Augmenting commerce with social, not the other way around
Companies across fashion and ecommerce have tried to bridge the divide between social and commerce by throwing boat loads capital at the problem. These efforts have been largely unsuccessful. Buy buttons and its ilk don’t convert as expected, and apps like Spring, Fancy, Svpply, Polyvore, which attempt to mimic the social experience, have also faltered relative to their lofty expectations.
Why have these efforts consistently come up short? These failures are the result of a fundamental misunderstanding of the possible relationship between social and commerce. Most of the prominent social-meets-commerce solutions, such as Spring, have forced social ideas on top of commerce. These apps tried to mimic the social experience and add a commerce layer below the social stack.
Social Above the Hood
To pick on Spring specifically, which I was originally optimistic about, the app attempted to fix Instagram’s fragmented browsing and shopping experience by adding a native commerce layer. But the problem from day one was simple: Spring was never going to replace the habit of browsing on Instagram and then shopping. Every stakeholder, from users to brands, already decided that Instagram was the primary social network for images, and therefore fashion. For Spring to work, consumers and brands had to abandon (or minimize) their Instagram ritual and port this time and energy over to Spring, or an equivalent app. People and companies will rarely abandon and replace such an ingrained behavior, especially for apps with Instagram’s scale and network effects.
Social Under the Hood
Inverting the Spring model might work. This means leveraging the rich data and social graphs of existing social platforms to improve the back end of new, unrelated apps and services. The goal is to pull the important parts of these apps and port them over to an entirely new and improved experience, not mimic or slightly evolve an existing, highly successful social app. This way, you’re not trying to replace everyone’s deeply ingrained habits with these social platforms.
I call this Social Under The Hood.
Here’s an example of an app that takes this into account. Let’s say a multi-brand retailer wants to leverage the social graph to better recommend the right products to the right customers, along the lines of what I recently wrote about. Instagram, as a massively influential platform within fashion, is a logical place to start. Instead of trying to build a commerce layer on top of Instagram, such as Like To Know It or replacing Instagram all together, use Instagram’s API to ingest a user’s likes, follows, tags and followers, and then use this data as the foundation for an entirelly new shopping app. Besides the user connecting this new shopping app to Instagram, the entire social layer is invisible. This is crucial. From there, the app could recommend brands and products based off of the key brands the user likes and owns, and extrapolate on out.
Almost every social layer in the example app above exists under the hood. The user basically doesn’t know the social layer exists, which is exactly how it should be. When the user is hit over the head with something along the lines of Hey look we’re using social in this app isn’t it great guys??? the answer is usually: no. And the experience suffers as a result because the social layer was jammed in.
Many of the current “social commerce” apps are solutions in search of a problem that are trying to change existing behavior. This is a fool’s errand. But piggybacking on existing behavior (such as a user’s Instagram browsing) to augment new shopping experiences is better for both the consumer and the brand/retailer.
Evolving the Storefront with a Social Backbone
This backbone gives the retailer an immensely rich data set that could lead to a range of new insights around buying, inventory and marketing. This mindset shift could go a long way towards tearing down the storefront as we know it and moving towards every consumer interacting with a different store built specifically for them.
As I recently wrote in Emulating Facebook’s News Feed will fix fashion’s inventory problems,
First, and most importantly, no two people should see the same storefront or have the same browsing experience. Instead, a site should make a bunch of inferences from a consumer’s purchase history about what she likes and what she might be open to. Showing her anything (or everything) is ineffective, as is sorting it by chronology or brand. Search and categories should still exist, in case she knows exactly what she’s looking for, but they should be a secondary interface, not the primary one she shops with.
Social data gives brands a great foundation to build on top of, since the consumer has done plenty of work already determining what they like and buy. There is no reason to ignore this and try and reinvent the wheel. Just build on top of it. The same goes for content that consumers are creating and freely sharing on Instagram. Why should brands stick to featuring bland, white background product shots when consumers are taking hundreds and thousands of beautiful images for brands every day. Adding these images to the workflow fits with the same thesis: build on top of the social layer that already exists. Image attribution could even be used as a tool for rewarding loyal fans, allowing their Instagram to be featured in front of a very large audience. Google’s Chromecast implemented a version of this using community photos for the home screen.
Mobile Real Estate and Accuracy
Correctly integrating the social layer is important for one last reason: the move from desktop to mobile commerce, which means less real estate to feature products, drastically increases the challenge for brands to show consumers the right products. On a desktop, it’s easy to browse hundreds of products in a matter of minutes. On mobile, this is near impossible, if not a terrible experience. Brands will need to be much more accurate with their selections, and luckily the social layer is there to help.
Rebundle the social layer. Don’t try to replace it.
Originally published at loosethreads.xyz on February 19, 2016.