The Long Tail of Marketplaces: A Conversation with Rewarder

I recently had the chance to sit down with Kendall Fargo, CEO and founder of Rewarder. Some of you may remember Fargo from his days leading StepUp Commerce, and early leader in what is now the growing area of real time retail inventory data (i.e. Retailigence). StepUp was acquired by Intuit in 2006.

Fargo first founded Rewarder with the idea of democratizing and flipping the common practice of employer referral bonuses. When I say flip, I mean that job candidates can set bounties for referrals to open positions. That makes a lot of sense in a buyers (employer’s) market when jobs are scarce.

“We were finding that people would put up offers that were equal to an entire paycheck,” said Fargo. This makes sense given how much job candidates will value job placement when they’re in that position. It’s their livelihood and thus something that is very price inelastic. So a marketplace was born.

Fargo and team have since expanded the model in categories and functionality. It now stands as more of an open marketplace for what I’d generally describe as assistance in finding anything. Those things could be vacation tips, collectibles, expert advice in obscure areas an anything in between.

A few examples the company gave me of real life success stories include:

— Man looking for a video of a basketball game aired on ESPN 2 over 10 years ago (he played in the game)
— Man looking for recommendations on social services to help an elderly neighbor
— Man looking for someone to proofread his manuscript
— Woman looking for a book from her childhood
— Woman providing a travel itinerary for a 3 day trip to Nashville

The way it works is like a reverse craigslist. Instead of a seller-initiated process of posting what is available. It is a buyer-initiated process of indicating demand and stating a bounty. If you think about it, that product model works better for long tail or obscure needs. And that’s one of the areas where Rewarder shines.

This has attracted lots of experts on certain topics, giving them extra income and playing into their passion to exercise that knowledge. These “solvers” can monitor queries from “seekers” and even set up push alerts for new queries.  This makes it similar to Task Rabbit and Zaarly, but less task oriented.

Rewarder works particularly well in categories for which the need is high and the solution is scarce. This causes price to be relatively inelastic for seekers, driving up rewards. The company benefits from this, as it takes a cut of the reward as a fee  for creating the marketplace and processing the payment.

The time could also be ideal for Rewarder, given the growth of the “sharing economy” and mobility. The latter creates an environment where seekers and solvers in a time sensitive mode can realize more value in the product.  This is similar to the way that eBay has found lots of success in mobile.

Rewarder is also generally interesting in that it solves some of the longstanding deficiencies of search — particularly in long-tail queries.  Rather than an algorithmic solution, it makes searching for scarce items more human-based and in that way sidesteps some of the technical challenges of natural language search.

So far there have been  $14 million in rewards offered, 500,000 registered members and a 4x increase in users over the past 6 months. Each reward currently sees an average of 7 responses. We expect this growth will continue as virality is inherent in the product. We’ll continue to keep a close eye as this plays out.

Update: Note that the registered user count has been updated from a previous typo.

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