Artificial Intelligence and the Digital Path to Purchase

Over a dozen years ago, Steven Spielberg’s movie Minority Report introduced viewers to a future in which marketing and advertising is so interactive that personalized sales pitches are aimed at potential customers as they walk through a mall. The film was set in the year 2054. A lot has changed since the film was released including the decline of malls and the rise of e-commerce. One prediction the film got right was the increased use of artificial intelligence (AI) in the marketing field. It’s no coincidence that the increasing value of AI systems in the retail sector corresponds with the increasing consumer use of the digital path to purchase. Leslie Hook (@lesliehook) explains, “In recent years online retail has followed the same formula — prettier websites, smoother checkout and easier access to credit card info. But [now] a growing number of retailers are deploying a new tactic to help boost sales: artificial intelligence.”[1] Sheila Eugenio (@shyeugenio) adds, “Paradoxically for a machine, AI’s greatest strength may be in creating a more personal experience for your customer. From product personalization to virtual personal shoppers.”[2]

How Artificial Intelligence Enhances the Digital Path to Purchase

ai-and-the-dp2p-02Hook observes, “This technology is maturing right at the moment when traditional retailers are struggling to remain relevant and many ecommerce companies are striving to find a winning formula.” Babak Hodjat (@babakatwork), co-founder and Chief Scientist of Sentient, adds, “Artificial intelligence is all around us, from searching on Google to what news you see on social media to using Siri. And with the momentum around AI growing every day, it’s not surprising that some of the most innovative retail sites have recently been experimenting with the use of AI, as well.”[3] Below are some of the ways pundits predict AI will affect the digital path to purchase.

1. Personalized Recommendations. “Personalization in ecommerce is nothing new,” Eugenio writes. “But thanks to emerging AI technologies, online brands of all sizes will have increasing access to tools laser-focused on personalization.” Personalization doesn’t begin with AI; it begins with data. Without data AI systems have nothing analyze. You might say, “Nothing in, nothing out.” The good news is that e-commerce generates data. Hodjat explains, “E-commerce is a space with a lot of potential, in part because it’s such a data-rich industry.” AI systems (particularly cognitive computing systems) leverage technologies like machine learning, natural language processing, and advanced analytics to generate insights and recommendations. They can actually help businesses decide how best to use their data. According to Catherine Aherne, many companies are struggling to get the most from the data they have. She explains, “One of the biggest challenges for the modern business is learning to utilize all of the data available to them in a way that is both meaningful and actionable. However, the potential for using data generated by a website is often left unexplored, and as a result, the intentions and reactions of individual digital customers can be overlooked.”[4] Cognitive computing can help.

2. Improved Search. Armando Roggio (@EcommerceBoy) asserts, “Machine learning algorithms will vastly improve ecommerce product search capabilities. … These learning search systems will deliver product results that your shoppers are likely to want and buy.”[5] Andy Narayanan, Vice President of commerce at Sentient, told Hook, “Ecommerce has not really evolved for the last decade and conversion rates have stagnated. We let the AI pull [up] the product that the shoppers want, and if we do that it is a level of personalisation we have not seen for a very long time.” Eugenio notes one of the most exciting advances in the search arena is visual search. She explains, “Software platforms that drive ecommerce websites are creating visual search capabilities which allow consumers to upload an image and find similar/complementary products. The visual search capabilities, particularly via mobile, ‘reads’ the item for clues — color, shape, size, fabric and brand. This helps consumers to find exactly what they are looking for right away.”

3. Chatbots/Virtual Personal Shoppers. Chatbots have garnered a lot of recent headlines. Hodjat explains, “Chatbots, in the form of assistants and automated customer service reps, are becoming increasingly common across the industry. They have the potential to create a pleasant experience for the user, one that is directed at identifying exactly what best suits their needs, while promoting the brand identity through the chatbot persona itself.” Hook adds, “Ultimately, retailers and tech developers hope that AI engines will be able to server consumers just like an experienced shop assistant would — by subtly deducing which features are important to customers, and which are not.” Roggio notes that chatbots can also be useful after a sale is made. “Good customer service,” he writes, “often requires a conversation. It is for this reason that chat works so well in ecommerce. When a shopper chats in a question, a customer service representative can answer and guide the shopper to a solution. Similarly, when a shopper posts a question or complaint on social media sites like Facebook, Twitter, or similar, a quick and helpful response makes a world of difference in the shopper’s experience.”

4. Omnichannel Sales. “For years,” writes Jacob Serebrin, “many brick and mortar retailers treated their online stores as an afterthought, seemingly more afraid of cannibalizing in-store sales than an ongoing shift in the way customers shop. … But that approach is increasingly being left behind as more and more online retailers move into the physical world and retailers who got their start in bricks and mortar look to stay competitive. They’re taking what’s called an omnichannel approach — reaching customers in-store, online and elsewhere and doing that in completely unified way.”[6] Serebrin goes on to note, “Technology will … play a big role in the future of omnichannel retail and marketing.” The specific technology to which he refers is cognitive computing and how it can be leveraged to provide a seamless consumer experience regardless of the digital path to purchase they choose.

5. Market-Right Pricing. Roggio writes, “Online retailers may be able to use learning algorithms to analyze and understand pricing trends, product demand, and customer behavior to determine the just-right price for a particular item, to maximize profit or achieve other ecommerce business goals. Too often, online sellers become involved in a margin-slashing price war with competitors, particularly on marketplaces. But a learning price-management system may help retailers find the best price for each item it carries.” Because so many variables can be involved in pricing, a cognitive computing system is ideal to manage market-right pricing.

6. Fraud Detection and Prevention. Another area Roggio recommends using AI is in fraud detection and prevention. “Fraud detection and prevention tends to be more of an issue for relatively large ecommerce businesses than for small or even mid-sized retailers,” he writes. “The reason is simply financial. Small ecommerce business may not experience enough fraud to make it worthwhile to purchase fraud detection software. … When it does make sense to employ a fraud prevention solution, you can expect machine-learning solutions to become popular.”

7. Better Business Decisions. All business leaders want to make better decisions. And no decision is more important in the retail space than knowing the right moment to reach out to a consumer. AI systems can help you determine when that moment of decision on the consumer’s digital path to purchase has arrived. Aherne explains, “Predicting customer behavior can tell you which customers to reach out to on your site, in real time, to convert website visits into tangible outcomes.” Roggio adds, “Machine learning algorithms may also contribute to ecommerce decision-making, including any of the following operations: Predicting product demand; identifying potential inventory problems; classifying products and identifying keywords; managing marketing campaigns; estimating shipping and packing costs; and improving customer segmentation.”

Summary

Eugenio reports studies show consumers on the digital path to purchase are open to AI assistance. She writes, “A study from the research firm J. Walter Thompson, reveals that consumers are interested in how AI will be used in retail: 70 percent of US millennials say they would appreciate a brand or retailer using AI technology to show more interesting products. And 72 percent believe that as the technology develops, brands using AI will be able to accurately predict what they want.” Hodjat agrees consumers are ready for AI help. “As far as consumers are concerned,” he writes, “a lot of these AI breakthroughs will lead to one central concept: adaptive, in-the-moment personalization — AI that can intuit what a shopper’s style is and adapt its recommendations as she or he shops; AI that can evolve a website to specific consumer needs; and AI that can understand user concerns and answer complicated questions.” He continues, “AI is also primed to make the massively complicated (and data-rich) world of logistics much easier for retailers, from making sure the right products are in the right warehouses to actually predicting which items will fly off shelves. … In other words, at every step of the buying journey, from discovery to delivery, AI will deliver tangible and important advantages for both retailers and their customers. It will make shopping both easier and more personal. And it’s already happening, all around you.”

Footnotes
[1] Leslie Hook, “Retailers look to artificial intelligence to bag sales,” Financial Times, 22 November 2016.
[2] Sheila Eugenio, “5 Ways Artificial Intelligence Is Shaping the Future of Ecommerce,” Entrepreneur, 8 November 2016.
[3] Babak Hodjat, “How artificial intelligence is changing online retail forever,” TechCrunch, 11 October 2016.
[4] Catherine Aherne, “How Machine Learning can be used to Predict Customer Behaviour,” altocloud,
[5] Armando Roggio, “6 Ways Machine Learning Will Impact Ecommerce,” PracticalEcommerce, 3 October 2016.
[6] Jacob Serebrin, “Can Cognitive Computing Leapfrog Omnichannel Marketing Platforms?Techvibes, 24 May 2016.

Follow me on Twitter