How Brandwatch is Building Game-Changing New AI Features Powered by GPT
By Oliver ShawApr 20
Learn about how Brandwatch is revolutionizing the industry with game-changing new features powered by GPT.
Published July 18th 2017
With the largest influencer database of its kind, Audiences is the fastest way to discover the people that matter to you. Now you can understand who those people are and, more importantly, what makes them different.
A successful social business knows how to speak directly to their customers. But how do you keep things personal at the scale of social?
All too often the answer to this is to speak to largest or most obvious demographic we’re addressing. That’s your marketing that’s aimed at ‘Millenials’ or ‘Moms’ and usually ends up speaking to neither. Audiences are made up of more complex, diverse groups of people than this and understanding what makes yours different is the key to creating campaigns that stand out.
That vital difference is what Brandwatch Audiences is built to uncover.
“There are three kinds of lie: lies, damned lies and statistics” goes the phrase popularized by Mark Twain. Statistics can be deceptive when they are taken out of context.
That’s why when developing Audiences we brought in our very own former head of Research Services at Brandwatch as Product Manager, the excellent Bex Carson.
She’s the brains behind much of the in-depth analysis we’ve done for some of the world’s biggest organizations and helped Vice Magazine understand their target audience when launching a new TV station.
“When working with a source like social data,” explains Carson, “it’s easy to fall into the trap of identifying inherent biases in that data as ‘insights’. Your Twitter audience may appear to have a strong interest in technology, but is that just because tech is popular among Twitter users in general?”
With Audiences, any report can be benchmarked against any two other audiences as well as the whole of Twitter, allowing you to validate your insights. For example if you were looking to understand ‘Dog Lovers’ as a target customer:
You might conclude that these people are most likely female and particularly interested in Family & Parenting, Sports and Food & Drink. But what happens when we compare them to all Twitter users?
We can see that Dog Lovers are disproportionately interested in Family & Parenting, Food & Drink but Sports is a much more universal interest, thus it’s been automatically relegated lower down in the report. Books and Travel now also stand out more as being specific to this audience.
Clicking on any data-point in a report brings up a handy pop-up that explains in natural language how your audience compares to the rest of Twitter.
Because Audiences lets us freely explore, we can continue triangulating the insights we need to understand what makes our specific customer unique.
By segmenting our Dog Lovers into US and UK audiences we can see what’s specific to each region.
American dog lovers are particularly family oriented while the Brits are sportier. This can help us design content that would speak more directly to each group.
We can also use more specific background benchmarks than ‘all of Twitter’. By comparing these groups to animal lovers in general we can see that the female-heavy split is not as relevant as we thought, it’s a trend among all animal loves.
And (I know you were wondering) we can find clear differences between Dog Lovers and Cat Lovers too. The Atlantic has never divided us so profoundly.
The Audiences team at Brandwatch have built a truly unique search engine that allows you to freely search a database of over 300 million people in seconds, combining bio, tweet content, interests, professions, gender, account type and location to pinpoint the people you’re interested in.
Now you can also search and segment the followers of any Twitter account as well.
This lets us see how our captive audience differs from our competitors’. For example, Nike has a larger proportion of sports professionals following them while Adidas has attracted more students and creatives.
We can also combine sets of followers in our search, for example to study people following Adidas but not Nike, revealing that the gap is clearly to do with the brands’ international audiences.
What’s more we can explore and export all these users to identify opportunities to reach these potential customers and target them with Twitter Ads.
Drilling deeper, we can understand more about Nike’s international followers and how they compare.
Perhaps a focus on new gadgets and apps would attract this younger, more tech-savvy audience.
Follower searches can also be used to look beyond your own community to craft and study niche target audiences.
For example by combining profession searches and followers transport information accounts we can compare commuters in London and New York.
And understand more about the kind of advertising we might display on trains and buses – depending on the common interests or professions of the people riding them.
If you want to learn more about how these new features can support your audience research and marketing strategies, get in touch and we can arrange a demo.
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By Alex WoodSep 21 2022
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