Segmentations powering digital ad targeting:

Targeting advertising on the basis of individual-level segmentations is not a new idea: other organizations have collected custom survey data – time- and cost-intensive, to create static segmentation of persuadables, as a one-size-fits-all solution for digital targeting of the entire campaign. PredictWise offers a radically different approach. We have created technology than can create completely customized segmentations for a host of diverse use-cases, with the click of a button and almost in real time. In short, our AI draws on API calls layering analytics on top of our massive data base of tracking all Americans on more than 250 political, economic and psychometric dimensions. Given the low cost of computation, segmentations can be developed for specific creatives within hours, beat current much more time- and cost-intensive solutions on all relevant behavioral metrics such as click-throughs, viedeoplays, or engagement (more below), and allow campaigns to evaluate RoI in real time.


  1. Custom segmentation for custom use-cases: In the old world, creating segmentations was expensive and time intensive, due to custom data collection. With the PredictWise method, segmentations can be created with a click of a button, within hours. This is possible because we do not collect custom data. And, that means it is scalable to produce segmentations matching the content of specific creatives, yielding much higher return on investment. This has virtually no variable cost to us!
  2. True persuadability scores: As supposed to only looking at horse-race data, our segmentations fold in attitudinal data matching the content of the creative and additional measures of party strength. So, if candidate A wants to target a digital ad on expanding Medicare, our persuadables segmentation identifies soft Republicans who are tolerant toward the out party, likely to vote, and support expanding Medicare
  3. Updates: We can produce regular updates that reflect true shifts in the underlying sentiment. As campaigns progress, people should move between segments, such that a static segmentation misses the key progression of the campaigns. And, we maintain records of movement such that month-over-month changes are easily tracked, providing convenient RoI for large-scale interventions, e.g. has an issue-centered ad campaign in a certain congressional district succeeded in moving likely voters from Persuadable to In Our Camp?


In late 2018, we were approached by OpenProgress, doing work for the CA-45 (Katie Porter) campaign. The campaign was in need of a segmentation identifying the most persuadable voters to power digital ad-buys on Facebook. Two rival segmentations were available: (a) Generic segmentation using voter file data, and (b) Custom segmentation based on custom, cost- and time-intensive survey data (collected over the course of a month, with 10x our raw-cost). PredictWise, instead, created a custom segmentation for matching the primary message content of the Katie Porter’s campaign (healthcare; taxes) to likely voters inhibiting a progressive position on these dimensions, as well as tolerance toward the Democratic party and soft support for the Republican candidate, based on the PredictWise back-end data tracking 250+ dimensions for all Americans. Each creative was targeted to all audiences with a similar budget. Our segmentations beat the rival segmentations handily and significantly on all relevant behavioral metrics of engagement (Plot1). Differences are especially stark on comments, with PredictWise providing a 2x lift over the custom audience and a 3x+ lift over the generic audience.

Plot 1: RoI of Ad-buys on Facebook powered by PredictWise segmentations and rival segmentation in CA-45

These differences are robust to day of week or overall $ spent on each creative and audience. Another way of describing the PredictWise advantage: for every $1 spent additionally, you would have gotten 0.015 comments and 0.74 engagements more had you used PredictWise versus the rival audience, all else equal (significant effects; Plot 2).

Plot 2: “Premium gains” in RoI per Dollar Spent when PredictWise vs. rival segmentation in CA-45

Of course, behavioral metrics are no fool-proof indicator of persuasion – in fact there is no one fool-proof metric. For example, randomized control trials across exposed and non-exposed have (a) a hard time controlling for decay of effects, and (b) say little about your ultimate goal, which is moving demographic strata from Persuadable to “In our camp”. In 2020, our offering of audiences will include segmentation updates (see above), offering the best metric of externally valid RoI to date.

Our segmentations are built on our massive data tracking all Americans on 250+ political/economic/psychometric dimensions. The data is built on the basis of 300,000+ survey respondents and more than 30 Million behavioral data points over the last year, with close to 100 Million data points in total. Models derived from this survey data – relying on state-of-the-art PredictWise machine learning algorithms – are projected onto a specially curated target universe. Segmentations are created from there via API calls, within minutes.