Shiv Kumar Saini

Estimating the incremental effects of interactions for marketing attribution

Assigning credit to different marketing activities has long been an important but challenging goal for a marketer. With the advent of digital marketing, the marketer can now potentially record each interaction with a prospective customer. With this development it is possible to measure and assign credit for each marketing interaction. We propose an econometric model to estimate the true incremental number of purchases that can be attributed to a given marketing channel. We extend our model to attribute credit for revenue realization. We also propose an approach to automatically identify audience segments where attribution models differ. We build and test our model on a real world data set belonging to a travel and experience industry organizations web data. The results show that our approach improves upon industry standard rule-based approaches, by correcting for the biases inherent to these model.

A Non-parametric Approach to the Multi-channel Attribution Problem

Multi-channel marketing attribution modeling is a two-stage process. First, the value of exposure from different marketing channel needs to be estimated. Next, the total surplus achieved needs to be assigned to individual marketing channels by using the exposure effects from the first stage. There has been limited work in exploring possible choices and effects of determining the value of exposure to different marketing channels in the first stage. This paper proposes novel non-parametric and semi-parametric approaches to estimate the value function and compares it with other natural choices. We build a simulation engine that captures important behavioral phenomenon known to affect a customers purchase decision; and compare the performance of five attribution approaches in their ability to closely approximate the known ground truth. Our proposed method works well when marketing channels have high levels of synergy. We apply the proposed approaches on two real-world datasets and present the results.

Probabilistic Deduplication of Anonymous Web Traffic

Cookies and log in-based authentication often provide incomplete data for stitching website visitors across multiple sources, necessitating probabilistic deduplication. We address this challenge by formulating the problem as a binary classification task for pairs of anonymous visitors. We compute visitor proximity vectors by converting categorical variables like IP addresses, product search keywords and URLs with very high cardinalities to continuous numeric variables using the Jaccard coefficient for each attribute. Our method achieves about 90% AUC and F-scores in identifying whether two cookies map to the same visitor, while providing insights on the relative importance of available features in Web analytics towards the deduplication process.