Google Product Lead on Data Products (Part 2)
One of the most exciting aspects of working with data products is being able to create solutions for long-term challenges. In my current role at Google, the focus is on improving media planning for advertisers. Specifically, we’re trying to use predictive models to help them spend more efficiently on our platform.
However, this challenge extends way beyond advertising on Google or other digital platforms. Simply put, efficient spending is a challenge that advertisers have faced since the dawn of media.
First, every advertiser tries to balance their spend against business outcomes. For example, this can be getting users to download an app or generating awareness for a new movie release. Even before online advertising became a reality, this same balancing act existed with print media and radio campaigns. However, the stakes are higher with digital advertising – and data products can be used to enable advertisers to spend more effectively.
Through the years, building out a media plan includes testing out campaigns with small spends. In doing so, you’re able to gauge what will work well before launching your complete plan. However, the problem with this model is that you’re required to spend money in order to gain insights.
Recently, there have been incremental attempts to solve this problem.
That said, the solutions have all involved slightly different takes on the same fundamental approach. For me, the ability to build data products that effectively predict advertising results is a huge opportunity to change the game. In other words, my current focus is all about eliminating the friction that comes with spending on an exercise that isn’t driving toward your ultimate goal.
Specifically, the innovation driven by our advertising data products will allow advertisers to make decisions without wasting resources. Instead, all budget used for media planning can focus entirely on the finished product, rather than wasting it to verify that something will work.
In summary, I’m most excited about coming up with a completely new way to approach this common challenge. Ultimately, my job is to build data products that combine independent features into a holistic solution. Simply put, these solutions eliminate competing functions and focus on what drives positive results for customers.