3 AI and Machine Learning Use Cases in Retail

3 AI and Machine Learning Use Cases in Retail

10 years ago, Artificial Intelligence (AI) and machine learning seemed like they were futuristic concepts we would never be able to apply to the real world. But now, these technologies can be be applied to the retail sphere, both on- and off-line.

Unlike their popular depictions, AI isn’t always a robot; instead, it is a computer that can think and emulate human behavior. Within AI is machine learning, a specific kind of AI algorithm that lets the machine keep track of data, make predictions, and create solutions all on its own.

Some examples of how AI and machine learning could be integrated include:

  • Data-driven upselling and cross-selling
  • Chatbots that recommend choices to consumers based on prior purchases
  • Pricing engines for situational pricing

Already, 30% of major retailers are preparing to implement commerce platforms that integrate AI and machine learning into their operations. It seems like the future is inarguably here, but how does this look in real life?

Let’s take a look at some use cases…

AI and Machine Learning Use Cases in Retail

Rebecca Minkoff & Alexa

Rebecca Minkoff, a clothing retailer, wanted a way to use data collected online and offline to integrate customer information. This data could be used to better understand the company’s clientele and to influence many factors, both internally and in customer relationships.

The company found a solution in Alexa, a conversational AI solution developed by Amazon. At a recent conference, Uri Minkoff, the co-founder of the luxury brand, asked Alexa a question about the most purchased spring collection item – and he received an answer in one second.

Uri’s demonstration shows that Alexa is not only able to aggregate data and make recommendations, but also that it can share this information – which can be used in many ways – in real time.

Target & Pregnancy Prediction

Major retailer Target hired a machine learning expert to create an AI that could determine which shoppers were likely to be pregnant. The AI collected data about shopping habits from women who later registered with the company’s baby registry. The predictions indicated specific details about the status of a woman’s pregnancy, too, including trimester based on the prenatal vitamins a woman would buy.

However, this was also a case study in the need for caution in predictive analytics. Target used this information collected to send promotions to pregnant women – and ended up sending one to a 16-year-old girl whose father then found out about her pregnancy.

Ultimately, Target discovered its customers didn’t like that the company knew so much about them, and they later started sending less-targeted promotions

Walmart, Facial Recognition, & IoT Tags

In a bid to better satisfy customers, Walmart is rolling out a facial recognition software AI that can determine customers’ levels of frustrations when they come to the check-out counter. If a customer appears to the AI to be highly frustrated, then a customer service representative can speak to the customer, alleviating their annoyance and re-establishing their relationship with the store.

Further, Walmart has developed Internet of Things (IoT) tags for its retail and warehouse shelves. IoT enabled objects connect to the Internet, and, in this case, an AI to automate commonplace practices. These tags can help employees know when to restock items, monitor trends in popularity, and even check for expiration dates.

Why AI and Machine Learning?

Now that we live in a retail landscape where shoppers feel devalued and anonymous, especially online, creating a personalized shopping experience can set you apart.

Shoppers want to have an emotional investment when they shop, a feeling that their needs are being addressed.

To provide this experience, an AI can collect information from many spheres that users update, including social media, online shopping, chatbots, in-store purchases, and use the information collected to create and deploy personalized strategies for the consumer.

In a nutshell, AI and machine learning, perhaps counterintuitively, make the customer feel more human in the retail sphere. All shoppers want to be recognized as individuals, and AI and machine learning can help you create this experience at your company.

Using AI and Machine Learning at Your Company

The first step is deciding what benefit you want your AI to provide, which, in turn, means figuring out where in your company it will be deployed. Create objectives, like developing personalized incentives for customers, that can drive your AI decisions.

For example, Personali’s Intelligent Incentive platform uses customer psychology and emotion to encourage them to make purchasing decisions. Specifically, the algorithm uses customers’ emotional responses to suggest they make purchasing choices. The platform also considers users’ price elasticity and purchase probability to create an individualized experience with tailored offers for each shopper.

Next, you want to decide what the AI will do when interacting with customers. For example, you could have an AI that tracked all the interactions a customer had with your website or online presence. Or, you could create one that develops a micro-targeting plan for a specific sector of customers.

Last, AI is particularly well suited to make predictions. They are so accurate, in fact, that analysts suggest that their predictions will be up to 82% correct. All you have to do is decide what you want to know. AI data can be used for inventory, of course, but also for less obvious pursuits, like creating budgets and hiring.

The Take-Away

As you might expect, AI and machine learning can be invaluable for companies when used correctly. With these technologies, retailers are then better able to tell how a customer might act, which, in turn, helps them individualize shopping experiences.

Really, AI is already here. For example, AIs can already send personalized recommendations to customers via their smartphones and monitor the wait times of customers in line at a physical store. What’s more, retailers who are using these technologies have seen a 19% increase in operating margin over the last five years.

Only one question remains: what are you waiting for? 10 years ago, we thought that AI and machine learning were a dream for a distant future. But now, that future is here, it’s time to consider the most effective ways of integrating these technologies into your business.

To read more about the relation between behavioral economics and marketing,
and demonstrate how creative marketing organizations can leverage the fundamentals of behavioral economics
download our eBook: Behavioral Economics as a Key Marketing Driver. Contact us, today.

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