Reaching the right audience is a vital element to keep in mind when developing your influencer marketing campaigns. You want to identify influencers with followers from your desired target audience because their followers are your potential customers.
Why does the age distribution of influencers’ followers matter?
You should know the ideal gender, age and market location, because what’s worse than investing in a campaign that’s not even seen by your potential customer?
This is where InfluencerDB comes in: We are proud to announce the launch of our brand new and highly-anticipated age distribution feature!
Depending on which age group a brand wants to target, the age distribution of the followers can have an extremely high impact on the true reach of this channel. Our new feature helps marketers to really pinpoint their desired audience carefully and improve the effectiveness of their collaborations.
André Cimander, Chief Technology Officer
Where do I find the age distribution data?
Find the new statistics within the Audience Data tab for any influencer. While you’re there, check out the new appearance of the Audience Data as well. The bar charts provide for an even better overview of the age and gender distribution of an influencer’s audience and are easier to read and interpret.
Age distribution of @leoniehanne’s audience
Note: We also added an LDA (legal drinking age) compliance label which lets you know whether at least 71.6% of a channel’s followers are expected to be of legal purchase age (21 and older). Depending on your industry, this information can be vital for targeting the right audience!
How is the age distribution data generated?
To make the age distribution of influencer audiences available, our data science team worked relentlessly on an artificial intelligence-based process over the course of the last few months. The software analyzes publicly available user avatars of followers’ Instagram channels.
From these avatars, the software generates accurate age statements regarding the audience of influencer channels on Instagram.
We use state-of-the-art algorithms based on recent deep neural network architectures and transfer learning. A true challenge is to cope with all sorts of biases and corresponding overfitting in the training process.
To give you an example: imagine the majority of young people would wear sunglasses, then a poorly trained model could easily fall into the trap and classify all people with sunglasses as young. In the future, we will constantly update our models to avoid such problems and to steadily improve the accuracies of our results.
Dr. Bernard Sonnenschein, Director of Research & Data Science
We hope that this new feature will be a valuable addition to our established audience analytics and support you in identifying influencers with a great fit for your brand.