6 Ways AI Is Making An Impact In Retail Tech

At Shoptalk 2018, we learned how tech companies are using AI to provide new solutions for some of retail’s biggest pain points. Here are some highlights from emerging players, big brands, and legacy platforms:

Identifying new real-time opportunities

At Breinify, timing is everything. The AI-powered tool analyzes customer data to predict and identify moments of purchase intent based off real-time events.

“Localized weather, events, and holidays have a huge impact on sales because it integrates real-life instances,” CEO and co-founder Diane Keng said.

Keng used the upcoming spring break season as an example. As the weather gets warmer, consumer needs will shift towards items like suntan lotion, or food and beverage purchases for barbeques. By understanding those needs before the demand, marketers can stay ahead of the curve and generate interest early.

“It’s reminding people that an event is coming, and then driving sales,” Keng said.

A more personalized approach

Oracle recognized the need for personalization early on, and worked to build a product offering that would help their customers better understand their consumers through robust data collection.

“The holy grail for a lot of retail businesses is personalization,” Katrina Gosek, of Oracle, said. “[Often] people make assumptions based on the marketing [data] they have, when they really should be using it to learn.”

Understanding demand for inventory planning

Afresh Technologies applies a similar theory of predictive intelligence to inventory management. CEO Matt Schwartz points out the unique difficulties retailers in the grocery sector face when it comes to accurately measuring demand and shelf-life for fresh food.

“Humans have outperformed in this category because of corner cases,” Schwartz said, noting that traditionally, grocery employees are the ones manually determining when to take items off the shelves.

Afresh Technologies’ AI system analyzes billions of data points for more accurate demand forecasting and product recommendations.

“What we’ve come to find is [understanding] demand leads to better inventory,” Schwartz said.

Organizing for product discovery

Spoon Guru puts the customer first in their approach to inventory management. Their algorithm analyzes products and recipes based on nutrition, and categorizes them by dietary restrictions, and popular search terms. Spoon Guru’s consumer-facing app then lets users create their own profiles based on their eating habits, and matches them to recipes or products that fit their individual needs.

“Retailers are struggling to keep up with demand,” Markus Stripf, CEO and co-founder, said, noting the rise in new nutritional trends. In this case, AI can help marketers make better product recommendations and guide shoppers using the app in-store.

Creating a community

TokyWoky, a community-based chat platform, invites users to share product recommendations and feedback in real-time. Companies can then use the tool’s NLP capabilities to identify customer pain points, map trends, and identify need.

The chat is fueled by real responses, from real customers — a different approach from chatbot automation.

“I think it’s the whole trust thing is what makes it so engaging,” Quentin Lebeau, co-founder and CEO, said.

Redefining the supply chain

At Johnson & Johnson, AI is used to organize the supply chain process — something Neil Ackerman, senior director, global supply chain advanced planning and innovation, says is even more important due to growing real-time demand. 

“Never before has supply chain had so much of an influence,” he said.

Ackerman believes AI can help play a role in managing large quantities of data, which aids the human process.

“AI is not artificial intelligence — it’s real intelligence,” Ackerman continued. “…It’s humans interacting with machines to make the best outcomes possible.”

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