Note: This is the first in a series of posts that explore trends at the intersection of human behavior and technology that might emerge over the next three years from the convergence of other, established trends.
The Macrotrend:
The normalization of social behaviors on the web, in combination with the ability to centralize human networks via mobile devices, will lead to a series of new opportunities for both brick-and-mortar retailers and shoppers.
Informing Trends:
- Standardization of following behavior across both symmetrical and asymmetrical networks
- Centralization of ‘personal networks’ accessible via mobile devices
- Emergence of dynamic POS signage with embedded RFID technology
- Increase in user ‘likes’ and ‘recommendation’ data
Overview:
While there exists no shortage of intriguing retail trends at the moment – from the innovations at the Apple Store, to the pop-up boutique, to rethinking emotional connections with shoppers – consumer engagement via social behavior has been limited primarily to online brand promotions ( think the recent buzz over Gap and Polyvore ) or product sharing tools embedded into the online retail experience.
The Standardization of Following Behavior:
As the evolution of social behaviors normalizes – the idea of ‘liking’ or ‘recommending’ a product or service whether as part of a commerce experience or via a utility like Yelp – the opportunities to leverage the data that these behaviors generate in traditional retail environments grows exponentially. This notion, fully realized, will find forward-thinking retailers owing a debt of gratitude to the unlikeliest of places – namely, The New York Times.
Tim O’Reilly (@timoreilly), in his excellent post ‘The Benefits of Asymmetric Follow‘, makes the following assertion:
Asymmetric follow should at least be an option on any social network. It’s the way the world really works. We never find ourselves in clearly delineated friend-circles, where everyone has or wants complete visibility with everyone else, or none at all.
While O’Reilly’s point is directed towards his use of Twitter (asymmetric network) vs. Facebook (symmetric network), it is perhaps nowhere more perfectly encapsulated than in TimesPeople.
For the uninitiated, TimesPeople is a functionality that allows any Times reader to follow, in a manner informed by Twitter, the paths of other users through the site. Followed users can be friends culled from one’s own network, Times editorial staff and writers, or one of a host of intellectual (or even less-intellectual) figures of note.
‘Following’, in this model, serves as a built-in filter for a reader’s New York Times experience, providing real-time answers to questions like:
- What did my friends like?
- What do people who write for the Times read, themselves?
and, perhaps, most significantly:
- What do the people I most admire / want to be like read and enjoy?
This model for using network behavior as a filter (necessitated by scarcity of time in combination with abundance of content) has been widely propagated by the rapid growth of Twitter, among other networks, and serves as an extremely powerful recommendation tool – via the voice of one’s own personal ‘crowd’ or those select few in our personal networks whose opinions we most strongly value.
Centralization of ‘personal networks’ accessible via mobile devices:
The typical smart device contains several applications capable of aggregating a collective view of the owner’s true social network, be it Facebook, Twitter, LinkedIn, or one of thousands of specific-function applications like FourSquare which both defines a functional network and allows the user to broaden the network via a Single Sign-On service like Sign in with Twitter.
The ability for a single, portable device to identify unique members of an individual’s social network becomes even more intriguing when paired with technologies that respond to RFID chips, such as those explored by Touch:
At the moment the interaction is a trigger, but what if the phone doesn’t just react as output but also as input to physical objects? How do we programme and manage our sets of media and applications in these objects?
Overall this points towards opportunities around the distribution of media through physical objects, it is an example of general ideas around an ‘internet of things’ or ‘spimes’ applied to the world of media. What opportunities would the distribution of embedded products open up in terms of media, gaming, services and marketing? What does this mean for the future of products?
The combination of a physical object bearing a unique identifier (such as a SKU or barcode) with a network of users of varying personal relationships – some of whom may also have a relationship to that same unique identifier – creates very intriguing opportunities in retail environments.
Emergence of dynamic POS signage with embedded RFID technology:
In May 2009, the Altierre Corporation unveiled a new high-contrast electronic shelf-label product which allows for dynamic price display via a centralized in-store network – effectively allowing retailers to adjust prices and offers on a per-product basis in real-time.
The real-time updating is handled via an embedded RFID chip, which according to the Altierre press release:
…supports a number of in-store applications including the ability to automatically adjust prices, introduce new promotions, or handle internal communications.
Similar products from competitors like Tagnetics and Nemoptic are making inroads into the space, with sales of more than $610 million projected industry-wide in 2010 alone.
Increase in user ‘likes’ and ‘recommendation’ data:
Among the many considerable recent trends in the maturing online commerce space is the emergence of user-specific alternatives to purchase, realized through functionalities like in-depth customer product ratings and the ability to store products in a ‘locker’ for future consideration.
These functionalities reveal an innate social dynamic, as well, revealed in a September 2009 study by Wendy Moe and Michael Trusov of the University of Maryland:
Extant research has shown that consumer online product ratings can significantly influence product sales. However, these ratings have also been shown to be subject to a number of social influences. In other words, posted product ratings not only reflect the customers’ experience with the product but also reflect the influence of others’ ratings.
…Our empirical results show that there are substantial social dynamics in the ratings environment, and the impact of these dynamics on product sales is significant. However, the overall effect of ratings on sales is still mostly driven by measures of baseline ratings behavior.
The combination of these social dynamics with the raw data available to retailers about specific user preferences on a per-SKU basis presents a very real opportunity for retailers – particularly as Single Sign-On becomes standardized across online retail properties.
The connection of a user rating or preference in the digital space to a retail interaction with the same SKU in the physical space allows for infinite sets of interactions, many of them based upon behaviors innate to social platforms.
Possibilities:
The convergence of these four trends suggests enormous opportunities for retailers to tap into user-specific mobile-enabled networks for POS feedback, including (but scarcely limited to):
- The ability to identify products in a physical retail space recommended or purchased by others within the user’s network, from a broad range of SKUs – even if that contact purchased online or via a different retailer.
- The ability to track the purchasing habits and cycles of other members of a network, for mimicry (‘I want the same thing she has’) or avoidance (‘I don’t want the same item she has’).
- Opportunities for retailers to dynamically price items based upon activity within a user’s network (‘your friend Emily paid $12.99, you can get the same item for $11.99′).
- Real-time network feedback on purchasing choices (crowdsourced responses to ’should I buy this’).
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