Why AI-Powered Search and Recommendations are Key to Ecommerce Success
Jul 20 2018 | 07:00 PM | 9 Mins Read | Level - Intermediate | Read Mode
Mark Floisand Chief Marketing Officer, Coveo
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Mark Floisand is Chief Marketing Officer at Coveo. He has over 20 years of marketing, sales and general management experience in the technology industry, spanning blue chip and start-up companies across three continents, including Apple, Adobe, BusinessObjects, SAP, Total Defense, Untangle and WeVideo. Mark was most recently with Sitecore, a Coveo technology partner, where he led product marketing. Mark holds a Bachelor of Commerce degree from the University of the Witwatersrand, South Africa; and an MBA from the University of Durham in the UK.
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Amazon has reported that upselling and cross-selling makeup to 35% of their total annual revenue. To say that a personalized experience and contextual content recommendations are an integral part of modern e-commerce would be an understatement — effective upselling and cross-selling is how today’s e-commerce platforms remain profitable and continue to grow.
THE MODERN CONTENT MARKETER’S BUYER GUIDE
Welcome to the 2019 edition of The Modern Content Marketer’s Buyer Guide. About 10 years ago, marketers realized that content is a critical piece of their pie, and have since been working overtime to generate content to help win the prospect’s attention.
DownloadWhat makes upselling and cross-selling such a fundamental necessity in the e-commerce world? In most cases, research has shown that recommendation engines help customers to avoid two things they hate more than anything else: wasting time and making difficult choices. Organizations that can help alleviate these two basic consumer problems often see dramatic increases in conversions and customer satisfaction alike, which often yield major returns on investment.
In this post, we’ll cover the practical ways that your company can unlock the revenue-generating power of upselling and cross-selling on your e-commerce platform using AI-powered site search and recommendations.
1. Your Data is Your Bedrock
Sitecore xDB helps track visitor engagement to segment them into a buyer persona
Any well-calibrated artificial intelligence solution needs data in order to work effectively. Being able to properly record, quantify your usage analytics is critical in learning who your customers are, where they’re from, what they’re searching for, what content or products they’re engaging with most, their purchasing trends, as well as what they’re looking for at not finding.
Only with this information can you deliver a truly personalized e-commerce experience and an AI-powered search and relevance solution will help you scale your personalization efforts with ease.
Having such data will help you segment your visitors into buyer personas, saving them time navigating your site through targeted content recommendations. With this machine learning solution, you can go the extra mile and proactively recommend relevant results based on what you know about other visitors that engaged similarly.
Did somebody search for a video game on your e-commerce site? Odds that they are a gamer — meaning that when they search for a “console,” they probably want an Xbox or a PlayStation as opposed to a console for a server farm. Your data is critical to understanding customer context and intent and with it, you can make informed and relevant upsell recommendations that are more likely to convert.
2. AI-Powered Query Suggestions Begin Upselling Early
Searching for headphones on Bestbuy
Implementing intelligent search query suggestions, based on insights from your usage analytics — usually seen as a drop-down window when you start typing in a search box — is a great way to upsell your customers in a way that’s organic and provides value. Capturing your buyers in the research and exploration stage, before they have even landed on a product page, gives you the ability to mold their expectations from the onset and propose more expensive products that meet their needs.
For example, I was in the market for a pair of cheap, functional headphones so I went over to Best Buy’s website and typed in “headphones” in the search bar. If the very first recommendation was a cheap, decent pair of headphones, I would have likely checked out then and there, for around $19. Instead, wireless headphones were the first result.
This got me thinking: wireless headphones would be more practical for my morning runs, which is the original reason why I was looking for a new pair in the first place. Instead of buying the cheap headphones, I purchased the $240 wireless version, all because the query suggestions hooked me early on to a more desirable product. Imagine what it would mean for your e-commerce business if you could upsell your customers by 1163%, even just a fraction of the time.
3. Personalized Content Recommendations Make Cross-Sells Essential at Every Stage
Walmart does a mix of cross-selling and upselling on its Apple iPad product page
Product recommendations are the cornerstone of Amazon’s success in e-commerce: Amazon recommends new products at virtually every single stage of the buyer journey. Other companies, like Etsy, Best Buy, and Walmart, have also followed Amazon’s suit.
You might be familiar with Walmart’s “customers also considered” section, which uses a mix of upselling and cross-selling in order to increase the customers’ basket size. The Walmart product page for the Apple iPad, seen above, features one instance of upselling (the more expensive Apple 10.5-inch iPad Pro), and two instances of cross-selling (the Apple Pencil and the Mignova Premium Leather Case). In both cases, Walmart features a promotional element: either two-day shipping or a “reduced price” badge, both of which suggest that the customer is getting a good deal on their product.
Implementing a strategy like Walmart’s, and powering it through the analytics of your e-commerce platform’s search, means having the tools to boost your cross-sells and upsells drastically. Knowing what works and what doesn’t also really helps. Your usage analytics should always be feeding back into your search analytics in order to help your machine learning algorithms to learn what upsells and cross-sells work for a particular customer segment or product.
By identifying customer intent with using usage analytics, providing custom user search query recommendations, and using the insights from your machine learning platform to deliver personalized product recommendations, you too can make upselling and cross-selling the foundation of your bottom line.
If you would like to share the ways in which you’ve improved your e-commerce site with AI-powered search, feel free to reach out to me on Twitter or LinkedIn.
