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The Biggest Changes AI Can Make For Retailers

This article first appeared on Forbes.

Retailers are always on the lookout for ways to make themselves more competitive — trends in advertising, social media channels to leverage, new takes on email and online marketing.

Particularly for smaller businesses, the focus is often on initiatives that could result in fast ROI, and this tends to mean efforts explicitly aimed at increasing sales volume. For larger chains, it can mean a focus on expanding into new markets or opening new locations. The problem is that these efforts take time and attention away from the simplest way retailers could increase both their short-term success and their long-term ability to compete: implementing AI-powered automation.

This is especially important in the current retail climate, where customers are shopping online more than in person. Forward-thinking executives, marketers and data scientists have been warning retailers that AI is an “adapt or die” prospect for years, but many companies have yet to take up the challenge. Although the huge e-commerce wave brought about by Covid-19 prompted some retailers to take action, too many thought that the retail landscape would “return to normal” within 12 to 24 months. Retailers and distributors who have already leveraged AI are more competitive than those who have not. Now that we know retail has changed forever, it’s time to act.

It all starts with product data, which is often the biggest pain point retailers face. Luckily, there are ways that AI can save virtually any retailer or distributor massive amounts of time and energy and allow them to compete against some of the largest big-box stores and e-commerce giants. 

Handle The Basics: Structure And Taxonomy

It doesn’t matter what industry you serve, how large your company is or how sophisticated your other technologies are; without adequate data structuring, AI can never reach its full potential for your business. Data structuring is the bedrock of every digital transformation and every push forward in innovation. We always suggest data structuring as a starting point when implementing any AI solution. 

But where does this process begin? First of all, it begins with the elimination of backlogs of unstructured data, which are estimated to be growing at a rate of 55% to 65% every single year. The problem with unstructured data is that it can’t be leveraged to power AI solutions or to deliver any important insights; it just sits there, taking up space and adding nothing of value to the business.

A retail-specific example of this problem is in data like product attributes and purchase orders. Many retailers receive this data in a plethora of disparate formats, and while they may be managing their inventory fairly accurately with this unstructured data, they could use it to improve customer experience if they structured it. By adding categories, tags, descriptions and a consistent format, retailers can keep their catalogs up to date and make them far easier to search, leading to better product discoverability and faster order fulfillment.

The next step is simply to implement a solution that maintains these structures and taxonomies going forward. There are fast and accurate AI solutions to this problem that can help eliminate outsourcing and the use of content teams for rote work like tagging.

Retailers may wonder how to establish the best taxonomy and structure for their unique sets of data. The answers lie in the same research that most retailers are already doing to dial in their marketing. Tags, descriptions and categories should be based on how customers search, what they purchase and the data that cements their decision to buy.

An example we often use is one we encountered working with a home improvement retailer. It was struggling with product discoverability for ceiling fans, but it was fairly simple for the retailer to identify how it could be improved. Customers shopping for ceiling fans were most interested in a specific set of data — indoor/outdoor suitability, size, color and hang length. Surfacing data like material, blade number and part number was unnecessary and potentially confusing for home improvement customers. Once the retailer applied this customer taxonomy and search pathway, discoverability improved and purchases climbed.

What Follows: All The Benefits

• Improved process for suppliers. Product data is a pain point for suppliers, too. Their clients need them to input product data in specialized vendor portals or in endless Excel spreadsheets, but suppliers rarely have time, and they rarely have their product data in the correct format and taxonomy for dozens or hundreds of clients, many of whom have specialized taxonomies. In an AI-powered system, suppliers dump their product data in whichever format and taxonomy they happen to have and let AI do the rest.

• Fewer cumbersome dependencies. The top goal in implementing AI is to reduce dependencies on outside resources. Many retailers and distributors hire third-party agencies (BPOs) to complete tasks like content validation, data enrichment, organization into custom taxonomy and editing. Unfortunately, despite the expense, companies find that their data processing through BPOs takes too long and isn’t high quality. Just as AI can automate the onboarding process for suppliers, it can handle these tasks that retailers and distributors are usually responsible for, limiting outsourcing.

• Scaling becomes a possibility. Suppliers can quickly onboard vast numbers of products, growing the retailers’ or distributors’ catalog exponentially.

• Increased speed and quality. For any business trying to compete in a rapidly changing, unpredictable economy, speed and efficiency are the key deliverables needed from automation solutions. Although most executives are quick to embrace the speed of AI, they often remain concerned about accuracy on tasks like data completion and validation. However, there are fail-safes; retailers begin the automation process by reviewing small amounts of AI-processed product data. AI then incorporates feedback and learns from it until it reaches better-than-human levels of accuracy.

The Bottom Line

It is not too late for retailers to implement AI solutions that will help them in both the short-term and the long-term. The key is to start with the bedrock of AI implementation: data structuring. The benefits of automation follow from there.

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