How AI is shaping merchandising expectations

Keeping Pace with Tomorrow's Shopper: The AI Revolution in E-commerce

A decade ago, consumers were delighted when e-commerce deliveries came within a week. Then Amazon Prime changed the game. Now, 41% of consumers anticipate deliveries within 24 hours, and 24% want them in less than two hours.

Consumer expectation for ultra-fast delivery has been well documented, forcing retailers to revolutionize their shipping methods, revamp logistics, and dramatically change operations. Now, the retail world stands on the brink of another monumental change: rapidly changing consumer expectations.

In the near future, we predict that consumers will not only want but expect effortless product discovery. Websites with cumbersome navigation will be rejected, and dwindling attention spans will continue getting smaller. If brands don’t deliver, they’ll lose customers and market share.

Fortunately, cutting-edge merchandising solutions powered by artificial intelligence (AI) are empowering e-commerce retailers to meet and exceed these burgeoning consumer demands. They simplify the agonizing task of organizing collections pages, making it easy to feature the right products in the right places. With the help of these solutions, any e-commerce retailer can provide effortless product discovery and intuitive product recommendations.

**Tailored Recommendations: The Future of Customer Engagement **

Top-tier apps are conditioning consumers to anticipate spot-on product and content suggestions.

• Spotify recommends songs based on your listening history.
• TikTok’s “For You Page” presents content based on each user’s unique interests.
• YouTube knows that if you watch Quentin Tarantino movie clips, you’ll probably watch a podcast interview where he explains his writing process.

In each case, the app is recommending something you didn’t know you wanted — and it dramatically increases engagement. Similarly, e-commerce brands will soon be forced to function in similar ways. They will use a combination of personalization, conversion data, site behavior, and AI to deliver product recommendations with the highest chance of conversion.

Wayfair is a great example of an e-commerce brand already putting these concepts into practice. With a product catalog with 14 million items, how can the home decor giant deliver consistently recommend the right products? How does it know what each web visitor might actually buy? Its secret lies in a Bayesian system that evaluates multiple parameters such as top sellers, cart additions, order rates, how favorable web page positioning affects sales, and more. The system continues learning and iterating to refine and optimize its product recommendations. It’s pretty impressive. (Read a detailed explanation of Wayfair’s system here.)

Elevate User Experience

Search functionality has become table stakes for e-commerce retailers. Offering a simple way for consumers to quickly find products is essential. Naturally, the effectiveness of internal searches has improved over time, from 51% in 2000 to 83% in 2017.

Soon, however, consumers will soon be less inclined to use internal search at all. In fact, we predict that consumer ​​expectations will soon shift from: “I should be able to find something easily” to “_sites should automatically surface things I’m interested in.” _They'll seek immediate, relevant content, or they'll go elsewhere.

How can you compete? Follow the Netflix model. Despite having thousands of TV shows and movies in its catalog, Netflix delivers effective recommendations in the very limited space it has on its home screen. That’s not by accident. Netflix presents only the most clickable title art and tailors home screen selections based on product performance, user viewing history, product context, and how items appear next to one another. They also feature fun and intuitive sub-genre categories with niche groupings such as Steamy British Independent Dramas or Heartfelt Sports Movies.

Just like in e-commerce, the stakes are high. The streaming giant reported that users spent an average of 1.8 seconds considering each title, and that they’d abandon the platform if they can’t find something to watch within 90 seconds. E-commerce companies contend with similar issues like limited real estate on critical pages — and merchandising sites in optimal ways maximizes conversions.

The Solution: AI-powered merchandising

Many of these shifts in consumer expectation are already underway. Consumers are increasingly becoming impatient with inadequate e-commerce sites, legacy web architecture, and product recommendations that fall flat.

These expectations will only intensify.

To win in this increasingly difficult climate, brands must leverage AI-powered e-commerce merchandising solutions. Premium solutions automate the laborious task of optimizing e-commerce pages and grouping products together — while still giving merchandisers control over all aspects of their sites. These tools harmonize consumer desires with business objectives, ensuring product relevance, efficient stock management, and ultimately, higher sales. They solve complex problems such as promoting new products without penalizing top performers; ranking thousands of products when certain sizes or colors may be unavailable; or delivering product groupings by color, vendor, brand, and type — all while keeping the brand aesthetic strong.

If you’re still merchandising manually or you’ve got an inadequate solution, you’ll be in serious danger of losing market share to the competition.

Ready to not only meet — but exceed — consumer expectations both now and in the near future? Interested in an AI-powered merchandising solution that elevates product visibility and leads to repeat customers? Check out Entaice. We boosted Ramy Brook’s conversion rates by 13% and Curvy’s revenue per user by 28%. Check out our case studies for deeper insights.