Imagine a world where offers come to you that you actually want, tailored to your specific interests, and customers come to a site and find exactly the products they are interested in, every time. Or what if prices optimized themselves, products sold through at higher rates, with lower stock outs and customer service was proactive, not reactive?
Sounds like the standard goals for personalization and omnichannel commerce, but over the decades, these goals have remained elusive. Personalization rules have relied on fixed logic, based on point-in-time analysis that can only get down to aggregate segments, or on business processes that strain the capacity of human teams to keep up, as the adoption of e-commerce has climbed ever higher, driving faster velocities and wider varieties of data.
A new generation of technology is emerging
To this challenge, we’re seeing the rise of a new generation of technology. In the history of computing, we started with technology that could count things – how many people, how many visits, etc. In many ways we are still there, even in the world of e-commerce. In some cases, based on the ability to count things, we have responded to the need for personalization with the second generation of computing, which is programmed based on a fixed set of rules.
The third generation of computing is here
Now, we are entering a third generation of computing, based on cognitive computing. What does cognitive mean? It means systems that learn. For commerce, it means learning about what everyone who visits your site wants, both in aggregate and at the individual level. And it’s more than just drawing observations; with a cognitive solution, that knowledge can be both developed and applied in real time.
A system that learns on its own
So if two people come to the site and click on “Shirts,” the first person would see a different list of items than the second person. Different colors, sizes, and types of shirts can be prioritized differently as well – all based on what has been explicitly shared by customers, what has been observed about each user, and what works for similar types of users in the aggregate. All without hard-wiring your personalization system, the system learns on its own and is enabled to act on what it learns. And that’s just scratching the surface – cognitive can be overlaid onto any phase of your customer engagement strategy to improve customer satisfaction and drive higher business results.
Sound futuristic? Well, it is to some extent. But the reality is that this capability is available now and has shown great results in real-world testing. The advantage could go to first-movers here. There’s no telling how quickly this will be used more broadly, but we think it’s a potentially disruptive capability that’s already here to stay. Are you ready?
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This post was written by Donovan Guin, who leads the Center of Excellence for SAP Hybris in IBM GBS. It was originally published on The Digitalist and has been republished with permission.