Paul Lindstrom
Head of Research and Analytics at Tunity
Muted TV technology is a raging adtech phenomenon and a disruptive customer experience trend. We spoke to Paul Lindstrom, Head of Research and Analytics at Tunity, to understand how it impacts the adtech ecosystem and the trends in Mobile Advertising technologies in the coming months.
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Tell us about your role at Tunity and the team/technology you handle.
I am the Head of Research and Analytics for Tunity. In this role, I look to transform the data collected via the Tunity mobile app into insights for our clients on the usage of linear TV in Out-of-Home (OOH) locations.
How do you work with analytics at Tunity?
Consumers can use the Tunity app to stream audio of linear TV channels when they are in proximity to the screen in OOH venues. We take the aggregated data generated during this activity and produce very precise measures of the viewing. Insights can include usage on a second-by-second basis by network down to individual locations or types of locations and specific age/gender.
What are the core tenets of your muted TV technology? How does it impact the adtech ecosystem with its features?
Up until the arrival of Tunity Analytics, on-going insights into OOH usage were only available through audio forms of Automatic Content Recognition (ACR). The majority of use of linear TV in OOH venues is either when the set is muted or when there is significant ambient noise. Neither of these can be handled through ACR. Tunity Analytics can change the way that people view and understand OOH usage.
How does muted TV deliver better customer experience?
The consumer value proposition is that they can stream the audio in situations where sound is not available. Tunity is using this data stream to model audiences for all muted TVs in OOH locations.
Which data streams do you rely on to build your product roadmap?
The key data streams are that of start and end times on usage of individual channels, user demographics, and GPS location information coupled with data about those locations.
How do you see trends in mobile advertising technologies improving sales and marketing ROIs?
The answer here is at least two-fold. There are holes in the current measurement systems such as the inability, prior to Tunity, to count audiences to linear TV channels when the set is muted or there is too much ambient noise. This is the majority of OOH use. Currently, it is considered zero audience. The inclusion of these missing exposures can change how media are evaluated. Secondly, the information that can be developed from mobile data is tremendous and the fact that it is keyed to an individual makes ROI metrics significantly easier. This is undoubtedly the direction the business is going.
How do you see AI and machine learning technology disrupting TV advertising performance in the future?
AI is bound to have profound impacts on the evaluation of advertising performance and, therefore, the entire advertising pipeline across both buyers and sellers. Advertising is increasingly evaluated on ROI. Advertisers want to be able to see that they got dollars in return for their investment. This is still a difficult thing to do cleanly. Unless you are looking at a form of click-through where you can directly tie an individual ad to a particular response, true ROI is still problematic. Ideally, no matter what your metric for evaluation is, you would like to be able to see if an individual ad on a particular medium contributed to the overall brand performance using that metric. True attribution (meaning being able to tell with certainty that an ad led to this result) at this point is nearly impossible at an ad level and perhaps harder still to look at synergies between individual ad exposures. AI and machine learning are likely to get us closer to this ideal. If we get there it will change the advertising and media businesses.
Would you like to be invited as a speaker at a tech conference that we partner with?
Yes. For sure.
Thanks for chatting with us, Paul.
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