The Haus Viewpoint
January 18, 2023
The Haus team brings together product, growth, data, and science leaders from some of the world's largest and most iconic brands. We've spent collective decades experiencing how hard it is to understand & measure marketing effectiveness.
We learned that the most sophisticated advertisers were breaking their dependence on user identifiers like IDFA and 3P cookies long before Apple, Google, and privacy regulations forced it on them. Brands like Netflix and Amazon were – and still are – investing in homegrown tools & systems to see the effects of marketing in their own books, instead of relying on 3rd parties & publishers. We've observed 3 major trends that led us to build Haus and empower all brands to adopt this technology.
Problems in marketing measurement have existed for years.
It is important to recognize and address the issues that plagued marketing measurement – long before the wave of privacy regulations – in order to develop effective solutions moving forward.
It’s all built on correlation (not causation)
- Real-time ad platform optimization is really good at finding people who are going to convert anyway, which leads to these platforms 'stealing' attribution and understating true costs. Many ad platforms take credit on attribution while driving no new business for you. Last click attribution further exacerbates the correlation problem by attributing performance to the lower-funnel channels and tactics that are often the least incremental.
Purposeful confusion in the world of digital marketing
- Vanity metrics like impressions, views, and clicks confuse the true business impact for advertisers. On top of that, publishers are all offering their own proprietary 1st party solutions. The fragmentation of the ad ecosystem will make reconciling performance across publishers harder than ever, as each new entrant builds their own walled garden solutions. The walls are only getting higher.
Deep distrust of vendors
- It came as no surprise to us that a common theme in our customer research was a deep distrust of vendor solutions that tend to overpromise and underdeliver. As one growth leader put it, “we are trained not to trust vendors”. Brands are rightfully suspicious of existing solutions, and feel like they’ve been let down.
Consumer privacy is fuel on the fire.
The backbone of the free internet was open data sharing and a complete lack of consumer privacy. Big tech used this data (understandably) to build the most profitable businesses in history. We were the product. Now, however, consumer privacy initiatives like GDPR, CCPA, iOS14.5 and 3p cookie deprecation have changed this for good.
The impact on marketing is severe
- With more and more users opting out of tracking, these privacy regulations have dramatically hindered marketing effectiveness and personalization. The precision with which Google and Meta are able to target advertisements has been severely impacted, and customer acquisition costs across the industry are rising as a result. Not only are ads less effective, but measurement is breaking down as well. This will lead to more black box products, more modeled conversions, and reports that don’t add up.
There is no going back…
- Every day we read new headlines about consumer privacy initiatives and privacy related settlements in big tech. While we don’t profess to have a crystal ball, it seems very unlikely that in a few years we’ll be having a conversation about how this wave of privacy legislation was just a fad. This movement is inevitable, and brands must protect against the inevitability of even more strict policies to come.
Existing tools are incompatible with the new world
- Measurement solutions like multi-touch attribution (MTA) are in a state of deprecation. Without a personal identifier, you cannot map conversion paths across digital media channels. And the extent to which it worked before is questionable – attribution rules across multiple touchpoints were arbitrary, and almost never adjusted for incrementality. We are seeing that industry privacy changes are the wakeup call that brands need to start focusing on their own data and future proofing their marketing for a new era.
We are building Haus to democratize access to world-class decision science tools.
This new paradigm calls for new tools. We believe that the next generation of leading brands will need to better understand the causal relationship between the actions they take and their impact on the business. Yet, marketing data is messy and often incompatible with the econometric models that enable higher quality decision making. This is why we built Haus – to unlock world class analysis that even Nobel Laureate scientists would consider robust.
- We believe every brand should have a simple, clear, and explainable understanding of what drives their business without having to rely on stitched 3p data - but that doesn't mean working with 1p data is easy. Haus offers benefits of scale to solve the complex data questions that are painful to tackle on your own.
- We believe that if you’re investing in paid media, you should be able to prove that it is driving incremental value to your business. Brands must calibrate platform attribution or MMM with experiments and incrementality testing, and we are building Haus to make this possible.
- We believe that causal inference and incrementality should be available to all companies, not just big tech. We are former leaders from FAANG who have seen the massive investment it takes to build these tools the right way.
- We believe in facilitating accurate decisions that increase profit over waiting around for statistical significance. Performance marketing is both an art and a science - and the path to higher profitability is to blend robust incrementality estimates with expert judgment.
- We believe in being realistic about what we can do for brands. While our current customers have already saved millions of dollars working with us, we don’t claim to be a panacea. We are a toolkit to inform smarter and quicker decision making.
- We believe that incentives are important. Our mission is to empower you with answers – not to grow your marketing budget. We believe there is as much value in validating what is working, as there is in discovering what is not.
- We believe that beyond marketing, every business will need to make better use of their own, first party data. Causal inference and experimentation infrastructure is critical tooling to answer business questions across organizations in an approachable and understandable way…without compromising scientific integrity. We envision a world where answering business questions is as easy as a google search.
We’d love to hear what you think as we continue to build the next generation of decision science tooling. We are looking for future-thinking growth and analytics teams who are excited to pioneer a new approach that can make an immediate impact on your business. Reach out to learn more at firstname.lastname@example.org.