Calculating Charli D’Amelio’s massive sales lift for Dunkin’ — StatSocial Influencer Attribution Use Case

Calculating Charli D’Amelio’s massive sales lift for Dunkin’ — StatSocial Influencer Attribution Use Case

In September it was announced that 16-year old TikTok superstar, Charli D’Amelio, would be partnering with international coffee and doughnut chain, Dunkin’.

It has been widely reported is that the campaign has been a success, with the drink selling like crazy, and Dunkin’ seeing a 57% increase in downloads of their mobile app. But how much $ are Charli‘s fans spending at Dunkin’ now? That’s what StatSocial can uniquely answer.

StatSocial took a look at the data surrounding Charli’s social audience and Dunkin’s customers. We analyzed the buying behaviors of these two groups, first from over the past 12 months, and then focusing on those purchases made during the campaign’s duration (September 2020). By comparing Charli‘s fans to this control group, StatSocial has shown that there’s been a significant sales lift within Charli‘s social audience.

Charli D'Amelio - Dunkin' Influencer Attribution


To generate an influencer attribution use case, you need a few different data points to bring together:

  • Dunkin’ U.S. Sales Data for the past twelve months
  • Dunkin’ U.S. Sales Data for the month of September (the 30 days during which the campaign was live)
  • Charli‘s social audience (people who follow and engage with her across her social channels)
  • A control audience (the average social media user who is not a Charli fan)

We start by overlaying and indexing the control audience with Dunkin’ sales data for both the past year and the month of September. What we found is that there was a natural lift in the month of September for the average U.S. consumer on social, with a sales index of 120 over the year preceding the campaign’s launch (20% more than the average U.S. consumer) increasing to an index of 130 in September. This increase is possibly due to seasonal effects — people tend to buy more coffee and donuts as the weather cools down.

The next step in our analysis requires us to compare the control group above to Charli‘s fan’s behaviors when it comes to spending their money on Dunkin’. Over the 12 months prior to the launch of the promotion, Charli‘s fans spent considerably less on Dunkin’ than the average social user (our control audience, as noted above), with a sales index of 107 (7% above the average U.S. consumer). However, for the month of September, her fan’s sales index exploded to an index of 144 (44% greater than the U.S. consumer average), considerably outpacing both the average for the previous year, and the control group.



StatSocial’s founder Michael Hussey writes about “What’s left when the cookie goes away?” over at Search Engine Watch

StatSocial’s founder Michael Hussey writes about “What’s left when the cookie goes away?” over at Search Engine Watch

Over at Search Engine Watch, StatSocial‘s President and Founder, Michael Hussey has written a terrific piece entitled, “What’s left when the cookie goes away?

StatSocial‘s President and Founder, Michael Hussey is seen here visibly pondering the soon-to-be cookie-less world. 

In the piece, Michael chimes in on the on the ongoing discourse surrounding the phasing out of cookie tracking technology (as was announced by Google back in January).

Mike wastes no time eulogizing the cookie’s demise, however, and instead looks to the future and the many opportunities this sea change will provide:

Below is an excerpt to whet your appetite before clicking through to read the article for yourself (which we, of course, encourage):

Many marketing tech companies are built on the faceless calculations of cookie-based tracking, targeting, and attribution. But as the general public came to understand how their data is being traded and used, their concerns sufficiently inspired regulators to come up with ordinances like GDPR and CCPA. Google’s decision to eliminate the cookie will make its own dealings with regulators easier, but it also forced a lot of companies who benefited from the ecosystem to rethink their own data practices from the ground up.

And so the demise of the cookie presents us with an opportunity – both for consumers and data-dependent organizations. What arises to replace the cookie in the coming years should lead to a more accurate, honest, and valuable digital ecosystem.

Thanks to Search Engine Watch for allowing Michael the chance to share his thoughts, and again we encourage all reading to head over there to check out the piece in full for yourselves.

“Talking Influence” — StatSocial on Influencer Attribution

“Talking Influence” — StatSocial on Influencer Attribution

Over at the industry leading publication, Talking Influence, StatSocial‘s Founder and President, Michael Hussey has written a piece entitled, “Attribution: The Missing Link for Influencer Marketing?

After a brief outline of social media marketing’s evolution (leading us to our current, influencer-centric times), Michael writes about the degree to which reliable attribution is absent when assessing the ROI generated from influencer marketing campaigns.

Influencer AttributionInstagram robot influencer (actual robot), Lil Miquela, doing a paid partnership promotion with Barney’s.

He explains that brands know that working with influencers makes an impact, but currently the evidential connective tissue that would prove definitively in what ways, and to what degrees — what he equates to the “missing link”  — is elusive.

He summarizes the difficulties as such:

The problem persists that earned media and influencer marketing have yet to develop effective attribution models. When a social influencer or blogger produces content favorable to a specific brand, the monetary value of that uplift has been impossible to track with any real accuracy because it doesn’t correspond with specific sales data regarding who saw it and made a purchase. In a world of diminishing ad budgets and rising costs, success or failure can depend on very slim margins of efficiency.

The answer? Identity solutions for influencer audiences. Michael concludes:

The evidence the market is looking for will arise when visionary data scientists apply the right technology to connect the dots. Once that happens, the influencer market with its proven attribution models will continue to change and evolve, just as other marketing channels do.

In order to make the influencer attribution equation work, a model needs to account for the quality and quantity of their reach on social networks, the nature and quality of their content, and other variables that tie influence to revenue. Ever cautious of their budgets, advertisers need better tools for influencer and social attribution if it’s going to take its rightful place in the performance marketing toolkit.

We encourage you to head over to head over to Talking Influence and read the entire piece for yourself by clicking through here,