If you still find yourself manually inputting data into spreadsheets and sifting through reams of tables and charts to extract the figures you need, rest assured there is a better way. Today, retailers are expected to handle and sort through vast quantities of data, whether that may be customer details, supplier information, or inventory levels. Not only must they ensure this data is accurate, but they need to be able to pull it all together if they are to gain full transparency across the entire supply chain and really understand what is happening with their business.

BI tools remove the data sorting burden and present accurate insights in user-friendly dashboards. Critically, this information can be viewed in real time, allowing businesses to react quickly to new information, such as adjusting prices, reallocating stock, or amending deals and offers.

General merchandise store Kmart Australia uses the latest AI capabilities alongside Microsoft Power BI dashboards and reports to better understand its customers and optimize its supply chain. The retailer’s data platform stores 60+TB of data and handles approximately 30,000 queries a day. As the flow of information grows, staff will still be able to easily see the information they need and create their own insights from stored data.

Simple analysis such as which product category generated the most revenue in a particular month, or what impact a promotion had on revenue, helps retailers identify general trends and consumer behaviors. But what about the less obvious insights that could help you really get ahead of the competition?

That’s where AI comes in. Through natural language processing and ML, AI can sift through reams of unstructured data including social media to identify trends before consumers themselves are even aware of them, whether it’s upcoming fashion styles, significant events, or innovations coming to the market. Companies can use these insights to predict demand for unlaunched products, shape new shopping experiences and refine their interactions with customers.

Some retailers are also exploring the value of emotion AI technology to learn more about how their customers feel about their retail experience and products. The technology can read visual cues like eye movements and facial expressions to analyze shopper sentiment.

You don’t need to be a marketing expert to know that well-considered and targeted product labelling, descriptions, and tags improve the chances of products being discovered on search engines like Google and Bing. But creating SEO-friendly, rich metadata for each of your products does require expertise, and time.

AI-powered product labeling and tagging can help. Retailers now use computer vision algorithms to automatically identify key product attributes and tag them. These algorithms can also be used to create SEO-ready titles and accurate product descriptions to ensure your customers find exactly what they’re looking for. Not only does this approach save on time and reduce the risk of human error, but it’s more likely to get your products to the top of the search engine results.

Did you know that wrong size, fit, and style are the cause of almost three-quarters of returns in fashion product categories? The good news is that retailers can take proactive steps to reduce these types of returns, for example improving online size guidelines, providing clearer product descriptions, and using technologies such as augmented reality to help customers visualize exactly how products will look on them – or models very similar to them – before committing to a purchase. In fashion, for example, AI is being used to model and style clothes on virtual models of all body types, ages and ethnicities. The aim: allow customers to visualize specific outfits, see them in different settings, angles and poses on models that look like them, and give them the confidence to buy – and hopefully keep – products.

Retailers are also using AI to refine and personalize product searches according to individual customer preferences. For digital pioneer Asos, AI means its customers don’t have to browse through thousands of products to find what they want and instead are offered up products that are personally relevant to them. The online fashion and cosmetics retailer uses business intelligence and machine learning technology to deliver unique recommendations from the approximately 90,000 products on its website.

Why do your customers abandon their shopping carts at the last minute? Have unexpected delivery costs put them off? Are they really ready to buy? Have they found a similar product for less elsewhere? Or does your online account sign up form take too long to fill out? Research firm Baymard Institute, who tracks the global average cart abandonment rate, found that currently, almost 70% of eCommerce shoppers add items to their shopping basket and then abandon their purchase. It’s costing the industry billions of dollars per year. AI has the capacity to bring this figure down.

AI-powered cart recovery tools assimilate masses of information, including individual purchase history, browsing habits and search queries to help retailers understand what factors might encourage customers to commit to buying. Perhaps your customers may be swayed by a 10% or free delivery code that pops up in real time as the customer hovers over the purchase button. Or maybe, what they need is a follow up e-mail with a discount or personalized product recommendation. These timely interventions can make the difference between winning or losing a sale.