I used to find a lot of my reading material on Twitter. I followed a bunch of media sites and writers, and as articles popped into my feed, I would read them or save them for later. I liked that this process was both immediate and direct; I felt plugged in. But this took a lot of work. I had to check Twitter often and look closely at each tweet.
But in the last few months, I've resorted to a different medium that I'm loving: the newsletter. Every morning I get around a dozen newsletters in my inbox, which directly place the stories of the day right in front of my eyes. I no longer have to hunt for individual stories, which took a lot of time and meant I would inevitably miss some.
The dichotomy highlighted above is best defined as push vs pull. A newsletter is pushing information to me on a regular basis, while Twitter required me to pull information from it. Pushing something allows the customer to be passive (waiting for the newsletter to arrive), while asking them to pull information requires a customer to be active (going out and finding articles to read).
This same lens yields some interesting insights about how pushing versus pulling affects shopping habits. Newsletters have long been a successful tool for brands and retailers. They provide a direct relationship with the customer and usually converted well. But recent changes to Google's inbox filtering and the exponential growth of email have weakened the newsletter's effectiveness. That said, the passive nature of a newsletter has always been nice for a customer; new products come right to her, which is a nice starting point.
Although newsletters in their current form aren't converting optimally, there's a lot to learn and implement by following the same passive framework. Supreme, for example, sends a push notification through their app every Thursday when new products drop. This passive notification turns a customer into an active shopper very quickly. It works because the notification is targeted and Supreme knows that customers with their app are diehard fans. I'm not privy to any real numbers but I would guess this single notification has done wonders to their conversion rates.
Conversely, I used to get a daily newsletter of new arrivals from Farfetch. Even though the passive method had potential, the offerings within the newsletter were way too broad. First, Farfetch didn't know my gender, and was spending half of the email telling me about women's clothes. Next, they had no idea what brands I liked or what price point I shopped at. Finally, Farfetch was sending so many options it was overwhelming and unhelpful. I eventually unsubscribed.
The two examples above show that the passive framework by itself does not guarantee success, especially since customers receive so much inbound communication. But there's a lot to gain by pushing highly targeted products to the right customers. The days of generic email campaigns that go out to entire customer bases should be ending, mostly because of their decreasing effectiveness. The open and conversion rates of these emails are dismal for a reason—they often don't apply to most of the people receiving them.
To effectively push the right products to the right customers, brands need a robust data operation. Interestingly, this requires the same framework to power Social Under The Hood, as I wrote about in Augmenting commerce with social, not the other way around
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....
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... Instead of trying to build a commerce layer on top of Instagram, ... use Instagram's API to ingest a user's likes, follows, tags and followers, and then use this data as the foundation for an entirely 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 out.
This is both exciting and daunting, given that this likely requires a new tech and data foundation to build on top of. But the power of a scalable, data-centric infastructure cannot be understated. This gives brands and their commerce efforts an entire new core, built on facts, not just instincts.
I hope that we see more brands pushing the right products to the right people. There will always be both the time and interest for pull-based shopping, where the customer seeks product out on her own. But if the pushing works, it significantly improves the pull experience.