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Measuring Advanced TV campaigns effectively with InfoSum

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Measuring Advanced TV campaigns effectively with InfoSum

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Measuring Advanced TV campaigns effectively with InfoSum

Measuring Advanced TV campaigns effectively with InfoSum

Measuring Advanced TV campaigns effectively with InfoSum
Richard Lloyd
Written by:
Richard Lloyd
Tuesday, August 24, 2021

Advanced TV in all its forms continues to enjoy meteoric growth, with CTV spend in the US alone forecast to reach over $13Bn by the end of 2021 according to eMarketer. It’s hardly surprising therefore that measurement of these new forms of TV media is top of mind for brands and media owners alike.

Measurement of advanced TV presents some interesting and unique challenges, however. Whilst most forms of advanced TV have similarities to conventional digital media, in that impressions are delivered independently to individual consumers (or at the very least devices), often using similar ad technologies, the last click measurement methodologies which have dominated digital media for the best part of two decades are difficult to apply to advanced TV. 

There are a host of reasons for this, but they include the lack of cookie-like identifiers in VOD environments such as smart TV apps, more limited access to delivery data (as a result of media owner policy restrictions for example) and inevitable disconnects between the device(s) used to consume the media, and the device(s) used to “respond” to the advertising, for example by visiting a brand website or buying a product. 

At the same time, the use of panel-based or econometric measurement techniques, for so long the mainstay of linear TV, have their own problems, with panels often lacking the coverage to accurately extrapolate precision targeted campaigns and econometric models lacking the required resolution. Measurement of advanced TV often feels like it’s stuck between the proverbial rock and hard place as a result.

Advanced TV therefore requires a fundamentally different approach to measurement, one which acknowledges the intrinsic fragmentation of identity and activity and preserves the ability to accurately determine cause and effect. Over the last 12 months we’ve been working with some of the UK’s largest and most progressive broadcasters and advertisers to do exactly that.

With InfoSum, brands and media owners can collaborate to accurately and transparently measure campaign effectiveness, all while retaining full control of their data. Media owners have access to campaign exposure data and can safely make it available for brands to analyze and generate custom measurement reports to fit their needs.

In the platform, brands can verify that their campaign reached the right audience using real people data, generate custom post-campaign audience insights, and measure effectiveness with incrementality measurement, which calculates if there is a lift in the desired outcome after exposure.

Verify the campaign reached the right audience

Audience verification is a process that confirms a campaign reached the right target audience. Techniques vary depending on how the target segment was defined, but brands always need both an exposure dataset and a truth dataset. In traditional broadcast TV advertising, audience verification would be done using a panel as a representation of delivery and to analyze the composition of the audience.

With InfoSum audience verification can be done using real people data. The truth set contains the definition of the target audience chosen by the brand, and the exposure data coming from the media owner contains individuals that were exposed to the campaign. A simple intersection between the target audience (truth set) and the exposed dataset will confirm what percentage of the individuals served the advertising were part of the target audience. 

Audience verification diagram

If the audience was defined outside of the InfoSum platform, or if there’s been some audience expansion, and a simple match wouldn’t be a fair representative, a second or third-party dataset can be used as the truth set. For example the truth audience can be defined using socio-demo profiles or behavioral attributes such as purchases. In this case, after the data match, a segment is created with the target attributes. If the percentage of individuals that match the target segment is higher in the exposure set than the truth set, the campaign was on target. 

Create custom post-campaign audience insights

Traditionally, post-campaign insights come in the form of a pre-packaged report produced by a third party and the brand has little influence into how these insights are calculated. Access to the underlying data is restricted and though the insights are useful they might not be completely actionable.

The InfoSum platform gives brands the flexibility to generate their own insights into campaign performance, using their first party data, such as their own CRM segments, alone or in combination with second or third-party datasets.

For example, by analyzing this exposure data in tandem with outcome data, such as website visits, brands can determine the combination of audience attributes that performed best. Using these insights, brands can build more customized audience segments and optimize their targeting for future campaigns. 

Run incrementality measurement

Incrementality is a popular measurement technique that determines the increase or lift in a certain outcome compared to a baseline. Incrementality is increasingly used as a measure of effectiveness over traditional attribution because a fragmented media landscape makes it difficult to accurately attribute outcomes to one medium directly. An experiment based approach, similar to A/B testing, incrementality compares behavior with and without exposure is a better reflection of the advertising’s impact on a brand’s bottomline. 

To run incrementality measurement, brands and media owners need to define a control group who will not see the campaign. Then the behavior corresponding to a certain outcome, for example sales, can be compared across the group exposed to the campaign and the control group. If a larger percentage of the exposed group had the desired behavior, then the campaign had a positive impact.

In the InfoSum platform, brands are truly in control of their measurement. Brands can control that the methodology is correct by defining the exposed and control group in the platform alongside the media owner. Then, once the campaign has finished, brands can use their own outcome data or license second or third-party data to ensure the measurement fits their needs. Once all the data is available for analysis a series of simple queries can return all the figures needed to calculate the campaign’s lift.

Incrementality works at all stages of the purchase funnel, with outcome metrics such as:

Funnel stage Metrics Who owns this data?
Awareness Website visits Brands
Intention surveys (eg brand awareness) Third-party measurement partners
Consideration Added product to cart Brand or retailer
Sales Sales Brand or retailer

Delivering better outcomes for brands, media owners and consumers

InfoSum’s approach not only provides brands and media owners with the tools they need to accurately measure success, it also prioritizes the privacy of consumers and delivers better customer experiences. Our proprietary ‘non-movement of data’ technology, combined with best-in-class privacy controls, ensure that no consumer is ever at risk. Powerful insights can be unlocked that enable brands and media owners to continuously improve the advertising experience, while ensuring personal data is never shared with a third-party, and individual identities are never exposed.

InfoSum empowers brands and media agencies to work directly with media owners to measure campaign effectiveness, instead of relying solely on third parties. By collaborating in this way, the planning and measurement process is streamlined, and custom measurement strategies can be created that fit exactly their requirements This means they have full control and transparency over how campaign success is measured. 

Learn more: www.infosum.com/platform

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