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In-brief: MoConDi’s one-click viral sharing gains traction. JT Klepp, MoConDi President, talks stats & strategy, and discusses the role of recommendation & reward in the scheme of things. Finally, a cool idea from an Amazon exec merits a closer look.

“Been there, done that” was my initial reaction to a service that rewards users when they recommend content to their peers. That’s basic Marketing 101. But in mobile it’s a lot harder to manage, and the virtuous cycle it creates (enabling users to share premium content in order to sell still more content) can pay off for the mobile operators and content companies that get on board. Indeed, next to social networking, viral marketing, which builds on the connections between members to potentially boost commerce, is poised to gain some serious traction in 2008.

Case in point is MoConDi’s opt-in content sharing application MeYou.

During the Mobile World Congress, MoConDi, a mobile entertainment creator and distributor whose customers include Disney, Warner Brothers, Sony Music, Gameloft and Electronic Arts, announced that One Italia, a mobile entertainment and value added service (VAS) provider, signed up to offer the content sharing app (branded Passa Parola) to H3G (or 3 Italy) mobile subscribers in Italy. (The mobile operator counts 7 million subscribers, btw.) Plans are to take the platform to the U.S. this year.

In a nutshell, MeYou offers users rewards (in the form of redeemable points) for content purchases and recommendations. How does the viral marketing part of this work? Typically, users send text messages to recommend cool content to their friends. This recommendation text consists of a message, a download link for the content and a link for MeYou application installation. The application has access to over 5,000 pieces of content via the MoConDi content management and delivery platform.

JT Klepp, MoConDi President, tells me the next step is to replace the free text window with templates users can choose from to spread the word. The idea is to make the one-click content recommendation process even more of a no-brainer for users; but the move is also necessary to also close an unfortunate loophole in the service that let users send text messages to their friends for free. Users were not abusing the service, but could, and that wouldn’t jive with expansion into the U.S. market. (To date three carriers are using the platform, including Vodafone and 3 Italy.)

JT counts just over 1 million users of the service to date, and “about one-fourth are active and buy or recommend content more than once a month.” JT also did the numbers and found that users who downloaded the MeYou app continued to explore and purchase content, generating a 33 percent increase in ARPU over users who didn’t have the client. These observations dovetail with some other stats JT recently announced in a press statement.

To recap:

• 60 percent of MeYou users have purchased mobile content in the past

• 64 percent of users send recommendations, with 24 percent of these users making purchases

• Over 1.6 million users have received a MeYou recommendation from a friend in the past six months

• Average carrier mobile storefront marketing costs have been reduced by 20 percent through the use of MeYou

During our briefing, I asked JT why he chose an on-device portal (ODP) approach and the place of P2P recommendation on the portal. Ideally, JT would like to see the client pre-installed on the handset – but for now it’s up to the user (and word-of-mouth) to drive the download.

The focus for now is on mobile content, but the approach can also be used to link back to physical retail such as the sale of DVDs and cinema/concert tickets, two areas that JT is exploring with Disney in Italy, for example. In this scenario, the users who refer items to their friends (who also buy the items) are rewarded with a free DVD. Of course, there is a potential here for the content owner to collect valuable data and analytics (name, address, preference) and build a model that unites the worlds of digital and physical commerce, but no one is thinking about this just yet…

It’s also early days for recommenders and formally building a recommendation engine in the platform to cross-sell and up-sell users to content they are likely to appreciate, but JT assures me that it is on the agenda. It would make good sense too, considering the long-established ties between MoConDi and AgentArts, a recommendation engine company that was snapped up by FAST and now belongs to Microsoft, which bought the company last month.

JT has experimented with AgentArts technology, which he says “works well on the portal.” Moving forward, recommendation will be part of the solution but separate from ours; we have to go one step at a time and operators are focused [for the moment] on increasing content sales through viral marketing, not through suggesting more similar content.”

The discussion with JT reminded me of a business model idea I picked up during a stimulating exchange with Pranav Dandekar, an Amazon exec I had the pleasure to connect with at the last recommendation technology conference, RecSys 2007. I wanted to post it earlier, but his idea was a giant leap ahead of where the thinking was in the mobile space. The raft of mobile ad/marketing related announcements at Mobile World Congress and MoConDi’s traction with its content sharing app changes all that.

Pranav honed in on the idea of tailoring recommendations to social networks rather than purchases, and making them personal – and profitable. Take the example of digital cameras. A system shouldn’t recommend a digital camera to you [the user], it should search the social graph to find a friend related to you who is an expert in digital cameras. And how would a system know that? It would pick up on that user’s expertise through the reviews and ratings that user might have given cameras, for example, and connect the dots.

The business model? The seller approaches the expert and offers a fraction of the revenue if that expert suggests a digital camera to the user – and the user buys it. As Pranav put it: “Now the incentive for the expert user is that they will get paid only if the user buys, so they will automatically not recommend everything to the user and annoy them or compromise trust; they will recommend items that the user is sure to appreciate.” The model creates some interesting problems (how to factor in recommendation through a person to another person, and how to recognize and weigh the users who are the hubs of these recommendation “networks”),  but could also boost the use and quality of social networks – particularly on mobile. After all, shopping is an on-the-fly activity where buying on impulse is the norm.

Put another way, users would have an incentive to build, maintain and grow their social networks – and place mobile at their core. Now there isn’t just strength in numbers, there’s a benefit in creating more tightly-knit networks. Members have yet another reason to get to know each other well enough to suggest content that will likely be well-received (and not simply collect “friends” to show who has the bigger network) and brands who are on the level with their offers and incentives can also wring some value out of these relationships.

Right now, networks like Twitter & Co. are predominantly passive. It’s not about things people could/should be doing/experiencing; it’s broadcasting news about “me.” I am here; I saw this; I am eating this…But mobile social networks could be a platform for discovery and an important feedback loop that guides us as we choose, flavor and commit to content, apps and everything else we come across on the journey.

Pranav and I both miss a deeper discussion of how to factor social networks into recommendations and harness mobile communications to ensure a continuing and contextual conversation between members. Let’s start one here…

• Posted in
• Web 2.0, Mobile Research, Mobile Social Networking, Recommendation, Mobile Advertising, Content Discovery

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