Holiday season is good time to put AI to work for e-commerce

mobile phone shopping cart order

E-commerce’s fundamental shift from brave, new technology to status quo is near complete. Consumer fears over the security and safety of shopping online, major obstacles for brands just a few years ago, have given way to demands for more seamless, personalized shopping experiences. Brands are undergoing digital transformations of their own in order to keep pace.

Today’s constantly connected customer is shopping from whatever device is closest at hand, using mobile devices to solve real-world problems. Some of these queries naturally result in local results – where to get a haircut, for example, or to find the best sushi “near me.”

For retailers though, a growing number of consumers are willing to wait for delivery if they can find a superior product or a better price online. The US Census Bureau pegs Q2 e-commerce sales at $127.3 billion, an estimated 9.6 percent of all US retail sales in the second quarter of this year. Online sales, number of orders and overall traffic to e-commerce sites are all up. On average, consumers spend $114.19 per online order.

retail ecommerce performace metrics chartThe convenience of home or office delivery, proliferation of free shipping, and ease of using voice-activated speaking assistants to accomplish shopping tasks are all among the factors feeding the shift to e-commerce over real-world shopping experiences. Yet only 7 percent of Chief Marketing Officers (CMOs) surveyed recently by IBM said that their digital transformation initiatives were exceeding their expectations.

I’m going to go out on a limb (but not far, let’s be honest) and suggest there’s a correlation between the lack of enthusiasm CMOs feel for their digital performance and the fact that only 36 percent of global marketing decision makers telling Forrester they use AI. To me, that feels wrong. AI holds the key to improved marketing performance.

According to research from BrightEdge, early AI adopters are already seeing benefits that align to CMO priorities such as gaining a better understanding of the customer and increased productivity.

success stories with AI chart
In this column, I want to dig deeper in to what CMOs can feasibly do right now, even as the holiday shopping season looms large, to put AI to work in your marketing organizations. The misgivings and lack of adoption for some laggards pave the way for forward-thinking early adopters to move the needle on engagements, sales and customer satisfaction in big ways. For example, according to BrightEdge Here are some practical AI-powered e-commerce applications you can start with to become one of them.

Implement simple AI-powered chatbots, and prepare for more meaningful assistant experiences.

Last year, less than 2 percent of customer service and support operations had integrated virtual customer assistants or chatbots in their engagement channels. But Gartner expects that figure will rise to 25 percent. What’s more Juniper Research says chatbots will save organizations more than $8 billion annually by 2022.

Earlier chatbots were programmed with a specific set of queries and responses. But AI now enables them to “learn” your site’s content and consumer preferences, bringing back more relevant answers and meeting potential customers with the right information in their moments of need. Consumers have come to expect instant answers as other brands have come on board.

Taking this service element a step further, we have AI-powered shopping assistants. In 2016, department store Macy’s, teamed with IBM’s Watson to create a personal mobile AI shopping assistant called “Macy’s On Call.” The innovative and cognitive mobile tool, which uses Watson’s Natural Language API, was designed to aid shoppers with information in 10 of Macy’s retail stores around the country, as they navigated through each one. This is a direction retailers want to look to for 2019.

Optimize your site’s content for voice search queries.

Even early last year, about 25 percent of queries on the Google mobile app and Android devices were by voice. By 2020, comScore predicts half of all queries will be voice queries. Given the explosion of zero-UI voice-activated devices making their way into homes across the US, I wouldn’t be surprised if it were even higher.

As voice search grows, smart marketing teams are tailoring content to both better answer these queries, and demonstrate to search engines that their answers are the best choice to bring back. Voice searches are more likely to be from mobile consumers; aren’t likely to deal with sensitive information; and are looking for simple answers, not lengthy interactions.

With this in mind, your team can reexamine your content strategy through a voice-focused lens—one that maps your content to the customer journey with intent, context, and formatting/markup for voice search top of mind. Use a more personalized tone, mirror back specific questions and keep answers to a sentence or two in order to better optimize for voice searches.

Gartner expects that by 2021, early adopter brands that redesign their websites to support visual and voice search will grow their digital commerce revenue by 30 percent. It’s a massive missed opportunity for those slow to get on board.

Incorporate prescriptive analytics for actionable e-commerce insights.

Enterprises are struggling to make sense of the onslaught of data produced by clicks, views and impressions, website browsing sessions, behavior in email, closed sales and more. By 2020, more than 40 percent of all data analytics projects will relate to an aspect of customer experience (Gartner). Predictive analytics makes sense of it all by analyzing inbound communications and metrics such as consumer engagement and conversions, to pull potential outcomes from those massive amounts of data.

Basically, as columnist Nandini Rathi wrote recently, the goal of prescriptive analytics is “to suggest actions to achieve the predicted outcome.” Rather than basing decisions on past outcomes alone, as in predictive analytics, prescriptive analytics tell marketers what they need to do in order to improve future outcomes.

This can be especially helpful is areas like remarketing where, according to Conversica, at least 33 percent of marketing leads are not followed up by the sales team. Sales are now so overloaded with unmanageable customer data that they struggle to do anything with it. This is where AI is a real necessity and can have the greatest impact.

Even if you don’t have the time to implement before this holiday shopping season, prescriptive analytics need to be on your radar for 2019. This is the technology that’s going to push our martech beyond simple automation to recommendations and even implementation. Adoption can be challenging as it sounds awfully scary to teams that machines might actually begin to “think” and make some of the decisions they’d rather make themselves. But as we’re seeing with technology in general, this is more likely to result in a shift of the types of jobs required than a mass of job losses.

Combat abandoned carts with AI-powered shopping experiences.

Shopping cart abandonment is an industry-wide scourge. It’s estimated that upwards of 70 percent of online shopping carts are abandoned before customers complete their purchases.

We can see another example of IBM Watson at work in an online shopping app used by North Face. This time, it’s used to personalize the customer experience by creating a psychoanalytic profile of customer data in near real-time. North Face’s AI assistant asks questions to help it understand how customers plan to use their apparel, then provides ranked product suggestions. It saves customers the legwork of searching and browsing their way through all of those products that won’t suit their needs. The result will be vastly improved CTR for product recommendations. AI can make informed recommendations that reduce the uncertainty shoppers feel around choosing an item, thereby increasing their confidence and the likelihood they’ll proceed right through to complete the purchase.

Even if you’re not ready to dive into the AI deep end…

You’re about to produce a volume of searcher and customer data over the holidays that was unfathomable even five years ago. If you’re among the majority of marketing leaders still trying to understand the best ways to tap into the AI opportunity, this is your chance to try out a few applications and gather data to see what direction you want to take in 2019.