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Facebook store flops demand a shift in emphasis February 22, 2012

Posted by David Card in Uncategorized.
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Last week, a pretty negative Bloomberg story about Facebook storefronts got wide pickup. It described how GameStop, Gap, J.C. Penney and Nordstrom had closed their Facebook stores. Social commerce is doomed!

Well, not exactly. I have been bearish on how many commerce transactions stores on Facebook would generate since the concept of “f-commerce” was introduced, but that doesn’t mean retailers should give up. Instead, they should put their Facebook stores in the hands of their marketing and promotions staff and prioritize marketing objectives over sales.

I have described before how storefronts built on Facebook pages face at least two significant challenges. Most e-commerce arises from directed shopping that exploits the Internet’s searchability and price transparency rather than the impulse purchases a buyer might make on a social media site. And I have also suggested some ways to accelerate Facebook storefront success: in-stream promotion, social commerce integration and ties to brick-and-mortar loyalty programs. Plenty of smart companies are implementing the first two, but they are still thinking too hard about sales volumes. Just like daily deals, f-commerce efforts should initially concentrate on customer acquisition, engagement and loyalty.

Feeding the feed gains user attention

E-commerce sites like Ticketmaster, TripAdvisor and Fab.com were quick to take advantage of Facebook’s October Open Graph enhancements that enable “frictionless” auto-sharing of activities without a user creating a post or pushing a Like button. Social commerce believers like Yardsellr.com think it is best that promotions come from customers rather than marketers. And store builder 8thBridge reports that 90 percent of Facebook shopping activity comes from friends sharing with friends.

That approach makes sense, but it oversimplifies some issues. “Frictionless” sharing doesn’t show up in Facebook’s main news feed but rather off to the right, in the live ticker. That means those kind of shopping activities may be quick to appear, but they will also disappear just as speedily and likely won’t be called to a user’s attention by Facebook’s ranking algorithm. In contrast, Ticketmaster and Lucasfilm encourage their customers to pass the word — leading to new customers — and actively engage in logical social activities like group travel-planning or event-planning.

Another store builder, Payvment, is also using Open Graph, but not the way Spotify does, where every interaction with the app is broadcast. Payvment is conscious that shopping activities might require more privacy and user control than music listening. So Payvment is focusing on the new action verbs, like Want and Own and claims they are starting to catch on. But the new actions are mostly on apps or Facebook company pages and have not spread outside Facebook on the Web the way Like buttons have.

And friend-to-friend sharing faces other scale issues: Payvment concedes that most users don’t have enough friends to deliver the kind of volume that big retailers want. So it is promoting the idea of a “taste graph” that aggregates interests — as described by Likes, Wants and Owns — across strangers as well as friends. That would enable offer targeting and the personalization of Payvment’s mall of Facebook stores. It is an intriguing big data play, but companies like Groupon and LivingSocial, with far more resources and data than Payvment, have yet to pull off customized targeting that would improve sales conversion. These are longer-term payoffs.

It’s all about marketing

So before f-commerce stores can generate many sales, smart sellers are treating social commerce as a means of branding, customer acquisition and loyalty building. Heinz says a “get well” soup campaign in the UK generated the sale of one can of soup for every eight fans, and it had to buy plenty of Facebook advertising to deliver that much. Wisely, Heinz was more concerned with adding fans and generating PR than selling soup. Similarly, ad agencies and marketing firms like TBG Digital think that over time, those new action verbs will be a key part of Facebook advertising.

Meanwhile, retailers looking to get the most out of Facebook for the next 18 to 24 months should reassign some of the merchandisers and retailers working on their Facebook storefronts. They should move staff in marketing, promotions, advertising and customer acquisition onto the job. The measure of their success will come from metrics like new customers, visit frequency and brand lift. That is where advertisers and marketers have expertise. Then two years down the road, the retail experts can start thinking about total sales, conversion rates, cost of sales and other transactional measurement.

Question of the week

How else can retailers use Facebook?

Going social: Recommendations engines need to factor in consumer reviews November 28, 2011

Posted by David Card in Uncategorized.
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Last week eBay announced it was buying Hunch, reportedly for $80 million. Even though it has its own recommendations system based on searches and popular items, eBay grabbed Hunch’s recommendations engine. The acquisition illustrates the trend of adding social media to the data-powered recommendations credited with increasing Amazon’s sales by 25 percent. But get ready, because the next stage of commerce recommendations will further mix in human-powered consumer reviews.

Pioneered by Amazon, the first wave of e-commerce recommendations engines were based primarily on collaborative filtering. That is, they compared a lot of data: “Customers who bought product x also liked product y.” As with most big data analysis, recommendations are not only about pattern matching but also about which patterns best predict future purchases. And like all pattern matching, the company with the most data usually wins. Social media, via open APIs from Facebook and Twitter, bring new data for analysis and lower the cost of entry for recommendations engines.

Game-changing social data from APIs

Hunch creates a “taste graph” of a user’s interests based on analyzing available social media data from Facebook, Twitter and others that is fine-tuned by user responses to a Q&A process on the Hunch site. That’s why it could deliver useful results without suffering from a “cold start,” or lack of enough data to be predictive.

Effective e-commerce and media recommendations will mix in big-data analysis of different information sources including product factors, observed purchase behavior, and social and interest graph data from APIs. They will create algorithm-driven engines from that data and present it alongside customer reviews that themselves can be filtered and analyzed by adding structure through categories, comparisons and ratings schemes the way Amazon and Best Buy do.

Another recommendations pioneer, Pandora creates personalized radio stations based on factor-based analysis somewhat like Amazon. Pandora programs based on mapping and matching song and artist characteristics, along with a user’s likes or dislikes. Its effectiveness makes it the leader in online radio, with 40 million active users and $75 million in third-quarter revenues. Lately, Pandora has been scrambling to build out social features to enhance its website and apps and get more inputs.

A startup with an MIT Media Lab background, The Echo Nest has begun licensing its music recommendations engine to online music programmers like KCRW and Clear Channel’s IHeartRadio. The Echo Nest automates its song-characteristic analysis by running each track through audio analysis. It also scans online blogs and music pubs for information that informs its audio analysis. This makes its approach more scalable — and cheaper — than Pandora’s engine.

A blended approach in the future

The established engine technologies still work, and social media data lets new players build them effectively. Last year, Amazon started experimenting with available social data. When connected via Facebook, Amazon remembers a user’s friends’ birthdays, suggests items popular among friends and makes recommendations based on favorites the user has listed explicitly on his Facebook profile. But so far, all of those results are on a separate, “beta” page of recommendations that Amazon links to from its personal recommendations page. They’re not mingled with Amazon’s traditional recommendations and user reviews on product pages — yet. Likewise, as it learns what social data is most predictive, I expect Amazon will incorporate the social signals it gathers from Facebook connections into its recommendations-ranking algorithms, the way Microsoft uses Twitter and Facebook in search.

Successful recommendations will take this approach of blending social and data. At GigaOM’s RoadMap conference, Wal-Mart sketched how its social commerce strategy, driven by its Kosmix acquisition, would focus on search, recommendations and local, in-store context rather than stores on Facebook. Wal-Mart is one retailer with serious data smarts. This is just one example of how, for commerce, it’s wisest to think of social media as data sources rather than shopping hubs.

Question of the week

Who do you think will build the best recommendations engine?