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Pathmatics Blog
Technology, Data Science |


Advertising Analytics: Empower Your Team with Digital Ad Data

February 20 2020 by William Merchan

"Data Science." "Artificial Intelligence." "Big Data." These are just some of the first words uttered by global brands and agencies as they look to the future of their products, teams, and internal capabilities. How do you apply--and get value-from these capabilities? Outside of the obvious use cases like fraud detection, churn forecasting, LTV etc.

Marketing technology companies have always been at the leading edge of applying technology to data given the masses of information they need to activate across their digital and non-digital channels (don't forget direct mail!). We have partnered with advanced analytics teams in finance, marketing, retail, and other categories. We thought it would helpful to share the top 3 advertising analytics use cases.

1: Incorporating digital ad data into Cross Channel Attribution and Marketing Mix Models.

We've partnered with leading firms like Neustar's brand and agency teams, to support their model development. These teams use digital ad intelligence to fill in gaps in data for their brands and estimate spend for their competition. This data goes into their models to control for market effects, or is used as a driver in the ad spend factor. In the end, this model-based approach for linking share of voice to sales improves the quality of the results.

model example



2: Using trends in spend to inform market actions

Marketing spend is a leading indicator for business performance: As digital marketing investment goes up that is a signal of a healthy growing business (and the inverse is also true). This data helps reveal revenue trends for publishers or platforms as dollars move in the digital ad market.

A prime example of this is the rise and fall of WeWork.  In reviewing their ad spend data we can see the initial growth starting in 2017, leading up to its Unicorn peak 2018.  In 2019 and 2020, we saw the hype and valuation fall from its peak.  We also see a direct correlation in their ad spending patterns. Monitoring these changes could have helped teams looking to understand the IPO and market price WeWork by reading the signal in the ad spend data.  



3: Extracting data from creatives to drive ad testing

Large-scale ad testing is now an established best practice for many leading brands. We've seen examples of this across the board for brands of all sizes who are looking to get a pulse on the market and review the strategies of their competitors. This approach helps find new opportunities in creative testing.
What can you learn from a creative?  Let's take this ad from Apple TV+ as an example.

We can pull up this Apple TV+ from Pathmatics Explorer.  We can see what dimensions, type, flighting, sites, and the landing page for this ad.  


In this example we will keep the process simple and upload the ad into the public version of the Google's Vision AI tool. After uploading this ad we now have new information to analyze, report and expand the creative executions in market.  We can see the prominence of the brand in this ad, the context and substance of the ad as well as the offers used by Apple TV+ to drive engagement and new subscribers.  We can test alternate CTAs, subscription offers, and text to optimize the performance of the ads. 
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About Author
William Merchan

William Merchan is a data science, marketing analytics, advertising technology and startup veteran. He currently serves as chief revenue officer at marketing intelligence company Pathmatics, where he is responsible for brand growth and awareness. Previously, William built products and grew teams at DataScience.com, MarketShare and Yahoo!. He holds a BS in Business from the University of California Berkeley and an MBA from the Kellogg School of Management at Northwestern.

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