I know I post a lot about social media tactics and the importance of analytics on this site. That’s because they ARE important. But don’t forget that social media marketing is SOCIAL, which means it has to be human — human connections, human stories, human emotions. At it’s most basic, the secret to social media success is HUMAN!
Data alone isn’t what makes the marketing needle move for business … True brand intelligence lives at the intersection of mind and heart
That quote from a recent HBR post about sums up the secret to social media success — blending the human elements with strong data analytics. Either one alone is much less powerful than the 2 together.
The graphic shows different types of marketing intelligence, both data-driven analytics and human-driven.
Now, some of you are reading this saying, “Duh, of course. We knew that. It’s not much of a secret.”
I can tell you you’re wrong. It is a BIG secret. Maybe a few examples will help.
Related Resources from B2C
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The allure of big data
It’s really not new. Brand marketers long believed you could successfully run a business with analytics. In fact, the discipline of business intelligence relies solely on analytics.
I remember I once allowed a C-suite level WalMart executive into my class. Her presentation was ostensibly to share WalMart best practices, but the subtle subtext was recruiting newly minted MBAs into the WalMart machine.
Ms. WalMart VP rattled on far too long about how brilliant WalMart is and all the analytics used to optimize merchandising ROI. I was so proud of my students because they asked the hard questions that ferreted out huge failures in WalMart’s strategy.
Students quickly understood WalMart’s obsession for measuring everything and learning nothing. Most decisions were based on simple heuristics. For instance, when asked about how WalMart determines how many Christmas ornaments to buy, the venerable VP answered they just add a fixed percentage to last year’s order.
Now, I’d recently finished reading the pseudo-autobiography of Sam Walton, Sam Walton: Made In America, which I recommend highly. In the book, Walton clearly attributes much of his success to his habit of randomly visiting stores and just shooting the breeze with customers and employees, even stockroom workers. He even routed through various WalMart stores when heading out for vacation.
Here, not even a generation after his death, Walton’s kids were substituting data for human understanding. And, the thinnest kind of data — descriptive statistics, rather than deeper, predictive analytics. It’s not surprising that some WalMart stores are now seeing declining year-on-year sales and some predict WalMart’s best days are behind it.
P & G
When I taught at Xavier University, many of my MBA students were folks from P&G, Toyota, and GE. Very smart people, but very stupid human beings.
Following the treatise of AG Lafley, former CEO of P&G (now returned to that role), managers aspiring to the C-suite believed data was their ticket to the big penthouse office. However, after the horrific financial crisis of 2008, even Lafley himself lost faith in strategic guidance using data along. While not entirely relinquishing his love of data, Lafley now agrees there’s nothing inherently scientific about data and opening himself up to other types of intelligence, including human intelligence.
When Lafley first assumed leadership of P&G, he was the savior for brands that had lost much of their luster over the years. His focus on innovation, marketing, and strategy using analytics-driven insights was just what the flailing company needed. Lafley’s engineering focus on number crunching worked — until the 2008 economic crisis, when tough economic times revealed the importance of human understanding in supplementing data-driven insights.
Here’s the meat of Lafley’s argument:
Many managers feel they are doomed to weigh the futile rigor of ordinary strategic planning processes against the hit-or-miss creativity of the alternatives. We believe the two can be reconciled to produce creative but realistic strategies. The key is to recognize that conventional strategic planning is not actually scientific. Yes, the scientific method is marked by rigorous analysis, and conventional strategic planning has plenty of that. But also integral to the scientific method are the creation of novel hypotheses and the careful generation of custom-tailored tests of those hypotheses—two elements that conventional strategic planning typically lacks.
Creating social media success
Bridging “scientific” data-driven analysis with more human understandings is challenging. In part, this is an epistemological problem — a fancy word that really just means that people who are good at one type of intelligence often fail to understand the other type of intelligence or lack the skills to develop understandings outside of their particular data-analysis stream.
I don’t think it is impossible o combine the 2 types of intelligence. It’s just challenging and businesses should embrace challenges with such rich potential to improve their market performance.
As an example, I recently spoke at the Text Analytics Summit — you can see my presentation here.
In the presentation, Mark Eduljee, head of social listening at Microsoft, and I made the business case for building deeper understandings from social media posts. Rather than simply condense rich social media utterances to sentiment numbers or using artificial intelligence to form categories comprising the topic of these posts, we argue businesses must use the attitudes, beliefs, emotions, and how connections respond to the post if a brand wants to develop a deep understanding of the potential of the brand and improve that potential, resulting in superior market performance.
Tracking sentiment over time is a gut-check on how well your brand is doing overall, but simple sentiment analysis doesn’t tell you WHY consumers feel better or worse about your brand.
Big data are seductive. As Lafley argues, numbers contain reality. They appear scientific and scientific management is BETTER. And, certainly running your business based on numbers is MUCH better than running it based on your instincts.
Data is really good for developing certain types of understandings:
- How many people visited my site and where did they come from?
- How did people move through my site and what factors impeded their conversion?
- Which posts did visitors (or Fans or Followers) like best/ least ?
- Which advertising (Adwords, Facebook promoted posts, Twitter ads, etc) converted the most visitors?
You get the idea. Big data is best at answering how much, how many, which, when, and where types of questions.
Now, you might use big data to build human understandings, but you’re as likely to be wrong as right. That’s because human behavior — which is what you’re inherently measuring when you use big data — isn’t a perfect predictor of attitudes, beliefs, feelings or future behavior. It’s just that — past behavior.
Yet, as a brand, you’re only mildly interested in what happened. You’re primarily interested in what’s going to happen in the future. As a marketing strategist, it’s nice to know that something you did worked, but you really need to know WHY it worked if you hope to reproduce the social media success you experienced — or avoid social media failure.
Human understandings are best for answering questions like:
- Why did consumers buy a competitor’s brand rather than mine?
- Why do some consumers complain about my brand?
- Why do some connections share my content (or engage in other ways) and some don’t?
- What do consumers need that I’m not giving them right now?
- If I did X, how would consumers respond?
Whether you need a complete analytics strategy, some help with brand marketing, or some consulting to optimize your existing social media marketing, we can fill your digital marketing funnel. We can help you do your own social media marketing better or do it for you with our community managers, strategists, and account executives. You can request a FREE introductory meeting or sign up for my email newsletter to learn more about social media marketing.