Profiling hedonic data in social networks

Continuing the discussion from the McKinsey interview of Cammie Dunaway, she states

[Yahoo!] is using behavioral data–really mining the wealth of transactional data we have about how people are spending their time online and trying to marry that data with attitudinal data…that’s where the most powerful insights can really come from.

Insights into what? It could be many things. Two of the most studied motivational data elements are utilitarian motivations and hedonic motivations. Utilitarian motivations center around goal-oriented behavior (e.g., I logged in to check my email, I checked my email, I logged out). Hedonic motivations are more social in nature (e.g,. I logged in to explore, to analyze, to decide, to eventually take action).

In real estate search, companies have typically focused on rewarding utilitarian behavior, often in a very reactionary manner. Consumer searches site > Consumer registers > Consumer selects home > Consumer is “passed off” to a real estate agent. Of course, the ultimate goal is to consummate a sale. And improving the “experience” of looking for a home on a real estate firm’s website could actually lead to more loyalty, referrals, and sales.

Nevertheless, overly focusing on “experience” at the expense of a goal can scuttle both consumer loyalty and ROI. Thus, balance lies in properly testing and deploying Web 2.0 assets that fulfill consumer goals while logically jibing with the product subject matter.

So how does mining attitudinal data fit this balanced approach or paradigm? Incenting consumers to add profile information that logically fits a goal is one idea. For example, if a real estate firm’s goal was to create a social network on their site targeted at tapping a suburban soccer mom demographic looking to buy a home, logical profile information may be zip code (current residence and desired residence), schools, sports, design preferences, and home type.

Zip code is important because the firm could relate this consumer to an agent who serves that zip code, where the agent serves as the social network ombudsman(woman) to answer questions and otherwise kick-start the group. Secondly, once a firm understands home type preferences and desired location, the firm can relate specific home information, community information and statistics, and other moms in the network to this person. The additional profile information constitutes community building information (e.g., relating moms who have children in similar sports). These steps help build a community and take the burden off the real estate firm to be all things to all consumers (if a mom has questions about how her child can join a traveling baseball team, she could ask the real estate agent, but more likely she’d ask the community). This way the firm’s “social asset” reinforces the firm’s local expertise, which allows for an eventual monetization of this consumer as she “graduates” through the process into ultimately looking at home types and eventually purchasing a home.

Through the tracking of profile data combined with the interaction of the consumer with the group (communications, postings, etc) combined with accessing utilities (e.g., widget downloads pertaining to design elements, video home tours, community data, statistics, etc), a firm could create an “engagement” index to validate whether their site is properly satiating consumers’ needs (Circuit City does this). The experience of this for the consumer is not so much having real estate listings and drip marketing pushed her way, but related data presented in a way that allows her to more deeply engage in the process and begin building a community before actually living in a community. Finally, in terms life-time value, this type of a social network could operate as a forum for a firm–and its real estate agents–to cultivate a valid and meaningful long-term relationship with consumers after they have actually bought a home (thus, closing the circle by adding transactional data with previously compiled attitudinal data).

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