A Latent Instrumental Variables Approach to Modeling Keyword Conversion in Paid Search Advertising

The authors present a modeling approach to assess the purchase conversion performance of individual keywords in paid search advertising. The model facilitates estimation of daily keyword conversion and click-through rates in a sparse data environment while accounting for the endogenous position of the text advertisement served in response to a search. Position endogeneity in paid search data can arise from both omitted variables and measurement error. The authors propose a latent instrumental variable approach to address this problem. They estimate their model on keyword-level paid search data containing daily information on impressions, clicks, and reservations for a major lodging chain. They find that higher positions increase both the click-through and conversion rates. When advertisements are served in higher positions, approximately one-third of new conversions is due to increased click-through while approximately two-thirds are due to increased conversion rates. The authors show that the keyword list generated on the basis of their estimated conversion rates outperforms the status quo list as well as lists generated by observed conversion and click-through rates.