Firms use different attribution strategies such as last-click or first-click attribution to assign conversion credits to search keywords that appear in their consumers’ paths to purchase. These attributed credits impact a firm’s future bidding and budget allocations among keywords and, in turn, determine the overall return-on-investment of search campaigns. In this paper, we model the relationship among the advertiser’s bidding decision for keywords, the search engine’s ranking decision for these keywords, and the consumer’s click-through rate and conversion rate on each keyword, and analyze the impact of the attribution strategy on the overall return-on-investment of paid search advertising. We estimate our simultaneous equations model using a six-month panel data of several hundred keywords from an online jewelry retailer. The data comprises a quasi-experiment as the firm changed attribution strategy from last-click to first-click attribution halfway through the data window. Our results show that returns for keyword investments vary significantly under the different attribution strategies. For the focal firm, first-click attribution leads to lower revenue returns and a more pronounced decrease for more specific keywords. Our policy simulation exercise shows how the firm can increase its overall returns by better attributing the real contribution of keywords. We discuss how an appropriate attribution strategy can help firms to better target customers and lower acquisition costs in the context of paid search advertising. Data, as supplemental material, are available at https://doi.org/10.1287/mksc.2016.0987 .
Free trial promotions are a commonly used customer acquisition strategy in the Software as a Service industry. We use data from a large-scale field experiment to study the effect of trial length on customer-level outcomes. We find that, on average, shorter trial lengths (surprisingly) maximize customer acquisition, retention, and profitability. Next, we examine the mechanism through which trial length affects conversions and rule out the demand cannibalization theory, find support for the consumer learning hypothesis, and show that long stretches of inactivity at the end of the trial are associated with lower conversions. We then develop a personalized targeting policy that allocates the optimal treatment to each user based on individual-level predictions of the outcome of interest (e.g., subscriptions) using a lasso model. We evaluate this policy using the inverse propensity score reward estimator and show that it leads to 6.8% improvement in subscription compared with a uniform 30-days for-all policy. It also performs well on long-term customer retention and revenues in our setting. Further analysis of this policy suggests that skilled and experienced users are more likely to benefit from longer trials, whereas beginners are more responsive to shorter trials. Finally, we show that personalized policies do not always outperform uniform policies, and we should be careful when designing and evaluating personalized policies. In our setting, personalized policies based on other methods (e.g., causal forests, random forests) perform worse than a simple uniform policy that assigns a short trial length to all users. This paper was accepted by Duncan Simester, marketing.
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