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What's the vision behind the work Sean Billings and I are doing at OutPoint, and why do we think it is so important?
This year, global ad spend will jump 10.2% to a record $651 Billion (with a B) in 2021 after falling 4.1% in 2020. But behind the record growth numbers lies a persistent problem around wasted spend: according to data aggregated from 70 studies, the median estimate is that 40% or $260 Billion of that spend will go to waste.
Wasted spend is, clearly, a huge economic problem for advertisers. Neither businesses or consumers benefit, creating a significant deadweight loss that only benefits a few large incumbents content with maintaining the status quo:
A deadweight loss is a cost to society as a whole that is generated by an economically inefficient allocation of resources within the market. A drop in the efficiency of resource allocation matters because it results in a reduction in welfare throughout society. This is highly significant because welfare, in economic terms, refers to a society’s living standards and overall prosperity. A deadweight loss is typically measured in terms of income or GDP (gross domestic product). - The Intelligent Economist
Our in-house definition of wasted spend is media dollars that do not map to business performance (including brand building) or are well past the point of diminishing and negative returns. To reduce waste, it is optimal to make ad spend decisions based on an understanding of incremental value, cross-channel impacts, and marginal returns. The causal effect of showing an ad to a potential audience or not, commonly referred to as “incrementality,” is a central question related to advertising effectiveness, reducing wasted spending, and budgeting for optimal growth
Suppose we can work to reduce the deadweight loss by re-allocating inefficient dollars to a more effective channel. In that case, the revenue of the businesses we serve increases, creating more potential for economic growth, job creation, customer relationships, and opportunity.
Our mission is thus to grow the size of the digital economy through marketing science. Stripe and Shopify are two inspirations for the OutPoint vision: these companies articulate missions around enabling GDP growth and "arming the rebels" against the status quo.
Alternatives to fix the problem
Growth-oriented companies are not blind to the multi-billion dollar 'wasted spend problem' and deploy tremendous resources trying to fix it, in search of higher return on ad spend (ROAS).
It is now possible to leverage data to model scenarios to reduce waste and improve effectiveness of ad spend, but it is challenging, complicated work.
Here's what the world looks like today:
Google Analytics Channel Reporting
For an example, check out this job description for Sr. Data Scientist, Growth Algorithms at Wealthsimple to see how a leading brand defines the marketing science opportunity:
Wealthsimple Job description
As a modern marketer, you’re leveraging a wider array of channels and tactics to reach your customers than ever before. The question is, how do you know which activities are actually driving revenue and profit, and where you should be spending to maximize impact? With Nielsen’s Marketing Mix Modeling solution, you can assess the impact of your investments, see what’s working and optimize your marketing and spend accordingly.
We think the problem is best solved with data science and technology, paired with an understanding of marketing fundamentals.
With OutPoint, you can simulate optimal paid media mix across current and new paid channels. OutPoint's models apply media mix modeling principles in real-time, using machine learning and causal inference. Our solution is a forward-looking decision-making system that aids marketers across the following areas:
In conclusion, we think there is a better way forward to improve effectiveness, reduce waste, and help companies scale paid growth. This path forward relies on data science and software.
Does this sound like an interesting and valuable problem to you? We're hiring for a Full-stack Engineer and expect to have many more roles available across data and engineering in the months to come.