Technology & Data
- Big Data
- Tech & Innovation
How to Get Your Sales and Marketing Teams to Work in HarmonyContent Marketing for Midsized Companies: Whom to Target, What to CreateAtri Chatterjee of Act-On Software on the New Generation of MarketersMarketing Automation: What It Is and Why You Need to Know
- Social Tools
Join us September 15th in Atlanta for The Employee Advocacy Summit and learn how to unleash the power of your employees.
Post your event here and we'll share it with our community. If one of our members is featured, we'll promote as well on their profile.
- Marketplace & Webinars
The SMT Marketplace
Your resource for exclusive content and insights from Social Media Today, and opportunities to reach our community of professionals.
The Social Business Book Club brings you books, discussions, and insights from today's to business thought leaders.
Join interactive talks and and panel discussions with leading thinkers and practitioners on social media and networked business, or browse the catalogue of recorded sessions - all completely free.
Reach Social Media Today's community of marketing and communications professionals in an editor-approved context with a native advertising package.
Turning Social Data into Social Results
Posted on March 20th 2013
The universe now contains a lot of social data. This article is about general approaches to turning social data into social results. (It was the topic of a Social Media Today expert thinkers webinar.)
Humans are peculiar creatures: what time they do not spend socializing, they spend monitoring themselves and collecting infinite volumes of data about their own behavior. The result is what we call...
The Vast Social Data Miasma
So how do we use this swirling data cesspool to provide meaningful business insights?
For that we need a general framework:
The Three Keys to the Gates of Data Nirvana
Since we already have Key #1 covered, it becomes Questions and Interpretation that are most critical in extracting value from social data. The challenge is, however, that every brand, every business, every product, every audience is a unique snowflake floating in the endless social snowfield. And hence, although there are many useful generalizations, the precise best Questions to ask, and the exact Interpretation that applies, remains specific to every situation.
Key #2 is an often under-served aspect of data analysis. Sometimes people expect data to simply sing answers to them, without anyone having to think too much about it. Instead, deeply considering the questions that a business wants to answer is usually the most important part of the process.
We think of the Questions as residing on a Pyramid of Data Inquiry, with the deeper, more "existential" questions forming the base, through to the more questions at the tip.
The Pyramid of Data Inquiry
Interestingly, the evolution of inquiry about Facebook Posts also tracks the evolution of social media as a component of marketing budgets generally.
Next we can give some further insight as to how we use the Data we have to answer these various types of Questions.
What response do my posts get?
[From the white paper "Optimizing Facebook Engagement".]
Is the response to my posts good?
[From a client competitive study. Brand names withheld.]
Are my posts worthwhile?
[From a client performance report.]
Don't let pretty charts fool you!
Finally, we come to Key #3: Interpretation.
You can make a sexy chart about anything. Well, we can. But data always requires context, always makes assumptions, always has uncertainty - and hence always requires Interpretation.
While this is a topic worthy of an entire website, our general guidance is that the more broad and general the questions (i.e., the further down the pyramid you go) the less certain the results become, and the more open to interpretation they become.
All inquiry is valid. We use Track Social to answer questions at all levels of the Data Inquiry Pyramid. However, it is also important to understand that not everything is knowable -- and at the level of Existential Questions, social data is sometimes only able to provide directional guidance to humans, who then have to make decisions with human brains.