The most common question marketers ask now is - “Did my ad campaign cause the user to convert and generate more revenue for my brand or would that have happened anyway?”.Also, targeting randomly generated customers make them suffer from huge costs and weak response. The complexity of the ad-tech ecosystem is constantly growing with brands running marketing activities across multiple channels, new targeting capabilities, and formats. Due to this, traditional digital measurement metrics like cost per click, return on investment, cost per conversion, etc. just scratch the surface while measuring the impact of marketing strategies. This measurement gap leads us to look at the incremental lift as a metric to measure the impact of a marketing strategy. Incrementality testing is a mathematical approach to differentiate between correlation and causation. We formulated different approaches to calculate incremental lift that can be implemented in the digital marketing ecosystem. Viewability is one of the methodologies that we are using for calculating incrementality in which we are measuring the effectiveness of an ad by comparing the users who are exposed to an ad versus users that are not exposed to an ad. Our methodologies cover concepts of test environment setup, randomization, bias handling, hypothesis testing, primary output and understanding different ways of using this output. We used this output for strategy planning and optimizations, helping us in achieving higher campaign efficiency. Having a set of different approaches to calculate incrementality gives us the flexibility to cater to a wide range of test cases having different setup challenges and restrictions.
HALL 2: KNOWLEDGE TALK/ CASE PRESENTATION