Customers love convenience. Quarantine and stay-at-home orders highlighted just how much so – as they accelerated the at-home shopping trend. Despite the rise in vaccinations and some city restrictions lifting, safety concerns remain. Deloitte believes it created a new kind of consumer: the “cocooning consumer” – consumers who plan to cocoon at home post-pandemic to save money and stay safe.
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These shoppers have higher expectations than ever before. While nine out of 10 consumers say they expect online shopping to be equal or better than in-store, half are experiencing crushing disappointment according to our study. If retailers don’t address this experience gap, more bad news awaits. Almost three-quarters of customers say they will abandon a brand after three negative experiences.
I view artificial intelligence as the crucial piece of the new customer puzzle. It can help e-commerce brands deliver on these high expectations and create a personalized e-commerce experience for every consumer.
How to Give Every Customer a Personal Shopper.
Imagine giving every one of your customers a virtual personal shopper. That’s what AI can do. It can create individualized experiences for each online shopper and take the place of a dedicated sales-rep. Like a sales-rep who sees how customers are moving around a store floor, AI can offer unique suggestions based on how customers are maneuvering through a website.
I always tell customers, when looking at digital experiences, to try to map to what they see in the analog world. Imagine going into a sporting goods store and looking for golf gloves and golf balls. In the analog world, a customer will ask directions from an associate for the location of the golf section. From there, they may ask for “gloves” and “balls”. Good AI will know the implied meaning and give the most relevant experience.
Oftentimes, retailers don’t know where to start with implementing AI or which resources they need. Democratized AI can fill in this gap as its development has recently seen leaps and bounds. It lets retailers deliver the types of relevant experiences that were previously only accessible to retail giants like Amazon or Wayfair.
Retailers have all the information needed to deliver winning e-commerce experiences. They know what shoppers have bought and what they search for. Yet retailers are often missing a layer of intelligence to put this data into action. That’s where democratized AI comes in. And with it, retailers can give every consumer a personalized assistant.
Why You Should Partner With an AI Leader for Personalized E-commerce.
Serious retailers spare no expenses for AI research. It’s important, it makes a difference and is worth investing in it.
Personalizaed e-commerce is a big deal. It has a direct impact on fundamental business KPIs such as conversion rate and average order value. It also indirectly impacts less transactional measures such as client loyalty and engagement.
A handful of e-commerce giants, Amazon, eBay, Rakuten, Alibaba, Flipkart, and the like, currently publish the bulk of e-commerce and AI research. They’ve set the bar high in terms of personalization and hooked customers with effortless, highly individualized and relevant experiences. However, what they research and find out isn’t applicable to the everyday retailer and the amount of investment in AI is beyond the capacity of most retailers. This is even more heightened in today’s war for talent.
Their focus on AI has been on extreme optimization. A 0.2% improvement in recommendation accuracy can translate into serious monetary uplifts at their scale. Yet, such a small optimization won’t impact smaller, more traditional businesses. While we’re starting to see these retailers, like Home Depot, take on more research and publish findings applicable to the wider retail market, we still need more. More of a focus on cross-shop personalization, small data problems and cold start learning will help retailers everywhere.
We are taking a leadership position in AI research to make the benefits of AI available to all retailers. Recently, we hosted the SIGIR eCommerce Data Challenge, releasing a huge, new anonymized dataset of a “normal” retailer that is of unprecedented breadth. This way, AI researchers can use it for years to come to find new insights and information that apply to a much larger number of retailers.
What you should look for in your AI.
A good AI will “think” like the customer. AI-driven recommender systems are a very complex and deep topic. As far as e-commerce goes, most users want to answer the following questions while shopping:
- Will I find the right product and will I find a b******?
- Can I buy a better product at the same price?
- Can I get the same product for less?
Consumers ask these questions even if they have a clear idea of what they want. So a good recommender will look to help consumers find new products, complete their orders with additional products, but also confirm their choices. These are all data-driven recommendations. It’s a matter of fitting an optimal function.
As retailers race to adopt AI-based solutions and Machine Learning Ops grows, I expect AI to become embedded in almost any aspect of the e-commerce lifecycle: from inventory prediction, to logistics optimization, to search and recommendations. Soon, I anticipate we’ll see e-commerce companies transition from using AI for specific problems (those most critical aspects of the business) to developing more comprehensive, harmonized strategies.
The pandemic has shifted consumers’ priorities and heightened their expectations, and AI helps e-commerce brands stay ahead of these demands. It can deliver more personal and seamless shopping experiences — making sure those cocooning customers stay happy in their homes or wherever they may be.
[To share your insights with us, please write to sghosh@martechseries.com]
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