI. The tool enables shoppers to get answers based on the store that they are physically shopping in rather than having to find a sales associate. While this is a great way to leverage AI to engage with current shoppers, what about using the technology to acquire new ones?


AI’s role in marketing

Applying AI to marketing not only helps retailers acquire new customers, but also encourages repeat business. As recommendations and offers become more tailored, shoppers’ loyalty will continue to deepen as they associate the brand with personalized and relevant experiences. For retail marketers that want to stand out from the hundreds of advertising and marketing messages shoppers see on any given day, AI-driven marketing is a must.

AI enables marketers to harness powerful algorithms to find patterns in internal and third-party data, and then look for repetitions in these patterns. One of the core underpinnings of AI that is transforming retail personalization is machine learning. Stated very simply, machine learning is about solving problems using probability and statistics. Used in the context of personalization, machine learning can continually adjust the data sets until the right marketing message for each individual shopper is presented at the moment, and through the channel that matters most.


This can all sound very daunting for marketers. For those just getting started in AI-powered personalization, keep the following in mind:

1. Don’t be overwhelmed by data. Many retail organizations have data everywhere and have no idea how to consolidate it, let alone make sense of it all. If you don’t have internal resources to organize disparate data sets, simply outsource this important task to a personalization technology partner.

2. Pull in as much third-party data as possible. From POS to loyalty data, retailers have a lot of information about customers. But to get a true 360-degree picture, it’s important to pull in as much third-party data as possible. I’m not talking simple demographics here (though that’s helpful as well). Your data combined with factors such as location, time, social media activity and price sensitivities can really make the difference in understanding how, why and when customers shop, as well as the right purchase triggers so retailers can present the most relevant marketing messages.

3. Determine the best output channel. Many retailers have seen the power of email personalization for driving traffic and sales to their stores, both online and off. But there are many other channels where personalization works well — in particular mobile. As we saw in the Macy’s examples, mobile can be used to augment in-store service, but it’s also a great vehicle for timely promotions and offers for shoppers on the go. In addition, the emergence of AI-powered chatbots is something to keep an eye on, as retailers look to follow their audience onto the appropriate channels, including social.

You don’t have to be a personalized subscription service like Stitch Fix to provide personalized service to prospects and customers. A huge impact can be made by applying AI to one area of your business at a time — from marketing to customer service to merchandising. A once over-hyped technology, personalization has arrived and is the future of retail thanks to the power of artificial intelligence.


Originally published on Retail Customer Experience