Blog Post:When something changes in the customer landscape, Walmart knows. And they know just how to react. “Walmart has a massive inventory with millions of products,” says John Bates, senior product manager for data science and predictive marketing solutions at Adobe. “And they adjust that inventory to better align with certain types of products, depending on what’s happening in real time.” For example, if a hurricane is in the weather forecast, Walmart will shift its inventory to have the things they know from past experience their customers will want to buy — extra grocery staples, bottled water, sandbags, wet/dry vacuums, chainsaws, and generators. Simultaneously, merchandise that is less likely to sell in this weather — again, according to Walmart’s data — is taken off the shelves. “This strategy provides sufficient inventory for the most-needed items on any given day and minimizes the shelf time of all products, satisfying both customer and retailer needs,” says John. Ensuring a relevant experience for customers — whether they’re heading to a store or shopping online — is achieved by leveraging the power of artificial intelligence (AI), including machine learning and predictive analytics, to deliver personalized experiences at scale. AI Helps Deliver What Customers Want Not surprisingly, retail and e-commerce have always been central to the personalization and optimization conversation. From Amazon’s recommendations — which drive 30 percent of its revenue — to targeted email outreach and push alerts promoting complementary products, the most optimization-focused retailers have always pushed the experience envelope, fueling the desire for more relevance at all touch points. Delivering relevance on those touch points, though, is where some retailers start to lose their footing. “Taking that next step is a big leap,” says Kevin Lindsay, director of product marketing for Adobe Target. “It’s a leap of faith in terms of how much you can bite off. How much is actually doable today and what benefits can you get from incorporating AI into developing these tactics today?” These days, supplying personalized experiences at every touch point isn’t just something customers want, it’s what they expect. More than half of consumers want a “totally personalized experience,” and three in five are happy to have interests and behaviors shared if it leads to a more personalized journey with a retailer. However, 42 percent of retailers say they know too little to effectively engage key segments. Even a Little AI Can Help Deliver the Right Experience Working with AI, predictive analytics, and machine learning might seem out of reach for many retailers. However, as Kevin mentioned, it’s not an all-or-none proposition. Retailers that take a phased approach to implementing and applying the insights they gain from AI are the ones that are already benefiting. Think about how you can start applying AI to help you in each of these areas: Invest in the right technology stack. Because many retailers haven’t made the leap of faith to invest in the right technology stack that delivers relevance at scale, the experiences they deliver are more likely to miss the customer experience mark. From e-commerce experiences to connected store associates to post-sales communications, without the machine anticipating next steps by acting on predictive analytics, retailers can’t effectively and efficiently map out the customer journey. As a natural result, they can’t act on those critical cues and moments in time. Start by taking inventory of the data to which your organization has access, and how it is integrated for the purpose of obtaining a complete view of your customers. Surface customer needs. Retailers also aren’t able to leverage key data points and real-time actions to deliver relevance beyond what’s right in front of them. “There are plenty of other applications that come along with machine learning,” John adds. “Discoverability of content in search is a good example. By leveraging machine learning and predictive analytics, brands can look beyond what customers are searching for and start connecting the dots on what they likely want. It’s cross-selling at scale — matching customers to specific products or content that will nudge them towards more conversions and greater lifetime values.” ASOS.com, a British online fashion and beauty store, uses AI to uncover and solve issues specific to online retailers, like helping customers find the right size, thereby minimizing returns. By analyzing which items customers keep, in which sizes, versus the items and sizes that get returned most often, ASOS is able to use machine learning to recommend appropriate sizes for individual customers regardless of the brand or fit of specific items of clothing. As a result, returns of ill-fitting clothing are minimized, the customer experience is improved, and ASOS reduces its costs. Produce relevant cross-channel interactions. When machine learning and predictive analysis do take the wheel, cross-channel customer interactions become increasingly relevant to customers on an individual level. This surprises and delights those consumers at every turn, and all but ensures they will keep coming back for more. Says John, “The impact is very straightforward. Machine learning and predictive analytics increase the likelihood a customer will convert — or, even decreases the likelihood an undesirable outcome will occur. That could be something like low retention for a subscription service.” Gather more data. Retailers should act on every opportunity to gather data. “Every single point of interaction that a consumer has with a retailer is another dot. It is another piece of data that helps to make up the picture,” explains Kevin. The picture you create with data will ultimately feed machine learning and predictive analysis for retailers. Brands like The Home Depot and Ikea are good examples of companies moving on this data, as they’re using beacon technology to understand the physical journeys and pathways that people take within a large mass merchant store. The data that emerge provide interesting insight into how they should be merchandising their products. Incorporating AI is a shift that’s happening daily. However, most retailers aren’t quite there — yet. “The ability to say, ‘OK, here is everything we’re learning,’ and then ask how we can act upon it right now to provide a customer with a much more relevant experience — I would say that is the piece that is not very mature yet even among bigger retailers,” says Kevin. “You can probably count on two hands the number of big retail companies out there that have the data, resources, and ability to build machine learning systems for the benefit of personalization.” Start small, but start, and you’ll be at the top of the pack when it comes to delivering personal and relevant experiences across your customer base. The Future of AI in Retail Experiences The technology powering artificial intelligence is quickly growing and evolving. “There’s a lot more we’ll see,” John says. “More intelligent systems with cognitive analytics — systems that go beyond serving up insights to actually making recommendations and decisions based on those insights, and then constantly learning to make better decisions.” Investments in AI at Adobe are consolidated under a single framework with Adobe Sensei. Sensei will unify AI components along with trillions of data and content points to create unparalleled experiences. In Adobe Target, a new experience decision engine dubbed One-Click Personalization is now in beta and enables marketers to test different web page layouts and activate the process with a single click. After that, the machine takes over, working through several hundreds of thousands of visits and interactions with the website to determine the ideal layout — the one that drives the most conversions.” And that’s just the beginning. Take steps now to incorporate the power of AI in your efforts to drive personalized and relevant experiences to each of your customers. For more insights on how retailers are adopting new technologies for more personal customer experiences, read more from our digital marketing retail series. Also, download our white paper to learn why retailers that use experiences stand out. Author: Date Created:June 22, 2017 Date Published: Headline:Retailers: Adopt Artificial Intelligence Now for Personalized and Relevant Experiences Social Counts: Keywords: Publisher:Adobe Image:https://blogs.adobe.com/digitalmarketing/wp-content/uploads/2017/06/Image-Retailers-Adopt-Artificial-Intelligence-Now-for-Personalized-and-Relevant-Experiences-e1498092133358.jpeg

When something changes in the customer landscape, Walmart knows. And they know just how to react.

“Walmart has a massive inventory with millions of products,” says John Bates, senior product manager for data science and predictive marketing solutions at Adobe. “And they adjust that inventory to better align with certain types of products, depending on what’s happening in real time.”

For example, if a hurricane is in the weather forecast, Walmart will shift its inventory to have the things they know from past experience their customers will want to buy — extra grocery staples, bottled water, sandbags, wet/dry vacuums, chainsaws, and generators. Simultaneously, merchandise that is less likely to sell in this weather — again, according to Walmart’s data — is taken off the shelves.

“This strategy provides sufficient inventory for the most-needed items on any given day and minimizes the shelf time of all products, satisfying both customer and retailer needs,” says John.

Ensuring a relevant experience for customers — whether they’re heading to a store or shopping online — is achieved by leveraging the power of artificial intelligence (AI), including machine learning and predictive analytics, to deliver personalized experiences at scale.

AI Helps Deliver What Customers Want
Not surprisingly, retail and e-commerce have always been central to the personalization and optimization conversation. From Amazon’s recommendations — which drive 30 percent of its revenue — to targeted email outreach and push alerts promoting complementary products, the most optimization-focused retailers have always pushed the experience envelope, fueling the desire for more relevance at all touch points.

Delivering relevance on those touch points, though, is where some retailers start to lose their footing. “Taking that next step is a big leap,” says Kevin Lindsay, director of product marketing for Adobe Target. “It’s a leap of faith in terms of how much you can bite off. How much is actually doable today and what benefits can you get from incorporating AI into developing these tactics today?”

These days, supplying personalized experiences at every touch point isn’t just something customers want, it’s what they expect. More than half of consumers want a “totally personalized experience,” and three in five are happy to have interests and behaviors shared if it leads to a more personalized journey with a retailer. However, 42 percent of retailers say they know too little to effectively engage key segments.

Even a Little AI Can Help Deliver the Right Experience
Working with AI, predictive analytics, and machine learning might seem out of reach for many retailers. However, as Kevin mentioned, it’s not an all-or-none proposition. Retailers that take a phased approach to implementing and applying the insights they gain from AI are the ones that are already benefiting. Think about how you can start applying AI to help you in each of these areas:

Invest in the right technology stack. Because many retailers haven’t made the leap of faith to invest in the right technology stack that delivers relevance at scale, the experiences they deliver are more likely to miss the customer experience mark. From e-commerce experiences to connected store associates to post-sales communications, without the machine anticipating next steps by acting on predictive analytics, retailers can’t effectively and efficiently map out the customer journey. As a natural result, they can’t act on those critical cues and moments in time. Start by taking inventory of the data to which your organization has access, and how it is integrated for the purpose of obtaining a complete view of your customers.

Surface customer needs. Retailers also aren’t able to leverage key data points and real-time actions to deliver relevance beyond what’s right in front of them. “There are plenty of other applications that come along with machine learning,” John adds. “Discoverability of content in search is a good example. By leveraging machine learning and predictive analytics, brands can look beyond what customers are searching for and start connecting the dots on what they likely want. It’s cross-selling at scale — matching customers to specific products or content that will nudge them towards more conversions and greater lifetime values.”

ASOS.com, a British online fashion and beauty store, uses AI to uncover and solve issues specific to online retailers, like helping customers find the right size, thereby minimizing returns. By analyzing which items customers keep, in which sizes, versus the items and sizes that get returned most often, ASOS is able to use machine learning to recommend appropriate sizes for individual customers regardless of the brand or fit of specific items of clothing. As a result, returns of ill-fitting clothing are minimized, the customer experience is improved, and ASOS reduces its costs.

Produce relevant cross-channel interactions. When machine learning and predictive analysis do take the wheel, cross-channel customer interactions become increasingly relevant to customers on an individual level. This surprises and delights those consumers at every turn, and all but ensures they will keep coming back for more. Says John, “The impact is very straightforward. Machine learning and predictive analytics increase the likelihood a customer will convert — or, even decreases the likelihood an undesirable outcome will occur. That could be something like low retention for a subscription service.”

Gather more data. Retailers should act on every opportunity to gather data. “Every single point of interaction that a consumer has with a retailer is another dot. It is another piece of data that helps to make up the picture,” explains Kevin. The picture you create with data will ultimately feed machine learning and predictive analysis for retailers. Brands like The Home Depot and Ikea are good examples of companies moving on this data, as they’re using beacon technology to understand the physical journeys and pathways that people take within a large mass merchant store. The data that emerge provide interesting insight into how they should be merchandising their products.

Incorporating AI is a shift that’s happening daily. However, most retailers aren’t quite there — yet. “The ability to say, ‘OK, here is everything we’re learning,’ and then ask how we can act upon it right now to provide a customer with a much more relevant experience — I would say that is the piece that is not very mature yet even among bigger retailers,” says Kevin. “You can probably count on two hands the number of big retail companies out there that have the data, resources, and ability to build machine learning systems for the benefit of personalization.” Start small, but start, and you’ll be at the top of the pack when it comes to delivering personal and relevant experiences across your customer base.

The Future of AI in Retail Experiences
The technology powering artificial intelligence is quickly growing and evolving. “There’s a lot more we’ll see,” John says. “More intelligent systems with cognitive analytics — systems that go beyond serving up insights to actually making recommendations and decisions based on those insights, and then constantly learning to make better decisions.”

Investments in AI at Adobe are consolidated under a single framework with Adobe Sensei. Sensei will unify AI components along with trillions of data and content points to create unparalleled experiences. In Adobe Target, a new experience decision engine dubbed One-Click Personalization is now in beta and enables marketers to test different web page layouts and activate the process with a single click. After that, the machine takes over, working through several hundreds of thousands of visits and interactions with the website to determine the ideal layout — the one that drives the most conversions.”

And that’s just the beginning. Take steps now to incorporate the power of AI in your efforts to drive personalized and relevant experiences to each of your customers.

For more insights on how retailers are adopting new technologies for more personal customer experiences, read more from our digital marketing retail series.

Also, download our white paper to learn why retailers that use experiences stand out.