5 applications for AI in retail

Retailers are using a number of AI-powered solutions to help them gain efficiencies, increase profitability and better serve customers, according to Dave Thompson, founder and CEO of AI consulting firm 3 Leaps LLC.

These include goal-seeking agents that look to identify and implement decisions to produce beneficial outputs; and systems that leverage external data and machine-learning techniques to provide recommendations and automated decisions.

Additional AI opportunities in retail include loss prevention, payment fraud detection, in-store theft prevention, food service waste reduction, merchandising, price and product optimization, assortment optimization, short life clearance, marketing, personalization, customer response prediction, advertising vehicle optimization, supply chain management, and out-of-stock avoidance, Thompson explains.

“The largest impact for many retailers has been in supply chain optimization, which could cover anything from replenishment to assortment planning, depending on the specific retail vertical,” Thompson says.

“For retailers with a multi-channel strategy, the priority may be to help the retailer better understand the benefits and costs of complex fulfillment options like ‘order online, pick up in store’ or to consider multiple delivery strategies.”

With so much of retail going online, one of the most important tasks of AI investments is brand awareness – helping retailers understand how consumers become aware of their products in the first place, or helping a customer recognize a product as being from that retailer.

Alternatively, the goal might be to recognize occasions that might be relevant to their products so that retailers can market them accordingly. What is needed is the ability to better understand new sales opportunities, not through traditional research techniques such as surveys, but by actually listening to consumers as they’re engaging online in different channels.

“We have seen an evolution of personalization, or the matching of potential consumers to products designed just for them,” explains John Dubois, consumer and retail AI leader at Ernst & Young (EY).

“You see a lot of work being done using AI to engage with customers via email campaigns, or with digital retargeting. A lot of that is using machine learning to essentially predict the effects of the outreach. There has also been a lot of work using machine learning to do churn analysis and predict various merchandising trends and intentions around consumer buying.”

This has been relatively hard in the past, Dubois explains. “But if you start to examine engagement from consumers, either to a brand or to their peers, as a strategic data set, and then apply AI to add instruction to it, you can start to get at things like ‘how is it that I am seen as different,’ or ‘am I truly getting people’s attention?”

In addition to aiding in awareness, AI also helps retailers understand how their products are being consumed, or how customers rate interactions with the firm. That enables retailers to answer the questions, ‘How can I give that experience a higher likelihood of enjoyment,’ or ‘How can I make it something that they’ll want to consider me again?’

AI technology can also improve supply chain management, which was a major goal of AI investments by agriculture and retail firm Land O’ Lakes, which is probably best known for its butter products.

Land O’ Lakes had for many years relied on a legacy computer system that had evolved into an extensive and expensive beast to manage. The system relied on a variety of tools from numerous vendors and needed many skillsets that were hard to find in the job market. When the pandemic hit the U.S. economy hard, it only made matters worse in trying to maintain those systems.

The firm decided to invest heavily in automation technologies, and to use AI applications to better tie together its sales, marketing and e-commerce efforts. The goal was to streamline the supply chain process and ease the flow of data among employees and products to customers.

Leading firms know to act quickly in times of crisis, and Land O’ Lakes built a new system in less than 30 days.

The company completely transformed its e-commerce and supply chain management processes, and in the process, increased productivity by 25%. The company now uses AI and automation at scale to offer its 2,500 farmers, 1,000 retail partners, and 10,000 employees real-time purchase information on their e-commerce platform along with shipping and tracking data. The supply chain holds 50% of all transaction volume and reuses data 30% more data between applications — resulting in a more integrated, transparent, and scalable business.

Other firms are quickly realizing that the way consumers interact with retail brands is really changing and AI can make for a much faster and smoother experience, explains Simon Marchand, chief fraud prevention officer at Nuance Communications.

“They use multiple channels and multiple devices, even within the same interaction. But they still expect to make an experience that is really fast,” Marchand stresses. “Seamless and consistent is really important. When we contact the company, especially when we do business with it regularly, we want to feel recognized, welcome, and secure. And then we want to get our business done with minimal friction.”

This desire to improve the customer experience is a top benefit from AI investments for many firms. With so much product inquiry and ordering being done online, it is important that the experience can be done quickly and efficiently. Human involvement is often not practical, so retailers are dependent on technology applications that can “learn” the firm’s products, their features, and ordering, inquiring and return processes that are needed to drive today’s sales. AI and machine learning applications enable that process.

The big challenge here is to develop an AI process that to the customer doesn’t seem like a robotic program. While they may not interact with a human throughout the process, they don’t want to be reminded they are merely using a software program.

“When we use AI in customer engagement, it should be warm and engaging, so that the customer feels that we’re still part of the process, and they feel we’re a partner on the journey,” Dubois explains.

Finally, e-channel retailers are especially interested in tools to automate very rapid competitive responses – what is often referred to as dynamic pricing. While the basics of such competitive indexing are rules-based, the approach often requires weights or strategy inputs built from various AI/ML processes to finalize responses.

To find out more about artificial intelligence in the retail sector, download our EBook – 'AI in Retail: Reinventing customer relations'

Retailers are using a number of AI-powered solutions to help them gain efficiencies, increase profitability and better serve customers, according to Dave Thompson, founder and CEO of AI consulting firm 3 Leaps LLC.

These include goal-seeking agents that look to identify and implement decisions to produce beneficial outputs; and systems that leverage external data and machine-learning techniques to provide recommendations and automated decisions.

Additional AI opportunities in retail include loss prevention, payment fraud detection, in-store theft prevention, food service waste reduction, merchandising, price and product optimization, assortment optimization, short life clearance, marketing, personalization, customer response prediction, advertising vehicle optimization, supply chain management, and out-of-stock avoidance, Thompson explains.

“The largest impact for many retailers has been in supply chain optimization, which could cover anything from replenishment to assortment planning, depending on the specific retail vertical,” Thompson says.

“For retailers with a multi-channel strategy, the priority may be to help the retailer better understand the benefits and costs of complex fulfillment options like ‘order online, pick up in store’ or to consider multiple delivery strategies.”

With so much of retail going online, one of the most important tasks of AI investments is brand awareness – helping retailers understand how consumers become aware of their products in the first place, or helping a customer recognize a product as being from that retailer.

Alternatively, the goal might be to recognize occasions that might be relevant to their products so that retailers can market them accordingly. What is needed is the ability to better understand new sales opportunities, not through traditional research techniques such as surveys, but by actually listening to consumers as they’re engaging online in different channels.

“We have seen an evolution of personalization, or the matching of potential consumers to products designed just for them,” explains John Dubois, consumer and retail AI leader at Ernst & Young (EY).

“You see a lot of work being done using AI to engage with customers via email campaigns, or with digital retargeting. A lot of that is using machine learning to essentially predict the effects of the outreach. There has also been a lot of work using machine learning to do churn analysis and predict various merchandising trends and intentions around consumer buying.”

This has been relatively hard in the past, Dubois explains. “But if you start to examine engagement from consumers, either to a brand or to their peers, as a strategic data set, and then apply AI to add instruction to it, you can start to get at things like ‘how is it that I am seen as different,’ or ‘am I truly getting people’s attention?”

In addition to aiding in awareness, AI also helps retailers understand how their products are being consumed, or how customers rate interactions with the firm. That enables retailers to answer the questions, ‘How can I give that experience a higher likelihood of enjoyment,’ or ‘How can I make it something that they’ll want to consider me again?’

AI technology can also improve supply chain management, which was a major goal of AI investments by agriculture and retail firm Land O’ Lakes, which is probably best known for its butter products.

Land O’ Lakes had for many years relied on a legacy computer system that had evolved into an extensive and expensive beast to manage. The system relied on a variety of tools from numerous vendors and needed many skillsets that were hard to find in the job market. When the pandemic hit the U.S. economy hard, it only made matters worse in trying to maintain those systems.

The firm decided to invest heavily in automation technologies, and to use AI applications to better tie together its sales, marketing and e-commerce efforts. The goal was to streamline the supply chain process and ease the flow of data among employees and products to customers.

Leading firms know to act quickly in times of crisis, and Land O’ Lakes built a new system in less than 30 days.

The company completely transformed its e-commerce and supply chain management processes, and in the process, increased productivity by 25%. The company now uses AI and automation at scale to offer its 2,500 farmers, 1,000 retail partners, and 10,000 employees real-time purchase information on their e-commerce platform along with shipping and tracking data. The supply chain holds 50% of all transaction volume and reuses data 30% more data between applications — resulting in a more integrated, transparent, and scalable business.

Other firms are quickly realizing that the way consumers interact with retail brands is really changing and AI can make for a much faster and smoother experience, explains Simon Marchand, chief fraud prevention officer at Nuance Communications.

“They use multiple channels and multiple devices, even within the same interaction. But they still expect to make an experience that is really fast,” Marchand stresses. “Seamless and consistent is really important. When we contact the company, especially when we do business with it regularly, we want to feel recognized, welcome, and secure. And then we want to get our business done with minimal friction.”

This desire to improve the customer experience is a top benefit from AI investments for many firms. With so much product inquiry and ordering being done online, it is important that the experience can be done quickly and efficiently. Human involvement is often not practical, so retailers are dependent on technology applications that can “learn” the firm’s products, their features, and ordering, inquiring and return processes that are needed to drive today’s sales. AI and machine learning applications enable that process.

The big challenge here is to develop an AI process that to the customer doesn’t seem like a robotic program. While they may not interact with a human throughout the process, they don’t want to be reminded they are merely using a software program.

“When we use AI in customer engagement, it should be warm and engaging, so that the customer feels that we’re still part of the process, and they feel we’re a partner on the journey,” Dubois explains.

Finally, e-channel retailers are especially interested in tools to automate very rapid competitive responses – what is often referred to as dynamic pricing. While the basics of such competitive indexing are rules-based, the approach often requires weights or strategy inputs built from various AI/ML processes to finalize responses.