Recommendation systems and online sales performance
I recently received a reminder regarding NVIDIA’s on-demand service Recommender Systems Virtual Summit. But it also reminded me that recommender systems, backed by more powerful artificial intelligence (AI), are increasingly an essential part of any web-based e-commerce solution.
Why? Because when done right, they can dramatically increase the close rates of sales efforts. Indeed, they more quickly connect the buyer to a hierarchy of products that will attract the buyer.
A recommender system better guarantees that a sales offer will be accepted and, on a large scale, will ultimately separate online stores that outperform their peers with similar products and services.
Let’s talk about AI-based recommender systems and why they are so important to online sales success.
Defining an excellent salesperson
If you’ve ever watched a top salesperson (I did this both in sales training and while I ran commissions for a while at IBM), this is an impressive showcase of interpersonal skills.
They will have done their homework on the prospect and the prospect’s business. They will know the interests of the prospect, the way the prospect likes to be approached and the way to build a relationship with the prospect, so that the prospect trusts their advice. They will also research the prospect’s business, if it is a business sale, and have some understanding of not only the issues facing the business, but also the approval process required to close a deal. sale. The best reps have gone a step further by building relationships with purchasing and learning the shortcuts to getting a deal through your company’s process that often goes beyond what the company’s own purchasing agents know.
In short, these people use soft skills to present an offer designed to appeal to you, and if there are others in the chain of approval, reach out to them as well.
While I’ve yet to see a recommender system that can do the dance with purchases that a better sales rep can do, what these systems can do is adapt to a level of interactions that no human could bear. They capture information about the potential buyer from a variety of internal and external sources, often using cookies to track the behavior and interests of the prospect, so that when the prospect arrives at the e-commerce site, they see themselves present a personalized interface that has been designed to appeal to them.
Although Amazon is an aggressive user of this technology, you see it applied in your initial screen and when you make a purchase, it does not yet show how far this technology can go if it has enough information. For example, Amazon knows which Kindle books you’ve ordered, and while it showcases similar books, it doesn’t automatically prioritize the next books in all series you’ve read, and doesn’t yet allow you to simply subscribe to a series. , the following books are automatically purchased. But these are things that a recommendation system could do, and before the middle of the decade, I expect Amazon to implement on this level.
The benefits of using a recommender system correctly go beyond simply closing deals and include creating greater customer loyalty and greater satisfaction with the business through improvement of the sales experience. Buyers appreciate not wasting time looking for things and avoiding buyer’s remorse as the buyer is pushed towards the product that best fits their profile.
Recommender systems, when executed well, scale the performance of top internet salespeople for larger, commission-free online purchases.
These systems can build relationships with buyers, improve the shopping experience, and generate not only closing rates on what the buyer is looking for, but also closing rates on additional sales the buyer might have missed. other.
I expect there will be a lot of them at NVIDIA’s Recommender Systems Summit, which is why I’m attending. If you’re into AI in retail, you might want to consider attending as well, if only to get a sense of how far this technology has come and how far it’s likely to go.