Data visualization is a potent tool for brands. But there’s a right way and a wrong way to do it. Insights from visualized data are impactful to a brand’s decision-makers when executed correctly. Poor data visualizations fail to provide helpful intel and also create misleading impressions that send businesses off course. With that in mind, we’ll look at how to visualize data the right way.
To do so, we’ll quickly break it down with a focus on:
- Why data visualizations are so powerful
- How inaccurate data visualizations are a net negative
- How to separate accurate vs. inaccurate data visualizations
Before we jump in, here are a few facts and figures on the importance of accurate consumer understanding as an incentive for accurate visualizations:
- From a customer experience (CX) perspective, only 3% of consumers will complain to a company directly, while 25.1% will take to social media or review sites. If your social listening data visualizations are missing any of this, you could be staring at a false positive.
- Accuracy is critical in understanding and addressing the voice of the customer (VoC) since it costs between 6-7 times more to win a new customer than to keep one you’ve already won over.
- Data quality issues can distort your metrics on campaign success and lead to lower customer satisfaction and retention.
With that, let’s take a peek under the hood of data visualization and why it’s critical to get it right.
Why Data Visualization is so Powerful
The fundamental reason that data visualizations are so powerful is that our brains are hardwired to process visuals exponentially faster than text. That being the case, a dataset consisting of a wall of text or numbers will take us longer to gain insight from as opposed to seeing the same data visually.
When we visualize data, we can grab the takeaway from the dataset instantly. For example, if we are running a social listening analysis and visualize the data, we can see straight away what the most important talking points in the query from the size of our topic clusters are.
We can also see related themes as well as outlying narratives on the fringes of the network. To drive home the point with a real-life example, look below at a recent social media analysis of electric vehicles (EVs) mapped to a visualized data network.
We can see at a glance that Tesla models, markets and EV battery conversations are most central to the overall discussion around electric vehicles. These are surrounded by larger and interconnected topic clusters that represent sub-themes. As a whole, these clusters give us a quick glimpse of the most significant themes within our data set.
Understanding the relationships between these themes in our data and their interconnectivity is intuitive. For instance, we can see the wall street cluster leans heavily into EV stocks with noticeable ties into markets, batteries and renewable energy.
Quite simply, an intuitive understanding of data relationships is the power of visualized data. With a brief look, we’ve gained a holistic view of how social media users are talking about EVs. Whether you are getting insight from social or traditional media, M&A and patent intel or your internal data sets, data visualizations lay out your data like a buffet for fast insight.
Inaccurate Data Visualization is Equally Harmful Though …
While data visualizations help you do the heavy lifting in finding intel to benefit your brand, there is nothing to be had from a dirty data set. And the implications can be dire, especially if you use it to form your brand strategy.
This is particularly important when cleaning your data sets from social media or news and blogs. Language is nuanced already, but factor into that the changing nature of hashtags and similar brand names, and you have a recipe for inaccuracy. Likewise, reposts and promotional content can heavily skew a dataset if not cut away.
Every data analyst has come across the inevitable topic that requires clearing away a mountain of noise to answer the question at hand. To get to the bottom of your brand conversation, social listening will test your data analytics tools’ limits. The same is often true to get to the bottom of a media conversation. Extensive use of Boolean search operators and robust filtering capabilities are a top priority if you want your data visualizations to show you something useable. Otherwise, it’s a waste of time. Here’s an extreme example.
Suppose your consumer and market intelligence requires an understanding of Smoothie King’s brand. In that case, negligence or faulty tools will leave you thinking they have a more significant media presence than they actually do. That’s because an NBA team, the New Orleans Pelicans, calls the Smoothie King Center home – and it has more media traction than the brand itself.
As you can see, this data set would need some serious adjustment before it’d be helpful to your purposes. Sure, this is a blatantly obvious example once you visualize it, but that’s not always the case. Since accuracy and clarity are the goals with our data sets, let’s look at how we can clean them up for peak insights.
How to Separate Accurate vs. Inaccurate Data Visualizations
A house is only as good as the foundation it’s built on. Similarly, all the patience in the world won’t pull actionable insights from substandard data analytics tools. Since VoC is an area that brands routinely have trouble with, getting accurate data sets from the get-go is mission-critical.
If you’re approaching a brand question or topic for the first time, sometimes it makes sense to visualize a simple search query right off the bat. This can be beneficial if you need to understand common ways to rephrase things or spot significant noise areas to filter out when you begin building your principal analysis.
Imagine for a moment that your client or brand needs in-depth historical insight into coronavirus vaccine reception. That’s a tall order bound to have heaps of noise, so your topic-building game has to be on point. And your tools have to be up to the challenge. It’s not hard to imagine the mountains of misinformation, conspiracy theories, propaganda and political commentary that you’ll have to move out of the way to get real answers.
A simple search of coronavirus vaccines is utterly unusable, but the liberal application of laser-specific Boolean operators and filters leaves us with a data set we have confidence in. As you can see below, the network map is super clean and organized.
Once you know that your data sets are 100% clean, then you can draw insight from them, assured that you are on top of actionable intelligence. On the social side of things, reposts and bot authors are two of the leading killers of accurate intel, so make sure you’ve dealt with them accordingly. Since we’ve pushed our tool hard to cut out the noise, we’re ready to provide our client with the intel they’re looking for.
From this example, we found that Pfizer is getting the most traction and volume by a long shot. Here’s a data visualization of the top topic clusters by total engagement, revealing that hope and excitement are trending hard.
Actionable insight is at the mercy of your data analytics tool’s capability to dig deep and cut out the noise. In today’s business climate, acting on inaccurate intel can be deadly. Suppose you’re monitoring your brand health or VoC conversations. In that case, you definitely need a tool that allows you to get transparent with your data. Sparkling clean data sets lead to topic clusters that tell the real story.
Are your data visualizations leaving you with a feeling of uncertainty? Your consumer, competitive and market intelligence depend on accuracy. Reach out for a demo, and we’ll put the power of impactful data visualizations at your fingertips.