The Challenge Of Online Product Discovery

As the pandemic drags on, one consistent trend is that people are doing more shopping online. In the United States, e-commerce sales are up by a minimum of 20 percent across the board. This trend looks to continue long after the pandemic subsides. Perfecting the product discovery process is essential as consumers navigate different websites to find their desired products. While several retail-tech businesses have invested lots of cash and years of research helping buyers find what they expect when they want it, a broad series of merchants are now devoting even more focus to this scientific research. For several, making it as appealing as browsing a store is the utmost objective.

Discovery consists of both retailer-provided referrals as well as searches conducted by the consumer. Some shoppers don’t know what they are looking for specifically and hope to stumble across the ideal product as part of the search process. Others know what products they’re looking for precisely and trying to locate that item within hundreds of products in a given retailer’s assortment. Whatever the technique, the process is about to undergo an AI-fueled transformation.

The online search process tends to be old-fashioned and stiff. Online purchasing platforms were purpose-built to fulfill a function. Now, the search process experience needs an overhaul to fill the gap left by in-store shopping. The challenge facing digital retailers is making the discovery process fun and enjoyable to make it possible for a frictionless purchasing trip.

Some retail-tech firms have developed search tools in-house to customize the search experience. One example is the luxury discovery platform Lyst, whose employees consist mainly of data scientists and engineers that have spent years developing systems that can automatically analyze, identify and organize the selection of millions of items from 12,000 brand names. Other retailers are using a mix of proprietary algorithms offered by big tech companies like Google and Shopify.

Nailing the search process and product discovery can help boost conversions as well as loyalty. According to retail-tech firm Vue, the average order value can double if the product discovery process is seamless. Issues with customer attention and retention are issues with product discovery and customer preferences. The more you give the customer what they desire, the greater the chance that the customer will make a more significant purchase and then return to make additional purchases.

Challenges and Opportunities

Tiny screens on mobile devices coupled with expansive assortments can make online shopping more of a task, which is something that retail tech firms are looking to address. Having a large inventory selection online can be both a blessing and a curse. If a customer can’t quickly find what they’re looking for, then that customer will likely head to a competitor.

Lyst, which created its search and exploration engine specifically for fashion, can take things like seasonality, product, event, and design into account when providing search results. For example, a customer searches for a specific bag; then the engine needs to be more specific to distinguish between over-the-shoulder bags, waist bags, hip bags, micro-bags, etc. Sneakers have evolved into multiple categories that encompass luxury, performance, fashion, and sporting activities.

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More Intelligent Product Recommendations

Retailer product recommendations usually involve “individuals who purchased this item generally purchased these items as well.” It’s not a reliable process because it doesn’t consider the customer’s specific preferences or the intent behind the language used in searching. Likewise, these referrals do not work specifically well for fashion. Efficient suggestions for fashion companies take consumer history and context into consideration.

Gartner’s research has determined that customers prefer shopping with sites that include product recommendations or items that were recently viewed and are also more willing to spend more time browsing. Adore Me, an e-commerce platform, recently began testing Google’s recommender tool to make recommendations of comparable items to the one you are viewing. Click rates increased by 300%.

E-commerce platform The Yes assigns over 500 attributes to each of the products available and utilizes Google Vision to analyze product images. The company has four proprietary algorithms that personalize product recommendations specific to each customer based on their preferences and activity on the site. In addition to recognizing the content, innovative suggestion engines have likewise to remember the context and the individual’s intent.

Luxury resale platforms are increasing in popularity. ThreadUp offers millions of one-off items on its platform, and product discovery can be incredibly challenging. Pinpointing the ideal product can sometimes feel like searching for a needle in a haystack amongst that amount of inventory. ThreadUp catalogs each item as they receive a range of attributes such as brand, size, color, category, and design information. As customers shop, ThreadUp tailors the messaging specific to each customer and uses machine learning to make product recommendations to improve the product discovery process.

The Yes has been perfecting a system to have customers vote on products to understand their style and personal preferences. Merely asking the customer to define their style wasn’t practical because a customer might use a word with multiple meanings. If they liked a particular image, asking customers wasn’t effective because it was confusing to the customer about what information The Yes was after.  The Yes determined that showing three photos of a similar look gave customers the ability to provide accurate information.

What’s On The Horizon

If a customer is searching for a specific product, user intent needs to be considered in advanced product searches to make the process more efficient. General search terms like “black outfit” can lead to an overwhelming variety of outcomes. Overly specific search terms can come up empty. Some mobile sites, stores may instead show previews, suggested searches, photos, or a purchase option to enhance the procedure. A customer that heads straight to the search bar generally knows what they specifically want. Is the challenge to reduce the clicks to purchase or to the most pertinent items? Does it make good sense to go straight to a product or show a curated selection of items?

One more challenge is creating referral engines that can adjust to changing prices and stock levels to prevent directing consumers to a dead end. Lyst includes countless updates to availability and rates in real-time. Making relevant substitution recommendations at the product page to mimic interacting with a sales associate in-store makes product suggestions.

Customers will continue to shop online long after the effects of the pandemic subside. And useful product discovery will be a vital component for competitive e-commerce sites. As with the pandemic’s many technological innovations, the importance of AI-powered product discovery will become even more crucial.

Conclusion

Un_Standard has a full range of solutions for businesses of all sizes to improve sales, increase customer satisfaction and get ahead of the competition. If you’d like to find out more, schedule a no-obligation 15-minute chat, click the following link and we’ll gladly show how we can make your business un-standard.

About the author

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David is both Agent Provocateur and Chief Executive Officer at Un_Standard. We create strategies for businesses of all sizes that improve customer relationships and help businesses grow. In his spare time, he loves to experiment in the kitchen, collect Scandinavian design, and chase after his two cats, Hallie Tosis and Lester Een.

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