Get real with AI in the digital commerce experience
Earlier this year, I attended a summit and met the head of digital at Leading Hotels of the World, a boutique chain boasting 375 properties at locations around the globe. He and I gave a presentation on the use of machine learning and NLP to improve the customer experience. At the time, they were in alpha on a project built with Wayblazer, a travel recommendation engine powered by IBM Watson.
Their discovery tool, allows prospective guests to explore their catalog of hotels using natural language. You can continue refining your aspirations as your search unfolds. When you review the results, it explains why it selected each.
The beauty of this application of AI in the digital commerce experience is elegant practicality. First, it just works. Every time I experiment with it I get the results I expect. Second, it’s effortless. There is zero cognitive load. If I default to old-fashioned boolean search, it provides results and politely requests more details.
Later, I met Gwynn, another Watson-powered chatbot. Designed to help less sophisticated shoppers like me, Gwynn lives at 1800flowers.com. There you can have an ongoing conversation with her to figure out the perfect gift among thousands of products across eight brands. All you have to do is say hello, and Gwynn will ask all the right questions and then offer up a custom-tailored catalog of products for your consideration.
AI in the digital commerce experience has entered the realm of the possible.
This summer SLI Systems, an eCommerce search provider, published the results of a survey including 250 industry pros. It showed more than half are currently or plan to add AI in the digital commerce experience they provide. The most popular categories reported are:
- 18% using & 38% plan to use personalized product recommendations
- 9% are using & 26% plan to use chatbots
- 8% are using & 24% plan to use visual search
The survey also asked respondents to rate their understanding of how AI can be applied to e-commerce. It turns out online retailers are quite savvy about the use of AI in the digital commerce experience. 76% of respondents said they understand how AI can be applied.
Interesting to note, this contradicts one of the points in a report published earlier this year in the Harvard Business Review entitled The Business of Artificial Intelligence. In it, they point out the most significant bottleneck in leveraging AI is not the tech, it’s the people. Lack of understanding and imagination among leadership were cited as contributing factors.
AI is not what’s next. It’s what’s now.
The Harvard piece goes on to share that asking what’s next is probably the wrong question. The current state of the technology is far more capable than most of us in business realize.
“In the sphere of business, AI is poised have a transformational impact, on the scale of earlier general-purpose technologies. Although it is already in use in thousands of companies around the world, most big opportunities have not yet been tapped.” -The Business of Artificial Intelligence, by Erik Brynjolfsson & Andrew McAfee
This general lack of understanding got me thinking about how I’m already leveraging AI at work. Throughout the day I’ve access to a handful of chatbots that can complete various low-level tasks to improve my efficiency. At this very minute, I am working with a highly focused background bot to help me write this post more clearly. And then there’s my smartphone, which has become akin to a personal assistant through Google Now.
Creating value with AI for the customers we serve.
But more important than my own productivity, I’m wondering what we might do to create value for our clients at Luminos Labs. As platinum Episerver partners, we have access to incredibly powerful AI, including the personalization and predictive capabilities of applications such as Episerver Reach and Episerver Perform, to improve the customer experience across every phase of the customer’s journey with the brands we serve.
It’s been my experience that the greatest barrier to deploying personalization was traditionally resourcing. Earlier iterations provided the mechanism to match content with personas but the time necessary to imagine scenarios and makeup all the business rules was prohibitive. Episerver AI personalization makes it possible to finally deliver at scale. It reduces the resources required to analyze data, build and manage segments, deploy and manage internal campaigns, or come up with content and product permutations.