Imagine you’re about to leave the house to pick up your kids. As you grab your keys, you hear a voice from the device on your coffee table: “It looks like you’ll use the last of your milk tomorrow, and yogurt is on sale for $1.19. Would you like to pick up an order from Trader Joe’s, for a total of $5.35?” You say yes, and Alexa confirms. The order will be ready for curbside pickup, on the way home from your kids’ school, in 15 minutes.
This future scenario isn’t so far off. Amazon, Facebook, Google, and Apple are accelerating consumer expectations and what’s technologically possible, from same-day delivery to machine-powered image recognition. You can call an Uber with Siri and book a flight entirely through a Facebook Messenger bot.
Responsive retail has peaked, and we’re about to enter the era of predictive commerce. It’s time for retailers to help people find products in their precise moment of need — and perhaps before they even perceive that need — whether or not they’re logged in or ready to click a “buy” button on a screen. This shift will require designing experiences that merge an understanding of human behavior with large-scale automation and data integration.
Retail giants have been using machine-learning algorithms to forecast demand and set prices for years. Amazon patented predictive stocking in 2014, and saying that AI, machine learning, and personalization technologies have improved since then is an understatement. Retailers need to think more like tech companies, using AI and machine learning not just to predict how to stock stores and staff shifts but also to dynamically recommend products and set prices that appeal to individual consumers.
Say you’re on a business trip and realize you forgot your phone charger. You’ll pay a premium for a new one delivered to your hotel room before an all-day meeting. An electronics retailer might also predict that you want new headphones. It can offer you a deal on a noise-canceling pair at a price that accounts for current pricing on Amazon, in-store inventory at Best Buy, the current rates for on-demand couriers, and the fact that you’re taking a red-eye flight home tomorrow.
This level of prediction requires detecting subtle patterns from massive data sets that are constantly in flux: consumers’ purchase histories, product preferences, and schedules; competitors’ pricing and inventory; and current and forecasted product demand. This is where AI and machine learning comes in and where companies are investing. Etsy just acquired a company that specializes in machine learning to make its searches more predictive by surfacing nuanced product recommendations that go beyond simple purchase histories or preferences. This is the natural evolution of product recommendations, one that will be the standard for years to come.
Predictive retail involves inspiring consumers in different contexts — before, during, and after a purchase. Commerce is already becoming less of a deliberate activity than an organic part of how we experience daily life. It’s not just smartphones that make browsing and buying spontaneous; Amazon’s Dash buttons and Alexa-powered Echo device are enabling purchases in the home. You can hit the Tide Dash button in your laundry room when you see that you’re running low on detergent, or ask Alexa to order your mom a bouquet of flowers when you remember that her birthday is next week. This is just the beginning.
The next generation of smart assistants and connected devices will learn from user habits and pick up on behavioral and environmental patterns in order to make these experiences more predictive. Devices like the Echo will access data from everyday interactions to predict specific opportunities for a transaction.
Chief Analytics Officer Spring 2017
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