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The Future of retail: AI’s role in predicting customer needs

Smart shelves, automated inventory management and dynamic pricing algorithms have greatly improved retail technology. However, not all AI technologies are worth the investment.

5 min read


Customer paying at register

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In recent years, artificial intelligence has significantly enhanced the capabilities of various technologies in the retail sector, improving the way businesses interact with their customers. AI has become embedded into such key technologies as smart shelves, automated inventory management and dynamic pricing algorithms. As part of these systems, AI allows businesses to anticipate and respond to customers’ needs before explicitly expressing them.

That said, there’s also a need for caution when companies adopt AI. Not all of these technologies will be worth the investment, and given the experimental nature of some of these systems, there’s a very real danger that they could backfire and scare customers away. With that in mind, here’s how I think retailers should begin integrating AI while also being mindful of its potential pitfalls.

AI for predictive analysis

One area in which the genuine potential of AI shines is in predictive analytics. Through analyzing customer purchasing patterns, demographics and seasonal purchasing trends, businesses can optimize their sales strategies, inventory management and marketing campaigns.

For instance, an AI-integrated self-checkout kiosk can analyze scanned items and a customer’s purchasing history to offer the customer real-time suggestions for complementary, discounted, or trending products. This improves the opportunities for upselling and cross-selling and ultimately benefits the customer as well, provided the suggested products offer them genuine value.

Another use case for predictive analytics is forecasting demand through historical data, weather forecasts, and upcoming events. For example, if there’s a local marathon coming up, the AI algorithm can anticipate a surge in demand for fitness-related products, allowing retailers to ensure their inventory is stocked up ahead of time to fully capitalize on this opportunity. To further enhance operational efficiency, businesses can automate this process, providing stock purchasing orders that retailers simply need to sign off on.

Ultimately, how well each business can use AI through predictive analytics will likely determine that business’s ability to stay ahead in the market. While predicting customer purchasing trends and preferences through data analytics has existed for many years, we’re still in the early stages of fully harnessing AI’s potential to make more informed decisions. As AI technologies advance, those businesses that prove the most adept at incorporating predictive analytics will likely establish a significant competitive advantage.

Building loyalty through personalization

Beyond predictive analytics, the next area in which AI has a lot of potential in the retail space is through personalization. Many customers today – especially millennials and Generation Z shoppers – expect personalized experiences when interacting with retailers. This can take the form of product recommendations, loyalty programs and personalized greetings, all of which can emerge from data analytics. For instance, one approach that’s becoming more common is the use of cameras with facial recognition technology to identify returning customers and greet them by name.

However, this is where caution is warranted. Some customers may feel uncomfortable when a vending machine or self-checkout kiosk addresses them by name. It’s not at all certain whether this kind of personalization will catch on or be a step too far. This is why I would advise retail owners to carefully assess these technologies before throwing money at them. Ask yourself whether these personalized features will genuinely enhance the customer experience or if they will put customers off.

And, if you do choose to integrate AI personalization technologies, be careful with how you roll them out. Begin with a limited launch and gather feedback from customers on how they feel about personalized greetings and product recommendations. Also, be transparent about the data you’re collecting and how you use it to personalize the customer experience. Customers should have the option to opt out of data-sharing programs and be able to receive assurance that you will never share or sell their personal data to third parties.

Looking ahead

I predict that anticipating customer needs through predictive analytics will have a major impact on the retail sector. We already have the technology: any future advancements will be about making things more efficient, like providing product recommendations based on the time of day or ensuring continuous stock replenishment. Forecasting peak periods in advance will also be a huge help for management when designing work schedule plans and ensuring adequate staffing levels to handle busy seasons or times of the day.

As for incorporating AI technologies into your daily operations, my advice would be to consider how you’re going to use these technologies and whether they’re ultimately going to increase your profits. There are a lot of flashy AI technologies on the market now that might look interesting but likely aren’t going to be worth the investment. So think carefully about where you put your money.

Lastly, AI should be viewed as something to support, not replace, human workers. AI requires human oversight, and once you start replacing workers, that’s where you’ll likely run into problems with AI making mistakes or making things up.