Jan 10 2020

How AI Can Optimize Discounts and Drive Customer Engagement

Artificial intelligence tools can help retailers to get the right deal in front of the right customer at the right moment.

It’s nearly impossible for human strategists to keep up with buying trends and constantly survey the marketplace for opportunities to showcase inventory. Providing a regular rotation of discounts to loyal customers can be more challenging than it seems.

That’s why many retailers are turning to artificial intelligence and machine learning solutions to help them streamline the rollout of discounts and promotions.

“Dynamic pricing strategies work well for products that are in high demand at certain times (like Saturday night with Uber) and for seasonal products,” writes Susan Guillory, president of Egg Marketing, on the Intuit QuickBooks website. “The force that drives dynamic pricing is one of perceived value — the worth a customer assigns to your product.”

Many consumers are already savvy enough to swoop in at just the right moment to snag the best deals — waiting for discount codes to arrive in their email inboxes via retailers’ newsletters, for example, then purchasing items in their online shopping carts.

As a result, retailers struggle to motivate additional buying behaviors.

AI Knows How to Set the Best Price

An intelligent algorithm can set optimal pricing for a retailer based on historical and competitive data, seasonal trends and patterns of online customer behavior.

Mastering a dynamic pricing system can allow midlevel employees to delegate their rote tasks to AI, freeing them up to make more strategic business decisions and helping to drive profits.

AI can help retailers with dynamic pricing based on engagement, reports Retail Customer Experience. While a less intelligent system may not be able to handle a single item being offered to different customers at a different price, an ML-backed web store can do this with ease.

If a customer is shopping on a retail website with a 20 percent discount code in the system, or a chunk of loyalty points afforded to them by their past purchases, they’re often looking to buy a product that feels like it “should” be at a higher price point. Therefore, it’s important for a buyer to see the original, higher price — to help them feel like they’re getting a bargain.

In order to configure these prices, a system must analyze large swaths of user data from the website, in addition to behavioral data gathered by other systems.

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How Retailers Can Get Started with AI and ML

So, how can a retailer best begin dynamic pricing through machine learning?

For one, an offer management platform is an AI-driven tool that helps retailers conduct and leverage complex data analysis in real time. According to Gartner, AI-powered price optimization through an OMP increases revenue by 1 percent to 5 percent, on average.

The tool can also boost profit margins by 2 percent to 10 percent, and can eliminate 80 percent of discount approvals.

Though many retailers understand the promise of AI, not as many have deployed the technology to optimizing discounts. Any OMP needs about a year’s worth of data to start making informed changes to a retailer’s dynamic pricing, and a majority of the companies working toward that point are still in the data gathering stage. In addition to highly useful data, retailers need supporting infrastructure and a well-trained team to power and distribute insights.

AI will likely soon become a routine part of the customer experience. This will make for happier customers, and will also support those tasked with sales forecasting, lead scoring and product development. 

Once a central team has been trained to learn from, and even enhance, a retailer’s AI system, the data that strategists gather and analyze will make everyone in retail better at their jobs. That’s not just an improved customer experience — that’s potentially seismic in the industry.

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