SECTOR DE LA CONSTRUCCIÓN EN EL ECUADOR

We investigate whether the optimization algorithm implemented by the ad platform is bidding in the interest of the firm. First, we assess whether ad platforms target consumers with higher purchase probabilities in line with the incentive structure present in contracts between ad platforms and firms. Generally, our analysis requires variation in the endogenous variable for average bids for ad impressions placed by the ad platform on behalf of the firm. The histogram of the average bid for an ad impression for a consumer displays the variation in the bid variable (see Figure 4.1). A coefficient of variation (ratio of standard deviation to mean) larger than 1 (CV= 1.470) of the bid variable further points to variation in the height of bids placed by the ad platform.

Next, we investigate the average treatment effect of the ads in our experiment. Lastly, we test whether higher bids are placed for ads that have a higher impact on consumers’ purchase probabilities.

To do that, we estimate a logit model that investigates whether consumers for whom the optimization algorithm does on average bid higher are more receptive to the ad treatment.

Purchase ProbabilityRetargeting_j = exp(U

Retargeting

j )

exp(U_jRetargeting) + 1

U_jRetargeting=β0+β1bidj+β2ad treatmentj+β3bidj×ad treatmentj+j

wherebidj gives the average bid for a consumerj over the duration of the experiment

Figure 4.1: Histogram Average Bid 0 50 100 150 Density 0 .02 .04 .06 .08 .1 average bid

addressed with retargeting ads (ad treatmentj = 1) or PSA ads (ad treatmentj = 0),

andj represents the idiosyncratic error term.

The coefficient of the interaction between bidj andad treatmentj represents the

focal aspect in this analysis. In case the ad platform does optimize the bidding in the interest of the firm, we would expect a positive and significant coefficient for the interaction term ofbidj and ad treatmentj.

Table 4.3 presents the results for our analysis. We find that, in line with the incentive structure in contracts between ad platforms and firms, ad platforms target consumers that are more likely to purchase by bidding higher for their ad impressions (βbid= 14.852, p < .001). In Appendix A4.1 we present evidence that shows that the

ad platform is using consumer characteristics to identify high purchase probability consumers. Overall, the ad treatment does significantly increase consumers’ purchase probabilities (βad treatment=.174, p=.032) pointing towards the presence of a return

on advertising spending. Nevertheless, we do not find evidence for a significant effect of the interaction between bids placed by the ad platform and the ad treatment

(βad treatment×bid=.531, p=.880). This means that while the optimization algorithm

is bidding higher for consumers that are more likely to purchase, it fails to identify and bid higher for consumers that are more receptive towards ads. The non-significant interaction term ofad treatmentjandbidjalso points towards no significant correlation

between consumers’ inherent purchase probabilities and their receptiveness towards ads rendering the bidding strategy adopted by the ad platform sub-optimal for the firm.

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Table 4.3: Logit Regressions for Purchase Probability

(1) (2) (3)

VARIABLES purchase Logit purchase Logit purchase Logit

bid 14.852∗∗∗ 14.895∗∗∗ 14.466∗∗∗ (1.377) (1.378) (3.158) ad treatment 0.174∗∗ 0.165∗ (0.081) (0.098) ad treatment×bid 0.531 (3.510) Constant −3.071∗∗∗ −3.213∗∗∗ −3.206∗∗∗ (0.038) (0.077) (0.089) Observations 20,918 20,918 20,918

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

We run additional robustness checks in which we analyze the relationship between the maximum bid, the median bid, and the cumulative bid placed for an ad impression for consumerj and their impact on consumers’ purchase probability (see Appendix A4.3). When operationalizing the ad platform’s targeting behavior, visible in the ad platform’s bidding for ad impressions, differently, we find consistent results.

To strengthen the argument that there is no significant correlation between con- sumers’ inherent purchase probabilities and the increase in purchase probabilities caused by ads we build a predictive model estimating consumers’ purchase probabilities prior to the ad treatment (see Appendix A4.4). The purpose of this analysis is to investigate the relationship between absolute purchase probabilities and the increase in purchase probabilities caused by ads more directly, instead of looking at the bid placed by ad platforms and the increase in purchase probabilities caused by ads. We use these predicted purchase probabilities to test whether consumers with a higher predicted purchase probabilities react more positively to our ad treatment. Consistent with our claim, we find that consumers with higher predicted purchase probabilities do not react more positively to the ad treatment.

To further assess the robustness of our findings, we investigate the possibility that we do not find a significant interaction effect in our analysis because our experiment has too low power. We assess the functional form of the relationship between bids placed by the ad platform and the increase in consumers’ purchase probabilities. We plot the average increase in purchase probabilities caused by ads per consumer and

bid decile (see Figure 4.2). In case the ad platform is optimizing the bidding in the interest of the firm, we would expect a monotonically increasing trend in the average increase in purchase probability caused by ads with an increase in the bid decile. This graph points towards the absence of a relationship between the bidding conducted by the ad platform and the increase in consumers’ purchase probabilities.

Figure 4.2: Average Purchase Probability Increase per Consumer and Bid Decile

−.05

−.025

0

.025

.05

Increase in purchase probability

1 2 3 4 5 6 7 8 9 10

Bid decile

Notably, firms do not pay their actual winning bid to serve an ad impression but are being charged the second highest bid in the ad auctions. Our results remain consistent when analyzing the impact of both the average cost per impression and the overall cost for impressions served to a consumer over the duration of the experiment (see Appendix A4.5). Firms are paying more for ad impressions that do not increase consumers’ purchase probabilities more significantly. This means that firms pay more for ads that do not deliver significantly higher value to them.

One explanation that would justify the ad platform’s bidding behavior is that the ad platform’s optimization algorithm incorporates consumers’ profit contributions (πj)

into its optimization. In case the profit contribution is negatively correlated with the increase in consumers’ purchase probabilities:

corr(πj,∆P(πj >0))<0

there might be a valid reason for the algorithm to not bid higher for more receptive consumers but instead target consumers with higher profit potential.

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