The Secret to Social Media Success is HUMAN: Measuring Heart and Head

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 its most basic, the secret to social media success is HUMAN!

I found this great graphic from the Harvard Business Review and Gartner that reflects this duality of branding that matches the notion that social media success comes through a meeting between the heart and head — between data analytics and understanding human behavior and creativity. Today, I want to discuss this duality and how to use both your heart and head to create social media marketing success.

social media success
Image courtesy of Gartner and HBR

The secret to social media success is in the heart and head

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 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. Brain-driven analytical tools include the classics from decades of marketing research:

  • classic survey research and newer qualitative tactics such as focus groups
  • ethnographic tools borrowed from anthropology to understand consumer behavior by embedding the person within their broader culture
  • sentiment analysis that involves assessing positive, neutral, and negative attitudes toward the brand
  • cluster to find naturally occurring segments within consumer markets such as those based on demographics as well as psychological and lifestyle choices to select one or more target markets
  • optimization tactics such as regression analysis to determine the most influential factors impacting consumer behavior

When I taught students enrolled in a graduate-level marketing course working for Fortune 50 companies like GE, Toyota, and P&G, they were taught that numbers were all that mattered. Plus, most were engineers who aren’t known for their human side when it comes to business.

Now some of you are reading this statement and responding, “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. Because you also need the human side of branding if you want to achieve success, especially in social and digital marketing campaigns. In this transit map developed by Garter, you see their efforts to optimize performance that combines creative, analytical, and operations management.

the modern customer journey map
Image courtesy of Gartner

The human side of branding includes:

  • ideation to discover problems faced by consumers that aren’t being met by existing offerings. The Chrysler minivan, for instance, observed drivers going about their normal daily activities to discover that they needed a vehicle that drove like a car with a larger storage capacity for people and things, especially one that could transform from carrying passengers to carrying stuff (or both). This car was the most successful new product type in many years of car innovation. Social listening and curation are key tools for this type of ideation.
  • serendipity comes from tinkering around with stuff to discover something that proves valuable. For instance, the Covid-19 vaccine was built on an understanding of mRNA developed without a clear idea of how it might prove useful. It saved millions of lives.
  • crowdsourcing involves using members of the community to develop ideas. For instance, Frito-Lay used this model to develop new flavors for their chip products. Because consumers suggested alternative flavors and other consumers voted up their favorites, Frito-Lay captured an untapped market where they faced little competition with weird flavors that no product development team would consider, like Flaming Hot Dill Pickle and Kobe Steak. Rapid response is necessary to gain first mover advantage from this feedback.

The allure of big data drives attempts to analyze your way to success

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. Let’s take a look at a few businesses that seem determined to ignore other aspects that provide insights to help optimize business performance.

data-driven b2b marketing strategy
b2b business to business marketing deal agreement between two company with some graph and chart statistic data – vector illustration

Walmart

I remember I once allowed a C-suite-level Walmart executive into my class. Her presentation was ostensibly to share Walmart’s best practices, but the subtle subtext was recruiting newly minted MBAs into the Walmart machine.

Ms. WalMart VP rattled on for 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 with 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.

Really?

Now, I’d recently finished reading the pseudo-autobiography of Walmart’s founder, 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 a vacation to increase the diversity of stores he visited.

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 lost faith in strategic guidance using data alone. While not entirely relinquishing his love of data, Lafley now agrees there’s nothing inherently scientific about data and he is opening himself up to other types of intelligence, including human intelligence in his return to the brand.

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 by adding human insights

Bridging “scientific” data-driven analysis with more human understanding 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. Sometimes, as we saw earlier, there’s even a failure to respect that the human insights developed have anything to contribute to effectively running a business.

I don’t think combining the two types of intelligence is impossible. It’s just challenging and businesses should embrace challenges with such rich potential to improve their market performance.

data visualization
Image courtesy of Bi Happy Blog

Text analytics

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 argued 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

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 (Google Ads, Facebook-promoted posts, Twitter (now X) 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.

Human understandings

Now, you might use big data to build human understanding, but you’re as likely to be wrong as right. That’s because human behavior- which 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?
what users want to hear from brands
Image courtesy of Sprout Social

Over the last couple of weeks, I’ve had numerous discussions with a new client regarding the art and science of social media analytics. After several years of trying to generate revenue for his business with social media, he’s finally turned to me because he’s just not getting the ROI he needs alone. Luckily, he didn’t give up on social media, as do many similar mid-sized businesses, believing social media won’t work for their particular industry or target market.

After several weeks of analyzing his metrics and doing some judicious A/B testing, I’ve learned a lot about why he’s not seeing the market performance he hoped. He had some content marketing issues that we’re working to fix, poor SEO performance, and was misusing some social media platforms. Today, however, I’d like to talk about what I discovered in his social media analytics report that is already creating big returns for his efforts.

The art and science of social media analytics: combining to optimize performance

Interpreting social media analytics is really part art and part science — or maybe two parts art and one part science. A dollop of experience and a smidgen of intuition doesn’t hurt either. Jim Sterne, the founder of the Digital Analytics Association, describes a data arts combining necessary attributes as someone who has:

a firm comprehension of hard science, a sound understanding of business goals and processes, a penchant for creativity, and a talent for communication — a very rare combination.

And, that’s my challenge in explaining to my client why he needs to change his social media marketing strategy.

Sometimes, I think I’m getting through to him. On other days, we joust at windmills — with him returning to ask and me answering the same questions already discussed several times. Personally, I think it’s the dollop of experience and a smidgen of intuition that separates our perspectives on what is the “RIGHT” social media marketing strategy.

KPIs, vanity metrics, and conversion

Ultimately, a brand seeks conversion from its social media marketing strategy, that’s the definition of social media success. With accurate metrics from Google Analytics, Facebook Insights, etc. analyzing conversion isn’t really that hard. The pathway to obtaining that conversion isn’t so clear.

Vanity metrics are those metrics that don’t clearly contribute to the success of your social media marketing strategy — in other words, don’t impact conversion. We often talk about Facebook Fans, Twitter (now X) Followers, etc as vanity metrics because, while everyone likes to list these as KPIs, they’re nearly useless because there’s only the most tenuous of corrections between vanity metrics and conversion values. Slavishly accumulating higher performance across vanity metrics is a waste of resources and can seriously derail your social media marketing strategy, according to Social Media Today. Probably that’s why there’s so much variation in estimates of the value of a Facebook Fan.

Explaining this to my client, I have to acknowledge that a Facebook Fan isn’t ENTIRELY useless, as each reflects a certain ability to spread awareness of my client’s business but only when that follower engages with the brand by commenting or sharing the post. Likes don’t translate into spreading the post in the way they used to before Meta introduced the variety of responses you can have to a post such as laughing, caring, etc. The question really becomes, what happens when awareness spreads? Hence, the art and science of social media analytics.

Here’s the scenario: He shares posts on his Facebook page, which has a really respectable number of Fans. He’s spending $1000s a week to boost these posts.

I won’t share his insights to protect his privacy and ensure confidentiality, but they aren’t good.

My argument is that he needs to re-evaluate his Facebook spending. Sure, he’s getting a bunch of new fans and great reach by boosting his posts. He’s also getting good engagement on his posts — which is NOT a vanity metric. Post engagement reflects folks who’ve liked, commented, or shared a post, which not only increases awareness, it acts as a tacit endorsement of the brand messaging contained in the post.

Despite showing some good numbers, his conversion rate sucks — a very technical evaluative term only used by seasoned analytics professionals. He’s driving a ton of traffic to his site, but less than .1% convert. Really sucky (another technical term).

He argues for the continuation of his strategy because it yields great numbers (even arguing for increasing his spend) and I argue for a change in strategy to get higher conversion. Of course, we need a better understanding of why we’re so successful in creating a social media marketing strategy capable of generating great KPIs and vanity metrics, but really bad at generating a conversion. Is it the CTA (call to action)? Is it something in our segmentation strategy (which is my personal suspicion even though I can’t put my finger on WHY I think it’s the target market)? We should focus on the problem first, possibly running some A/B tests before jumping into making changes.

Conclusion

Social media success (and, more broadly digital marketing success) depends on using analytical tools and combining them with those developed to understand how consumers form relationships, attitudes toward brands, and how they are impacted by those in their communities (including online communities). Thinking with both sides of our brains and testing assumptions will generate higher conversion and mean success for our marketing strategies.

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