Here’s a funny reality. Despite the steady rise of e-commerce since the 1990s, we still stroll through malls and shops. Even with one-click ordering and overnight delivery, we purchase many of our day-to-day products and services locally and offline.
A big reason we do so is that shopping engages us with the wider world. You head to a mall with a group of friends, try things on and show them to each other and maybe catch a movie or have a glass of wine when you’re done.
“Shopping is gratification. It’s validation. It’s an immensely and intensely social experience,” said Punit Soni, chief product officer at the Indian online retail giant Flipkart, in a conversation at the TiECON 2015 conference.
Yet as Soni and many others of us focused on consumer technologies have observed, that offline experience often is the opposite of what happens online, where shoppers are usually relegated to a screen, a keyboard and a mind-numbing series of mouse-clicks made in isolation.
The reality is that online and offline commerce have a symbiotic relationship. People research new TVs online, then buy them in stores. But they also will take a photo of a chandelier at a friend’s house, then order it on the Web.
So instead of pitting online and offline commerce against each other in a zero-sum game, we should be looking at how to make this relationship stronger.
Our view is that online shopping needs to be much closer to the individualized, emotional and social experience of its offline counterpart. As longtime technology investors, we are paying attention to three things: the overall customer experience, machine learning technologies and the march toward greater personalization.
Kill The Clicks
Too many retail sites still lean on the early days of Web design, with tree-like structures that force people to drill through multiples levels. It’s annoying, outdated and turns shopping into an endurance test.
What’s needed is to kill the excess clicks — to shorten the distance between what people want and their ability to buy it. That’s especially true as the instant gratification of mobile commerce becomes a greater part of the shopping experience in the United States — as it is already the de facto experience in many parts of the world, including Asia.
The Google Suggest feature is a brilliant implementation of this idea. You start to key in a set of search terms, and up pops an array of options from which you can choose immediately, eliminating a bevy of clicks.
Do online retailers use this kind of technology? Sure. Some have even rolled out advanced approaches, like those from Algolia, which lets retailers add suggest-while-searching capabilities to their existing sites.
Online and offline commerce have a symbiotic relationship.
But for many companies, the site-search implementation often falls short. For example, when you Google the phrase red sweater women, you see an array of sweaters, for women, and all of them red. But use that phrase on the sites of many major U.S. retailers, and too often you get sweaters for women in every shade under the sun.
The search isn’t nearly as smart as the shopper.
Then there’s the issue of buying an online product at a neighborhood store. Remember, people shop locally for any number of reasons: they’re planning a trip to a certain neighborhood, have a function to attend or are heading out of town. Not everyone can wait for a delivery van.
So imagine you find that red sweater online and want to buy locally. Anyone who has been in a mall at 8 p.m. on a Tuesday night knows what happens next: too few cashiers, at least one escalator ride and wandering past the perfume counter or teen jean section to find what you want.
Two things could make this experience easier.
One is stationing tablet computers at store entrances; shoppers can use them to enter a search phrase or product ID, then get a picture of the sweater and the fastest route to it, a concept known as interactive wayfinding. The second approach moves this experience from a tablet or kiosk to a smartphone, mapping a route all the way from your house to the clothing rack.
By eliminating as many clicks as possible, these techniques not only make the online customer experience better, but also strengthen the links to the offline one.
Quit Looking Backward
One of the more preposterous parts of today’s e-commerce experience is that advertising is always looking in the rear-view mirror. The ads that appears on my screen today reflect searches or purchases I made weeks ago. I’ve moved on, but the advertising algorithm is still stuck in the past.
Machine learning has the potential to reverse the situation. That’s because it’s not simply processing more information more quickly; rather, it’s actively working to understand me as a shopper more fully.
The algorithms for machine learning are in their infancy, and we expect them to become more nuanced, comprehensive and pervasive in the years to come. It’s why Intel Capital invested in and added Reflektion to the Intel Capital portfolio, which uses machine learning to recommend e-commerce products to shoppers so they are shown just-right products — and in real time.
For machine-learning algorithms to really break through, one more thing must change: the narrow data view that retailers have of their customers.
Personalization is where we see the world moving.
Consider my favorite online clothing store. It knows my favorite type of shirt, but has no idea about the music I download. My go-to travel site knows where I’m thinking about vacationing, but it doesn’t know the e-books I’ve been reading. And none of these retailers know I have an upcoming business trip to India and Singapore booked on my electronic calendar. Yet all will serve me ads using only their keyhole views into my world.
We will know that the online commerce experience is more like the offline one when we stop getting suggestions based on how we lived our lives last month — or on a thin slice of data that represents only part of who we are — and instead receive thoughtful, nuanced recommendations based on a full view of how we live.
Which leads me to my last point.
Really Get To Know Me
If too many clicks are the hobgoblin of today’s digital commerce, time wasted in changing rooms and return lines may be the brick-and-mortar equivalent.
That’s because most apparel is less about “ready to wear” than “ready to try on.” Designs are cut to approximate body types, and we do our best to fit into what’s available.
In other words, this brick-and-mortar commerce is a lot less personalized than it could be.
One idea we find intriguing is using more exact measurements to customize retail choices or, at the very least, weed out the ones that won’t work. Imagine knowing in advance that a shirt is cut too narrow through the chest, or a pair of pants lacks enough hip room.
Leading the charge to make these precise measurements possible are high-resolution cameras. Intel’s RealSense offering is among several in this category of devices that have the ability to measure detailed contours with 3D precision.
What if your search function, store kiosk or phone had access to your specific measurements?
Several companies are jumping into this measurement market with both feet. Body Labs, a Manhattan-based startup and recent addition to the Intel Capital portfolio, lets you generate full 3D body scans in less than a minute. True Fit provides highly personalized fit ratings and size recommendations to shoppers. And Intervisual lets you see detailed 3D images of how different kinds of jeans will actually fit your body.
So go back to the red sweater example and consider this: What if your search function, store kiosk or phone had access to your specific measurements? What if you could take those measurements and use them to filter out the sweaters that were cut too big or too small? Or even have one made to your specifications?
That’s the vision of Nike, whose COO recently said he can see a day when customers receive a digital file and use 3D printers to make their own shoes at home. Think of it — one of the world’s largest footwear companies actually making fewer shoes. That’s the power of personalization.
Clearly there are questions about who owns personalization data, where it resides and who authorizes its use. Our view is that shoppers should have ultimate control over how retailers learn about them and use information from them.
But this personalization is where we see the world moving. Because in retailing, context is everything: It’s about what you’re reading and listening to now, whom you’re meeting next week and where you’re traveling next month.
Developing technologies that appreciate this importance is what will make the online e-commerce experience much closer to that of the offline one — to the ultimate benefit of both.