The engagement metric is basically the CTR normalised to remove machine traffic
Before we dive into the detail I will point out the influencer numbers are overstated if you choose to exclude the click throughs generated by other influencers.
To help you understand the difference between our normalised model and the industry metric the oft quoted engagement for Facebook Ads is 0.9%. Influencers is 2.5%
As you can see these experiments reveal that, when it came to driving traffic, our collection of Organic TikTok videos significantly outperformed both Facebook Ads and Instagram Influencers
Moreover our organic TikTok videos generated the same level of engagement as promoted by the Influencer Industry
This suggests we may have engaged the wrong influencers for our experiment
But that raises the question: What is the right type of influencer?
The simple answer is the Influencer market mirrors the digital marketplace
20% of the market drives 80% of the engagement
and 20% of that top 20% drives 64% of the engagement
Why? Because the influencer market is another example of the long tail of digital economics
There is also the Influencer Paradox: The more followers you have the less influence (Engagement) you will have on the behaviour of the crowd (ie it is easier to influence 10 followers than 100000)
By choosing to game the platform to attract an audience the influencers choose growth over intimacy
Basically the lesson here is averages are useless when evaluating or predicting performance in the digital space
Now this revelation may come as surprise to many.
Especially now that influencer marketing has become one of those must have strategies in the world of eCommerce
We believe this is because few, if any, marketeers appear to be equipped to evaluate the ROI on leasing influence
So we have built a model to help you understand what works and what doesn't in the world of influence
You can begin by clicking on the buttons below to reset the calculator to the industry benchmarks
Alternatively you can adjust the campaign costs by manually changing the influencer campaign and product metrics
Note: We recommend the RRP cost of the goods shipped to the influencer as the minimum cost of creative
BTW: Exceptional performance = Top 10% of the market
The first thing you will note is the published social media industry benchmarks of influencer engagement are significantly better than the results of our recent experiments
However experimenting with the default buttons will quickly reveal influencer marketing, like most paid advertising channels, can be an expensive channel for acquiring 'one off' retail sales
You will also discover Reach (Think: Follower Count) is only one part of the Influencer Equation.
Engagement with the post or video (Think: CTR) and your ability to convert that traffic into carts and sales are just as important in delivering ROI on your influencer campaign
Gross margins also play a key role
The key takeaway from the model is - for your influencer campaign to succeed both you and your influencer have to perform exceptionally. Or, at the very least, well above average
And yes, the same rules of thumb apply to generating traffic from Organic TikTok videos
You need to be creative. You need to deliver exceptional content to generate interest
But you don't need to engage an influencer to do this on your behalf
You just need to engage with a strong, vibrant creative team capable of generating engaging TikTok videos that promote your brand, grow your follower count and ultimately, increase your influence
Part 2 of this ongoing Organic TikTok vs vs Facebook Ads vs Instagram Influencers Experiment can be found here
Disclaimer
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Questions of accuracy
Tymbals is an experiment in machine learning and statistical modelling of small data pools. Tymbals is still under development. It is still learning. Tymbals is Beta - i.e. Pre-release.
The probabilities and outputs (e.g. calculators, charts, visualisations) will evolve and change as the system ingests more data.
Tymbals is a probability model. The results generated by Tymbals are market estimates based on the cummulative value of the data within the distributed data pools.
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