Is it really surprising that on social media, generally speaking, people share more emotionally linked thoughts?
For my money this is not much of an insight. After all, humans, like many other animals, are social creatures. From birth, our lives depend on others. In time, those who bring us along and introduce us to the ways of the world nurture specific beliefs and frame our understanding of the world. Our connections to others are vital to our survival, happiness and success.
Social media simplifies our ability to share and connect. The social impulse that compels us to take part naturally mirrors underlying, maybe even unconscious emotions. The result is a natural association between content and intention rooted in sentiment. Following the tradition of anthropology, or design research, self-reported assertions such as our tweets or Facebook updates can prove revealing. Tracking and tallying these qualitative data crumbs outline a wider system of association linkages and are wonderful additions to descriptive analysis. Whether linked specifically to more traditional demographic variables or not, they show characteristics, detect relationships about something or someone; but are no proportional in their representation.
So what’s the problem? Insights don’t scale. The accompanying graphics suggests that there’s added value, and maybe there is for the casual observer, but at the moment I’m not convinced.
Last week, I shared lunch with a group of people familiar with both quantitative and qualitative research methods to talk about big data. Design, or anthropology, research methods focus on observing very small groups of subjects in natural conditions. Watching people as they shop, work, make dinner, go to work etc. The data and analysis skews to the qualitative. Watching what people do has always proved to be more reliable a predictor than asking what they think. Researchers long ago discovered the knowing vs. doing gap.
For the less statistically inclined, probability sampling is necessary but not itself sufficient to make claims about a larger population group. Exercising diligence in selecting a random sample to ask a series of questions, or observe them can still produce bias or large errors in the results if input from those who respond or were readily available are included. All surveys include a margin of error due to sampling. National voter exit polls, for example, carefully sample to keep their margin of error for a 95% confidence interval low, e.g. about +/- 3% . ( For further information check out: Edison research on exit polls) The margin of error on public opinion polls asking what people believe and for whom they plan to vote is wider than the post voting survey results taken at the polls.
Diary studies illustrate the value in subjective research. Sure, the results are challenging to extend and difficult to scale as the richness of this data does not easily lend to classic systems analysis. Often in the hands of the experienced researcher, the subtle presence or absence of contextual cues lead to new insights, or deeper understanding of the situation, or present circumstances responsible for a behavior. Researchers isolating the specific cues come closer to understanding our inner nature and then developing insights into cause and effect.
Build it and….
The inspiration implied in the phrase if you build it they will come, suggests knowledge of what and how to build, this intuition may come from subjective research. Note, the phrase is neither strategic or predictive of the number or timing of visitors. Contrast anecdotal indicators to an algorithm churning through significant quantities of transactions to find common elements, the co-related information. Observational data offer context, while the algorithm provides the measure of total significance.
If we’ve learned anything from the work of the behavioral economists, humans are predictably irrational. Why? The relative strength of an emotion can but doesn’t necessarily overcome reason. The contextual elements trigger both specific behaviors, as well as unexpected associations and very different behaviors.
We are far from understanding how to successfully integrate expressed wants social media provides with analysis of objective, aggregate data.
As Steve Smith, of Pegasus Capital Advisors suggests, there is great power in pushing the economics analysis up the value chain. Social media doesn’t create the transaction, the risks focus on reputation which has implications but has yet to disrupt the flow or more accurately allocation of capital.
I’m looking forward to seeing the continuing evolution of social media and the teams of marketing analysts familiar with statistical sampling to help chart a new course. It would be
great if they can help lead the charge toward a more robust metric of success. One that favors the quadruple bottom line and thus captures Environmental, Social, Cultural (including governance) and. Economic factors.