Read my mind: adding recommendation to the mobile search mix
Understanding users can’t buy content if they can’t find it, an increasing number of mobile operators and content providers are scrambling to offer mobile search capabilities as well as an array of tools that will encourage users to explore more of the content at their fingertips.
The raft of recent announcements, involving tier-1 mobile operators and market giants such as Google, Yahoo!, Microsoft and a growing number of white-label search providers, including InfoSpace, Fast Search & Transfer, Medio Systems and JumpTap, as well as mobile search platform provider, Mobile Content Networks (MCN), shows carriers and content companies are clearly excited about mobile search.
But operators that merely retrofit Web search solutions for the mobile Internet ultimately short change themselves and their users. Popular Web search engines such as Google are fatally flawed. They were designed to treat all searches – and searchers – equal. While the approach consistently delivers the same list of links in response to key words, it fails to recognize the need of individual searchers for relevant results that really matter.
Push vs pull business models
More importantly, such search schemes patently ignore the shift in the business model from user-pull to content-push. Pull is built on the premise that users know what they want and are prepared to go look for it. That’s quite an assumption when it comes to fast-paced content such as entertainment and multimedia which changes faster than users can keep up. The pull model also ignores the rise of empowered customers who increasingly expect – even demand – content and services consistently tailored to their individual needs and in tune with their lifestyles and life stages.
The new paradigm is personalized content-push based on a deep understanding of the individual’s purchases, passions and past click-behavior. It’s even more compelling if the technology can learn users’ likes and dislikes over time to dynamically and consistently deliver the right content mix.
The message is getting through, which is why this year will see the usual suspects experimenting with techniques and technologies to deliver personalized mobile search. Perhaps the most vocal on the market is Yahoo!.
Personalized search and relevant results are concepts that run like a leitmotiv through the company’s new product offer and its future roadmap. A prime example of this new direction is Yahoo! oneSearch, a Web 2.0-type search engine picks up on users’ intent, intuits the information they want and then presents the relevant content, grouped by subject, in synopsis form. A sports search on oneSearch, for example, will return a relevant bundle of scores from a team’s most recent game, along with game schedules, team rosters, photos, local results, and so on.
Adding user choice to the mix
While search does assist in delivering a better end-user experience, the much more lucrative business opportunity may be in combining search, personalization and recommendation to provide users personalized and relevant results – as well as the tools to discover other content they might not have otherwise known existed.
An increasing number of vendors are clued into recommendations and are lining up to wield the power of the analytics they generate. Some, like Medio Systems, are borrowing from the Amazon.com approach to suggest content on the basis of the individual user’s past preferences or on the basis of what a user’s peers consumed, or both. Others including MyStrands, Gracenote and ChoiceStream, making the transition from online to mobile, have cleverly combined their music recommender capabilities and social networking to help users connect with both content and the like-minded members of their community who share the same tastes.
Indeed, the sheer variety of personalization and recommendation solutions enables a multiplicity of business models the mobile industry is only beginning to explore. Moving forward, personalization and recommendation will be must-have features of mobile content services.
mPortal, for example, creates personalized recommendations based on users’ billing records, download history and purchase habits. The company is taking this analysis to the next level, drilling down in the customer data to create ‘content referrals’, and so allow users to share content tips with their friends and family.
Given the highly personalized nature of the mobile phone and the way people use it, there is a high probability that people will buy content and services if they know a friend – and not an operator – is making the suggestion. It’s clearly in operators’ interests to deliver effective and targeted commerce experiences to their customers. While recommendations based on customer information such as page views and downloads will be an important part of this strategy, it will be the recommendations from the tight-knit communities users know and trust that clinch the sale.