What’s all the Buzz about?
In the earlier post about separating Signals and Noise, I mentioned the Jones Stone Arch Bridge Here it is, in all its autumn glory.
Recently, I’ve used this image as an analogy to describe how we can bridge from communication outputs to business outcomes. Why do companies covered by the press need to build that bridge? Because news drives shifts in brand perceptions. News influences purchase intent. News factors into customer satisfaction scores. And in extreme cases, news-triggered shocks can cut sales and diminish market share significantly.
Beyond that, an inability to monitor, measure and at times mitigate these news impacts, in my mind, has marginalized the standing of communication — and communicators. By crossing this bridge, business communicators can finally generate tactical, forward-facing insights to guide financially material business decisions.
But — there’s always a but — existing media monitoring tools will not do the job. Media monitoring produces vanity metrics. Imputes media influence. Calculates ROI incorrectly. Erroneously equates news to advertising costs. Media monitoring exists largely to demonstrate campaign success and validate PR budgets.
In order to rigorously align communication outputs to business outcomes, we will need statistically discrete data that meets business standards for reliability, validity and accuracy.
From my perspective, here’s what that research stream needs to measure. Actual readership of relevant news. Consumer perceptions about that news. And, corresponding consumer perceptions about brands, products and companies.
In business terms, it’s a waterfall. Readership -> Buzz -> Reputation.
Those are three keystones of communication research. And the actionable, forward-facing insights supported by this research will enable business communicators to cross over the bridge and finally become a strategic asset.
Travel advisory
Before we step on the bridge, a quick word about the charts below.
In this blog, we will overlay Readership — our blue columns — with line graphs showing positive and negative shifts in Buzz that correspond to the news.
Best-in-class brand trackers routinely produce Buzz metrics. YouGov’s BrandIndex, for example, asks respondents every day whether they have heard something positive or negative about a company or national brand. It’s a simple, elegant question that generates highly reliable data that has been validated and replicated over hundreds of news cycles.
Before we look at the charts, then, let’s wrap our heads around two critical concepts.
First, published news is often perceived by consumers to be neutral. There is no discernible Buzz. In those cases, we will see spikes in Readership and no corresponding shift in Buzz. That’s a key insight communicators need in order to make tactical decisions about responding to breaking news. There have been cases where companies over-reacted to otherwise neutral or short-lived news cycles, causing significant self-inflicted damage.
Second, Buzz can be both positive and negative at the same time. Because Buzz is comprised of both positive and negative consumer perceptions, charts visualizing the metric consist of two lines that at times will move in different directions simultaneously. In our charts, when the percentage of positive responses increases, the green lines will go up. When the percentage of negative responses increases, the red lines will go down. More positive Buzz = up. More negative Buzz = down. Got it?
OK, so what’s all the Buzz about?
The chart below may look somewhat familiar. This chart visualizes daily Readership of news about a Big Tech brand — the blue columns — tracked by Memo over a 12-month period. We’re adding the green line on top to represent daily positive Buzz — the percentage of people who read, heard or saw positive news about the brand. The red line on the bottom represents daily negative Buzz — the percentage of people who read, heard or saw negative news about the brand.
What does this chart tell us? A few insights. One, the most-read news coverage in this case has not triggered shifts in Buzz. If you have worked in media relations, you know the anxiety that comes from having to rely on gut instinct to advise executives on how news is landing with people. This data alleviates that anxiety. As a practical matter, you will sleep better.
We can also see on this chart that while negative Buzz is flat, positive Buzz is trending higher. This data can guide us toward a deeper understanding of how narratives feed into Buzz about the brand. In addition, for this brand, the communication team now has a vital baseline metric. If breaking news does move the needle — and it typically does — they will be able to quantify the shift and forecast the business headwinds or tailwinds.
Let’s look at another chart. In this case, the blue columns visualize the Readership of news about our Healthcare company during the COVID-19 pandemic, as reported by Memo. As we saw in Signals and Noise, Attention can rise significantly for several weeks after memorable stories were published.
Why do Attention levels rise? In the case of our Healthcare brand, about four in ten people read, hear or see something about this national brand on a given day. Attention is about 40% for this brand, while half of the Buzz is typically positive, and half is negative.
But in the weeks after those two big stories were published, more people recalled reading something negative about the brand. Negative Buzz rose from about 20% to just over 30% on the first big story, and ticked slightly higher on the second. And the news-driven headwinds in both cases lasted about a month.
What’s the takeaway here? Attention to the Healthcare company rose because more people read news that they considered to be negative. The story was in the mix for about two weeks, and within about a month the story was no longer top of mind with consumers.
Now let’s look at a more extreme example. The chart below visualizes Buzz for a fast-casual restaurant chain. The CEO in this case generated national news by commenting on a third-rail social issue during an interview with a newspaper reporter. This coverage pre-dates Memo’s Readership tracking, but I can tell you the media monitoring news volume and tonality data scored this news as highly negative. Like, defcon 1 negative.
YouGov’s Buzz tracking tells an entirely different story. While three in ten consumers perceived the news as negative, more than four in ten considered it to be positive. Ultimately, this news cycle corresponded to the largest sales day in the company’s history.
What we are looking at in this chart is polarization, an accelerating social trend that will increasingly impact leading brands covered by the press.
Lessons earned
For large companies, news Readership is the catalyst for significant shifts in consumer perceptions. That makes sense. News is consumed actively from trusted media sources. Reporters are the original influencers.
And Buzz is a touchstone metric for gauging those shifts. Based on research that we have done over the years, let’s walk through a few key learnings:
— Net-net, Buzz about national brands is generally slightly positive, but significant shifts in Buzz tend to skew negative. Intuitively, that makes sense. News coverage also skews negative.
— Buzz is an early warning indicator for significant shifts in brand Reputation. In the next blog, News moves the needle, we will walk through that relationship.
— In severe cases, the corresponding shift in Buzz becomes predictive of the depth and duration of shocks. Yes, predictive. Using these touchstone metrics, communication research will finally be able to forecast long-tail events, and inform business responses. More to come on that.
Here’s a final thought. In Tone deaf, we explored the challenges in relying on the qualities of words that are used to describe a brand as a way to gauge corresponding consumer perceptions about that news.
Think about how often negative news elicits positive responses, or vice versa. It may sound counter-intuitive, but in business this happens all the time. A company announces a quarterly loss or a massive litigation settlement. That’s negative tonality to a natural language processor. Yet, the stock prices goes up.
Here’s another example. A company announces planned layoffs. A natural language processor typically tags layoff coverage as negative. But that’s good news for investors who price in the earnings impacts. Bad news for consumers, though, who do not appreciate it when companies fire people.
Bottom line, from a communication research perspective, using news volume and tonality scores to impute news reach and resonance is futile. At this point, the outdated volume and tonality metrics, to me, are water under the bridge.
Moving on.
’Tis the season to start thinking about strategic planning for the coming year. If groundbreaking communication research is on your wish list, let’s talk shop.
Jim.Pierpoint@HeadlineRisk.com
