Wouldn’t it be great if you knew exactly what your customers were thinking about your brand? And how those opinions were shifting over time?
As a matter of fact, you can. It’s all about measuring brand sentiment. While sentiment might seem like a complex thing to measure, thanks to modern technology, it can be broken down, quantified, and measured just like any other metric.
In the digital age, brands hold more power than ever before, shaping consumer perceptions and driving purchasing decisions. As a result, understanding how your brand is perceived by your target audience has become a paramount concern for businesses striving to build meaningful connections and long-lasting loyalty. This is where brand sentiment analysis steps in—a valuable tool that goes beyond surface-level metrics to gauge the emotional pulse of your customers towards your brand.
Join us as we embark on this illuminating journey into the realm of brand sentiment analysis. Whether you’re a seasoned marketer or a brand new to this area of study, our aim is to provide you with the knowledge and tools needed to harness the power of brand sentiment analysis effectively. By measuring and understanding how your audience feels about your brand, you can take confident strides towards making data-driven decisions that elevate your brand’s reputation and secure its position in the hearts of your customers.
Let’s dive into this transformative exploration of brand sentiment analysis together and unlock the keys to lasting brand success.
How to Measure Brand Sentiment
The way in which we measure brand sentiment has changed dramatically over the years, all thanks to the internet.
In the past, brand sentiment was measured through surveys and focus groups. This was inherently flawed. When you ask your customers directly for their opinions, you’re unintentionally shaping them – our opinions are influenced by our surroundings and what we’re ‘expected’ to say.
Thankfully, there’s a new and improved way of measuring brand sentiment: social media listening. Brands can ‘listen in’ on the conversations their customers are having on the internet about their brand using social media listening tools to extract a wealth of data from which marketers can calculate their brand sentiment.
We’ll get to what these tools are and how they work in a moment, but first, let’s talk about what we’re actually listening out for.
Brand sentiment metrics
The most obvious way to track brand sentiment is to look for mentions of your brand name across the social space.
However, it’s not good enough to just look at the number of brand mentions. If hundreds of more people are talking about your brand today than they were last week, it isn’t necessarily a good thing. In fact, it could be very, very bad.
The keyword here is context. You don’t just want to know how many people are talking about your brand, you need to know what they’re saying too and whether it’s good or bad.
To figure this out, rather than checking each individual brand mention ourselves, we can look at broad metrics like:
- Number of comments
- Tone of comments
- Tone of brand mentions
- Engagement statistics and trends
- Volume and frequency of brand mentions
We can then use these metrics to arrive at a quantitative figure that tells us our current brand sentiment.
How do we track these metrics? With social media listening tools, of course!
Social Media Listening Tools
Social media listening tools are tools that add context to all those brand mentions, comments, and shares. They automatically crawl the web to find conversations about your brand, then filter and categorize those conversations based on their ‘tone’.
They’ll typically categorize each comment/mention of your brand as either ‘positive’, ‘negative’, or ‘neutral’, depending on the context.
For example, let’s imagine you run an eco-friendly coffee cup retailer called ‘The Green Cup Company’. Here’s how a few different social media comments about your brand might be categorised:
- “Just got my @GreenCupCompany cup today in the post – absolutely love it!” – positive
- “Anyone else sick to death of companies like @GreenCupCompany cashing in on value signaling? #climatehoax” – negative
- “@GreenCupCompany Are your cups dishwasher friendly?” – neutral
These tools will show you the total number of positive, negative, and neutral comments so that you can see a snapshot of your brand sentiment at a glance. You can track this over time by looking at percentages. For example, if you have 33% positive comments this month, you could set a target to move the needle up to 40% by the end of the year.
There are a bunch of these kinds of tools out there. One of our favourites is brand24.com, but there are many more, including Hootsuite Insights, Social Mention, and Quick Search.
How Accurate Are They?
While sentiment analysis tools can be very effective, it’s important to remember that their algorithms aren’t perfect, so the data isn’t always 100% accurate.
To assess sentiment, the software typically looks for ‘keywords’ that indicate tone. When ‘positive’ words appearing alongside your brand, it’s categorised as a positive comment, and the same vice-versa.
This sometimes leads to comments being incorrectly categorised. For example, take a look at this hypothetical comment:
“Props to the @GreenCupCompany for putting all these evil corporations and their disposable plastic cups to shame. Stop filling our oceans with garbage and get yourself a reusable cup!” – negative
Here’s another example:
“Wow, been waiting 4 weeks for my cup to arrive in the post. Great job Green Cup Company…” – positive
This time, the commenter is clearly being sarcastic; he’s not at all satisfied with his product delivery times. Unfortunately, the social media listening tool’s algorithm isn’t advanced enough to pick up on sarcasm just yet, so it mistakenly takes the comment at face value and categorises it as positive.
Nonetheless, despite these inaccuracies, these tools can still provide valuable insight and give you a good overview of your brand sentiment. As the technology these tools are built on grows more advanced, it’s likely that they will also become increasingly accurate.