Musings from the Thesis team on Growth Marketing, Conversion Rate Optimization, and more.
At the suggestion of our Snapchat rep, we tested Snapchat's Lifestyle Categories that an account indexed strongly with to see how they performed relative to our standard broad targeting.
A basic introduction to providing ad networks' with a feedback loop that helps their algorithms optimize for payback period rather than pure CAC.
Every new TikTok ad group will come with a prompt suggesting interest and behavior categories. We wanted to test these recommendations and see how they perform relative to broad targeting.
At Thesis, we’ve developed a testing methodology that generates more learnings, quicker, while also protecting our core scaling campaigns from creative flops. We’ll show you exactly how we set up these creative tests and generate more creative wins for our clients.
A detailed look at how Ad Relevance Rankings correlate with actual performance.
Inspired by a client with whom we’ve seen delivery shift dramatically away from iOS and towards Android, we wanted to see if this was part of a broad shift on the platform.
We wanted to establish some basic hook rate benchmarks for our own internal consumption and we thought these might be useful as a point of reference for the broader ecosystem.
We wanted to revisit our Prospecting ROAS benchmarks from February, given that we are now living in a post-iOS14.5 world. For the sake of comparison, we did our best to make this data comparable to our ROAS benchmarks from February 2021.
The best way I could think of to share what I’m actually seeing in the Facebook/Instagram universe was to do an aggregate analysis of a handful of ad accounts based on very recent data.
Another attempt to look at platform-wide dynamics post iOS14.5 through the lens of Thesis clients' data.
A feeble attempt to try to understand how Facebook's performance as a user acquisition channel has changed since the iOS14.5 rollout.
Dara & Barry of the Thesis growth team answer the top 7 questions we’ve seen thus far about the iOS14 changes and their impact on Facebook campaigns.
Nothing is more important than creative in today’s paid social landscape. Research shows that creative is responsible for ~50% of performance variance.
If detailed targeting is used to "guide" ad delivery, how far off is the DTC equivalent of Facebook's Automated App Ads?
The second post in our paid search account audit series in which we take a look at search keyword match type distributions.
When managing subscription-only ad campaigns on Facebook, return on ad spend becomes nearly meaningless. Profitability rarely ever happens after the first purchase but typically comes from a user’s lifetime value as they submit recurring payments months into the future.
It’s time to admit the days of relying on a social platform’s native ads manager to evaluate and optimize ad performance are likely coming to a close. In 2021, Blended ROAS will be our first step as advertisers to overcome this wrench in platform attribution.
When we audit prospective new client accounts, I always look for tell-tale signs that they might have an incrementality problem.
Like every other online advertiser, we see differences between what Google Analytics records on a last click basis and what Facebook's pixel reports, even when using a 1-day click attribution window. We wondered if there may be consistent discrepancies, so we dove into the data.
It’s estimated that the average person sees between 6,000 to 10,000 ads every single day, so it's incredibly difficult for your ad to cut through the clutter if someone only sees it once a month. What really is the golden number of times to reach a user?
I very rarely see D2C brands with meaningful YouTube ad spend being measured on a performance basis. For this post, I'll be breaking down the basics of the setup for Brand X, which spends just north of $600k per month on prospecting on YouTube.
iOS 14 is forcing Facebook to implement a variety of platform changes, and one of these changes is the removal of placement breakdowns. With this loss of data looming around the corner, it’s more important than ever to understand where this feature is distributing your ad spend.
At Thesis we perform new ad account audits nearly every day. To aid those conversations, I've maintained my own internal ROAS benchmarks for reference. In this post, I've sanitized that ROAS data so that I can share it for general consumption!
Despite launching ads for the first time in just 2019, TikTok has quickly become a go-to platform for industry leaders. TikTok is vastly different from what we’re used to on Facebook in two different capacities: measurement and creative.
These days an overwhelming amount of attention is paid to video creative, and for good reason! Thesis now offers creative services including video production and post-production, and I find that most discussions online about FB/IG creative focus primarily on video assets.
In advance of Apple’s promise to crack down on user tracking on iOS, Facebook has begun implementing a series of updates to the platform that will forever change the way advertisers optimize and collect data.
As we've grown Thesis, I've taken a much greater interest in the agency ecosystem specifically. I'm curious about how agencies decide where to focus, how they position their services, and how they execute.
I've spent time this year doing ad hoc analysis of year-over-year January performance for a handful of subscription ecomm brands. To make sure I wasn't missing any macro trends, I analyzed Facebook campaign data for a basket of North American focused, D2C subscription clients.
Thanks to my work with Thesis, I have the opportunity to regularly audit paid search accounts from various ecommerce/D2C and lead gen brands. Over the years, I've developed a few quick reports that I use to gauge account health & expansion opportunities.
Privacy regulations are a new challenge marketers need to overcome, as platforms like Facebook are increasingly implementing their own requirements. And the results of non-compliance are dramatically impacting conversions.
Here is a sample summary of this blog post. We'll give you the gist in just a few sentences.
Prior to starting Thesis, I dabbled with content arbitrage/slideshow sites. These sites featured awful content, misleading ad placements, and worse. One benefit of this work is that I spent a great deal of time looking at native ads, and came across a lot of effective DR funnels.
I'm obsessed with incrementality testing, and generally skeptical about paid retargeting and view-through attribution. With that obsession in mind, I'm happy to share the results of a number of lift tests that the Thesis team and our clients have run over the last 6 months.