Optimizing Connected TV for Performance

​Ad Tech is starting to evolve again, so it is time to take a look at it, poke at it, and offer a viewpoint on where it is going. To do this, it is necessary to sound a touch clinical in framing what’s up before providing an opinion on where it is going.

Performance

Performance is about effectiveness and efficiency. Effectiveness: “what works”, and efficiency: “doing it for the least amount of money”.
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​Effectiveness entails identifying, targeting, and converting prospects into sales. To do this efficiently, you need to unify the measurements of these activities and then forecast, optimize plans (for optimal return on investment), and activate opportunities.

Organizing

The key data are prospects, exposures, duplications, presale steps, and sales.
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​For Connected TV, we need exposure patterns for prospects to forecast where we are likely to find them. Prospects are population segments. Exposures are tracked in panels and census samples. Since panels can only report broad segments like gender and ages, it is necessary to combine information from panels and censuses to discern exposure patterns for prospect segments.

Census & Panel Data

Census samples measure all activities of specific apps or devices. Panel samples relate data across different census samples and in best cases projects them to universes.
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​Panels are used as general estimation tools, or as calibrators for census data.  In the latter case, narrow segments of census data can be projected to national estimates.

Often overlapping samples work better together.  As an example, a smart TV census sample can gauge the duplication between linear and streaming TV, while a “projectable” panel sample can be used to calibrate that smart TV data using geography and devices to create a converged measurement.  Even without granular data from one or both of the sources, you can still leverage the aggregated patterns to inform both duplication and projections.

Pixels

Advertisers can implant pixels on their digital assets (ads, websites, apps, and presale steps) to track and connect exposures to outcomes. ​

Sellers often offer to implant pixels on a buyer's digital assets to provide an audit of their performance. If the seller also supplies which programs the ad were on and when they were viewed, this can be used with the viewing planning data sources to evaluate which opportunities have better engagement.

To connect pixels deterministically, it is necessary to have a device graph. When an activity happens like “the ad has been successfully served”, the pixel “fires”, announcing the date, time, device, and sometimes location. The device graph says which devices have the same owners. The graph connects multiple activities to specific devices which can then be mapped through similar graphs to specific homes and people. 

If privacy rules do not allow deterministic connections, then proximity matching (or more nuanced probability scores to create synthetic agents) using date, time, and more generalized location data such as post code and post code profiles can be an excellent near-term substitute and long-term solution, as the emerging privacy laws will require this. 

If Advertisers are managing their own pixels, they can often connect them all the way to sales. Brand direct advertisers generally know the people who they are selling to. Brand advertisers that sell through intermediary retailers need to leverage other census and panel data sources focused on sales to make this connection. This can likewise be done either deterministically or probabilistically.

Segments

A segment represents a specific slice of the universe. In the case of prospects, they are potential purchasers. Segments are generally used to target potential behaviors with the purpose of converting potentials into action. ​

Advertisers often know past purchasers. They leverage this list of people to find similar people, through “look-a-like” models, to target a larger list of potential purchasers. Sometimes Advertisers buy characteristics or a list of people who purchase competitive or similar products to expand their scope of potential purchasers.

These segments become their targets, and the advertiser then plans how to convert potentials into action through various communication investments.

Planning

The original focus of measuring direct response to digital ads conflated media planning and buying. The immediacy of direct response to gauge performance has led media investment decisions to become tactical and in some cases real-time. This makes tremendous sense for sales of known products that have no brand differentiation or message. ​

For advertisers that want to create brands to enhance pricing and sales opportunities, a messaging plan prior to activating messages is necessary. Branding objectives and hence their effectiveness measures vary. 

What’s the best way to plan for effectiveness?  Firstly, we need ways to measure it.  Like: how many times and how often has your target segment seen the ad?  Content Performance or: “Was the context right?”  (Some messages work better than others in a video about airplane crashes!).  Most importantly, does the message resonate with the people you’re trying to reach?  This is typically measured with branding surveys and A/B testing messages.

These effectiveness measures then become inputs to planning future campaigns. For newer brands, these can be based on look-a-like brand benchmarks or market test results.

Wielding these inputs to plan forward involves mountains of data processing to figure out a brand’s potential and the least expensive way of getting there. This is where planning pivots from focusing on effectiveness measures to efficiency.

Viewpoint

Adtech is evolving from two connected quagmires: privacy and operations. In “The Rise and Fall of Deterministic Analytics”, we addressed the impact of privacy on data ownership, management, and attribution modeling. ​

As privacy challenges arise, Advertisers are seeing their response data becoming more integral to internal strategies and operations, with analytics and workflows becoming more automated. This inward pull and customization of adtech is disrupting large vertically integrated, external adtech solutions. Friction is increasing. Large tech companies want to control the adtech ecosystem while advertisers want to integrate the data and tech into their own enterprise resource planning (ERP) systems.

The new players chomping at the adtech pie will be the ERPs and the modular players, who can plug-and-play and reside in anyone’s cloud.

Oracle exemplifies ERP companies trying to incorporate adtech. 

MediaBrain is a modular player that enhances ERPs. Its OptiBrain module ingests effectiveness measures and reports out optimal plans that deliver the most effective potential for a given price or guide decisions by framing the best price solutions for different levels of effectiveness.

Companies that provide modular solutions are natural partners for the ERPs. Modular components enable innovation through enhancements and replacements without disrupting mature ecosystems. Adtech modules allow ERP platforms to evolve with the digital transformation of advertising and communications