Supernova Award Category
Data to Decisions
The Problem
A client approached Cint with the goal of accessing respondents with specific browser settings, that were willing to participate in a cookie dropping exercise. They needed to identify, establish and engage pools of specifically profiled custom audiences. To achieve this, Cint initially needed to profile a specific audience, while creating an accessible, pre-profiled “sample pool” of respondents. Cint implemented the Cint Optimizer as an integrated router solution, powered by a profiling engine to capture reusable1st person data in a pre-screening environment.
The Solution
To ensure timely completion of the client’s project, Cint had to develop a strategy that would keep their panelists engaged. The solution was personalization. Cint leveraged their people-based marketing strategy using 1st person data to regularly provide surveys, targeting the right respondents and increasing completion rates. Cint executed this approach by setting up the specific profiling question in Cint’s self-service sampling platform, Access. Using “gen pop” samples, the client was able to order projects using the profiling question. Cint Optimizer automatically presented the profiling question as a pre-screening filter to a maximized set of respondents. Then, Cint Optimizer automatically updated the profiling point to the panelist record; generating a “sample pool” that evolved in tandem with the research executed. The client was able to use this data to complete their research project while retaining access to data for future endeavors.
The results
Cint successfully implemented the Cint Optimizer solution to capitalize on existing data and improve survey sampling through connecting surveys to the right panelist, at the right time. Cint had to develop a strategy to keep panelists engaged, through personalization. Cint leveraged their people-based marketing strategy using 1st person data to regularly provide surveys, targeting the right respondents, which drove increased completion rates. Cint executed this approach by setting up the specific profiling question in Cint’s self-service sampling platform, Access. Using “gen pop” samples, the client was able to order projects using the profiling question. Cint Optimizer automatically presented the profiling question as a pre-screening filter to a maximized set of respondents. Then, Cint Optimizer automatically updated the profiling point to the panelist record; generating a “sample pool” that evolved in tandem with the research executed. The client was able to use this data to complete their research project while retaining access to data from the “sample pool” for future endeavors.
Metrics
Cint successfully raised its maximum feasibility by over 200 completes per day during the first week alone. The client was able to quickly establish and grow a “sample pool” for this custom audience. There was a 72% increase of the profiling question penetration. As the client continued to execute more projects, fewer and fewer “gen pop” samples were needed to create the right audience. Eventually, The client achieved a 100% IR (Incidence Rate) while running the project. This resulted in a 27% drop in price (vs. the “gen pop” IR samples) and a 50% reduction in field time. Cint Optimizer increases profiling question penetration, utilizes contextual1st person data to build custom audiences and accelerates project completion, maximizing ROI. With Cint Optimizer, the client enhanced consumer engagement, loyalty and spend.
The Technology
Cint Optimizer
Disruptive Factor
Cint’s overall platform challenges the status quo of how market research sample is sourced, aggregated and distributed to samples (Targeting and fielding) via a SaaS, whereas the rest of the industry is managed service (send an email and wait for a human to write back a few hours later.) Cint Optimizer further capitalizes on our play, as it also allows us to capture 1st person data during the market research fielding process.
Shining Moment
The build of Cint Optimizer allowed Cint to move beyond serving the market research vertical exclusively, expanding to both the AdTech and MarTech verticals with the 1st person data aggregated. This 1st person data is particularly valuable to publishers as it improves customer experience through direct ad targeting based off interests, leading to better click through rates and increasing revenues from ad placement.