When you look back at your day, think about how much digitally infused our lives have become. Digital is everywhere—in your electric toothbrush, smart watch, mobile phone—and it assists in other non-digital aspects of our lives.
Kiran Mani, the Managing Director for Google’s Retail Sector, started the session with a story about a day he spent at the park with his friends and family. Mani has a three year old son who is still in diapers and needed a changing. He asked his wife to grab the diaper bag from the car. Instead of leaving her friends to grab the bag, she jokingly said, “OK Google, help Daddy find diapers.” At this point, a few things happened: Mani’s Google phone recognized his wife’s voice and understood that he was “daddy.” Then, his phone not only provided information for nearby stores that sold diapers and options for purchasing online, but it also knew which size diapers his son wore and the brand they frequently purchased. It’s experiences like this that make our lives better. All of this was made possible using machine learning and artificial intelligence.
According to Mani, today’s consumers are “more curious, more demanding, and more impatient than ever.” Think about all of the mundane things you research today. What’s the best toothbrush? What kind of milk should I buy? What’s the best organic makeup brand? In fact, there has been a 55 percent growth in search ideas on Google. When shopping online, one third of consumers expect personalized recommendations. They don’t want to just know what’s the best toothbrush, but what’s the best toothbrush for me? Once you’ve found the product, you want it as fast as possible, the same day even. Mani says that there’s a 120 percent growth in search for “same day shipping.”
Mani shared an acronym he learned from his cousin to sum up consumer demands: I.W.W.I.W.W.I.W.I. It stands for “I want what I want where I want it.” This is where machine learning can help. Machine learning takes existing data and makes it available in a useful form in real-time.
Before diving into case studies and best practices of AI and machine learning, it’s important to recognize the paradigm shift we’re currently in. Mani says, “We are shifting in a world that is going from information to assistance. Information is about following a customer and a customer tells you what he wants and you’re following him with product information, pricing information. Assistance is actually about getting ahead of the curve, it’s about knowing before a customer really knows what he wants, making sure that he or she is ready to hit that buy button.”
As a result of this shift, there’s a huge a $800 billion opportunity for retailers who get personalization right worth according to Mani. In order to create a better, frictionless experience, you need to focus on connecting with customers throughout the shopping journey, drive action, and accelerate.
Connecting with Customers Throughout the Shopping Journey
Say someone is looking for the best natural makeup. They start their search on Google, browse a few sites, search again, browse a few more sites, watch product review and tutorial videos on YouTube, search again, and finally purchase the product. Mani estimates this is equivalent to 17 plus touchpoints—17 opportunities to engage with your customers.
The search journey usually doesn’t stop when the customer purchases the product. Customers will spend more time researching other products from the brand and watching how-to videos on YouTube. So how do you follow the customer throughout this entire journey? Google came up with Smart Shopping Campaigns. Smart Shopping Campaigns help advertisers determine the right price point, the right time of day, and the right product to put in front of the right consumer. The technology takes your feeds of data and puts a layer of machine learning on top, removing human intervention. This allows the machines to look at patterns to figure out which ads will resonate the most with the user and what type of product to offer. On average, companies using Smart Shopping Campaigns saw 20 percent more conversions.
What about the customers that don’t know what they’re looking for? Google uses machine learning to drive discovery and provide inspiration for products. This technology, or Showcase Shopping Ads, are more immersive and engaging and can be infused with YouTube videos. Showcase Shopping Ads have a 3.6 times higher average click-through-rate than regular shopping ads.
Another example of machine learning technology is Google Lense. Google Lense allows customers to take a picture of an item and Google will find items similar based on the image. For example, if you’re in a restaurant and you like the faucet in the bathroom, you can take a picture and Google will find the product for you.
Drive Action On Site, In Store, and Across the Web
Site speed is the number one friction point for consumers. On average, American retailers have three times more mobile visitors than all of their physical stores combined. However, the average mobile site speed in the US is ten seconds. If a consumer has to wait more than three seconds for the site to load, 50 percent of them will abandon the site. “Ten seconds of the mobile load site speed is equal to making a customer wait 20 minutes before allowing them to get into the store,” says Mani. He adds, “It’s awkward.” Companies improving their site load speed find success: J. Crew reduced its site load time by 90 percent and saw a 75 percent improvement in faster checkout.
The second friction point is untethered online and offline shopping experiences. 91 percent of US retail sales happen in stores, however, consumers are much more informed when they step into these stores. Ideally, there would be zero friction between the two. Adidas co-mingled its online and online shopping experiences, and as a result, it increased its store visits by 42 percent.
The last major friction point consumers face is not feeling assisted. Customers now expect you to anticipate their needs and provide product recommendations. They want you to remember who they are when they visit your site and provide a personalized experience. By implementing personalized product recommendations, Google checkout, and auto-filling customer information, Target saw a 20 percent increase in basket size.
Accelerate By Making Everyone Responsible for the Customer
Mani suggests thinking about your businesses as omnichannel businesses. Omnichannel is about what is happening behind the scenes, especially supply chain. For example, Home Depot offers a variety of order and shopping options. If the product you’d like to purchase isn’t available in the nearest store, the retailer will ship it there. And, regardless of where you purchased a product, you can receive service for the product in-store or online. By integrating supply chain to create a seamless experience for the customer, Home Depot improved its online sales by 6 percent.
CarMax is another example of a company using data to create more customer centric experiences. Most people wouldn’t think to purchase a car online. That said, CarMax has 50 times more visitors to its mobile site than all of the foot traffic to its stores. Its primary customer isn’t the buyer, but the car seller. By creating a data-driven mobile experience, car sellers can take comfort in two factors: they will receive a fair price for their car and every car is sellable.
Machine learning doesn’t have to be a billion dollar undertaking. There are small ways to integrate the technology to provide your customers more personalized experiences, such as voice assistance, cleaner checkouts, and better product recommendations. As we shift away from information and towards assistance. Machine learning will help you stay ahead of the curve by anticipating your customers’ needs.