4 minutes

Looking beyond mentions: How to better understand product preference in social audiences

In recent years the consumer market has completely shifted.  We now live in this Age of the Consumer where choices are plentiful and consumers control the buying cycle. With a multitude of product options available, plus peer feedback and customer reviews everywhere, finding the right product that meets their needs has never been easier.

To combat this, brands have tried to adapt by using data and buying signals to become as relevant as possible with consumers to hopefully rise above the noise.

The problem though, is that brands have been leveraging a lot of one-dimensional buying signals as true buying intent. Online technologies, including ad retargeting and cookies, have the potential to give businesses useful information on their audiences; however, they’re also responsible for a lot of false signals. Just think about the times your child has looked up a destination for a school project, and now you’re being followed around the internet with ads reminding you to book your flight to Ukraine.

 

Instead of using one-off signals to make marketing campaigns appear more relevant, organizations need to take a step back and understand their consumers underlying patterns. Achieving this goal isn’t tricky. Businesses can analyze multiple data points, that when viewed together, give a truer sense about their audience and their interests.

As an example of this, let’s take a look at social data. Social listening platforms have become par for the course for understanding what is happening in the social landscape. By analyzing mentions, these platforms are able to tell you how often particular terms are discussed, what the patterns and trends look like, as well as what the sentiment is around those conversations. Social marketing to these audiences is very linear. It’s as easy as exporting the audiences that are part of this conversation and then uploading them to a social platform as a Custom Audience. You can also choose to focus on directly targeting those who have mentioned particular keywords or who are following a particular brand or handle.

The problem with this type of approach is that before launching any kind of campaign, you’re missing the opportunity to ask and address a fundamental component of marketing: who is this audience?  


By understanding an audience, you are uncovering their true passions, and therefore can create campaigns with much greater relevance.  To illustrate this, let’s take a look at what an audience-first approach looks like when analyzing the conversation around #NationalCerealDay. Looking past mention counts, Affinio is able to determine that this hashtag was used globally by 51,179 unique accounts on Twitter. By then analyzing everything else those individuals are following, 8 unique interest-based audience clusters automatically form off their shared interests.

These interest-based clusters aren’t naturally forming because of a single interest point that these audiences have in common, but rather because of the string of interests that they are all following.

For example, by looking at the Sports Fans cluster, we can see they share interests on everything from media choices such as SportsCenter or Bleacher Report, to celebrities and TV personalities like Adam Schefter and Ian Rapoport. They’re also interested in various sports leagues, including the NFL and MLB.

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Looking at a different audience cluster such as Wrestling Fans shows a completely different audience make-up. Unsurprisingly, they follow a number of WWE wrestlers.

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Analyzing these unique audience interest clusters, though, is only half the battle to creating a campaign that is relevant. The other part of this equation comes down to which of your product lines spark the most interest among audience segments?

By leveraging features such as Benchmarks, you have the ability to input a list of all your brands (or even your competitors if you want) to quickly identify which brands have the highest affinities in which audience clusters.

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With this data, you can uncover insights such as, the groups talking about #NationalCerealDay who are also interested in News & Politics prefer Honey Bunches of Oats. On the other hand, those that fall into the Gamers cluster have a higher affinity to Frosted Flakes, and those that were classified as Sports Fans prefer Cinnamon Toast Crunch.

Coupling which brands have the highest affinities into particular clusters arms marketers not just with the signals about how to best reach an audience, but also which product or brand to promote to them. This helps to ultimately build more relevant campaigns that will rise above the noise of the current advertising world.

Curious about how your brand can identify hidden audience segments & build relevant campaigns for them? Be sure to Request a Demo today.

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