Practical Ways AI Can Improve the Retail Industry

There is no denying that artificial intelligence is disrupting the retail business automation is not only replacing lots of jobs but it is also making the retail business more efficient and profitable. This article looks at some of the practical ways Al can improve the retail industry.

These are interesting times for the retail business. On one hand, the production and logistical costs are skyrocketing while on the other hand, consumers are getting pickier and harder to please. Most retailers are struggling to adapt to the changing ecosystem, but those who have embraced technology have found the secret of thriving in a hostile business environment.

Retail businesses are now using artificial intelligence (Al) to automate processes as a means of improving their efficiency and conversions. Here are some practical examples of how Al can help to improve the retail industry.

1. Digital assistants

In 2016, Lowe introduced LoweBot assistants which can help customers as they shop for specific products. When interacting with a robot, customers will feel freer to ask all sorts of questions including some that they might be afraid to ask a salesperson. But digital assistants can do much more than just find products - they can also be used to carry out inventory tracking in real-time. Digital assistants will, therefore, improve the customer experience while at the same time helping the store owners with inventory control.

2. Price adjustment and prediction

Al can be used to determine seasonal trends and other factors that are known to influence the prices of goods. The data collected can then be analyzed and used to make relatively accurate price predictions. Predictive analytics can also be used to forecast the price points of products throughout the year. If customers had such an app, they would most likelybe very loyal to the brand. Companies like Kroger and eBay are already using Al tools to optimize their prices. For instance, they make appropriate promotions and adjustments based on the data collected.

3. Supply chain management

Supply chain management can make or ruin a business depending on the approach taken. Thankfully, automation can help to make the logistics process efficient, easy and inexpensive. Automation can help to eliminate bottlenecks in the supply chain that result in huge losses. For instance, Al can be used in network planning to ensure inventory doesn't lag demand. When a company knows what they expect, it can allocate more resources where a higher demand is expected. Smart warehouses that are completely automated can also be adopted. Apart from simplifying otherwise tedious processes, smart warehouses will also reduce operational costs.  A real-life example of how Al can improve logistics is the use of GPS tools by drivers.

Drivers can use a GPS tool for taking the most cost-effective routes. The GPS tool would optimize the routes based on traffic conditions, the distance among other factors.

4. Virtual dressing rooms

The thrill of picking up clothes randomly is ebbing away, especially with the growth of e-commerce. The modern-day shopper wants to know how well the clothing will fit them before they even try it on - and that's where virtual dressing rooms come in. The obvious application for virtual dressing rooms is for fitting clothes, but this technology can be applied in lots of different niches. For instance, you can upload a photo of your apartment to see how the furniture you intend to buy will fit in, or upload your selfie to find the best pair of glasses. Virtual dressing rooms will make it possible to get the perfect items without having to visit the brick-and-mortar store for a fitting. Buyer remorse will be eliminated because shoppers will buy what they really want and not what a customer sales representative convinces them to buy.

5. Prediction of customer behavior

Customers want to be treated as individuals and not just some statistics. One sure way of achieving this is using AI to predict customer behavior and customize a unique shopping experience for them. Behavioral economics can be used to analyze the emotions and psychology of a customer in order to make relevant product recommendations, upsells and cross-sells. The behavior of the customer can also inform the algorithm of the optimal offers to suggest.

During the customer acquisition stage, predictive analytics can also be used to predict which customer is likely to make a purchase. Such customers can then be forwarded to a dedicated salesperson who can guide them through the purchase process.