Recommender Systems and Online Sales Performance

I recently received a reminder about NVIDIA’s on-demand virtual Recommender Systems Summit. But this also reminded me that recommender systems, backed by more powerful artificial intelligence (AI), are increasingly a critical part of any e-commerce solution on the web. 

Why? Because when they are done right, they can significantly increase close rates on sales efforts. This is because they more rapidly connect the buyer with a hierarchy of products that will attract the buyer.  

A recommender system better assures that a sales offer will be accepted and, at scale, eventually separate the online stores that outperform their peers with similar products and services.

Let’s talk about AI-driven recommender systems and why they are so important to online sales success.

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Defining a great salesperson

If you’ve ever watched a top salesperson (I’ve done that both in sales training and while I managed commissions for a time at IBM), they are an impressive showcase of interpersonal skills. 

They will have done their homework on the prospect and the prospect’s company. They’ll know the prospect’s interests, how the prospect likes to be approached, and the path to creating a relationship with the prospect, so the prospect trusts their advice. They’ll also study the prospect’s company, if this is a company sale, and have some understanding not only of the problems the company is facing, but the approval process needed to close a sale. The very best sales reps have gone even farther by creating relationships with purchasing and learned the shortcuts to get a deal through your company’s process that often exceeds what is known by the firm’s own purchasing agents.  

In short, these people use relationship skills to present an offer that is designed to appeal to you, and, if there are other people in the approval chain, appeal to them as well. 

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Recommender systems advantages

While I have yet to see a recommender system that can do the dance with purchasing that a top sales rep can, what these systems can do is scale to a level of interactions that no human could possibly sustain. They capture information about the potential buyer from a variety of internal and external sources, often using cookies to track the prospect’s behavior and interests, so that when the prospect arrives at the e-commerce site, they are presented with a custom interface that has been designed to appeal to them.  

While Amazon is an aggressive user of this technology, you see it applied in your initial screen and when you complete a purchase, it doesn’t yet showcase just how far this technology can go if it has enough information. For instance, Amazon knows the Kindle books you have ordered, and while it will showcase similar books, it doesn’t automatically prioritize the next books in any series you have been reading nor does it allow you, yet, to simply subscribe to a series, so subsequent books are automatically purchased. But these are things a recommender system could do, and before mid-decade, I expect Amazon will be implementing at that level.  

The advantages of using a recommender system correctly go beyond just closing deals and include creating deeper customer loyalty and satisfaction with the company due to the improved sales experience. Buyers appreciate not having to waste time to find things and avoiding buyer’s remorse, because the buyer is pushed to the product that best fits their profile.  

Driving close rates

Recommender systems, when done right, scale the performance of top sales reps to the internet for higher volume online buying and no commissions. 

These systems can create relationships with buyers, improve the buying experience, and drive not only close rates on what the buyer is looking for, but close rates on additional sales that the buyer may have otherwise missed.  

I expect there’s a lot of this at NVIDIA’s Recommender Systems Summit, which is why I’m attending. If you are into retail AI, you might want to consider attending as well — if only to get a sense for just how far this technology has come and how far it is likely to go.  

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