After implementing automated bidding, the number of new patients visiting the clinic increased by approximately 20%.
When the initiative was launched in July, there was a seasonal trend for the number of new patients to decline every year, but taking this into account, it can be seen that this initiative had a positive impact on the number of new patients visiting the clinic.
6. Insights gained from cases
In this case, we believe there are two main factors that make the measures seem to have worked well.
Reason 1: Restructuring the account structure to make phone number list it easier for automated bidding to work
First of all, I think one of the reasons why the measure worked was that we restructured the account structure to make it easier for automatic bidding to work .
As mentioned above, when using automated bidding, the theory behind programmatic advertising is that a certain amount of learning data should be guaranteed in order to optimize machine learning.
In this client's account, each store had previously been running its own campaign, and the conversion data for each campaign alone tended not to meet the optimization guidelines for machine learning. Therefore, in order to create an account structure that would make it easier for machine learning to proceed, we first consolidated the stores in the Tokyo metropolitan area that were the subject of the measures' verification by region, and created and ran new campaigns.
We also believe that switching from manual to automatic bidding enabled us to deliver ads more efficiently to users who were likely to become new patients, which also contributed to our success.
Reason 2: There was little difference in the attributes of Web conversion users and offline conversion users.
This time, we introduced automatic bidding without using offline CVs as learning material, based on the hypothesis that there would be no significant differences in the demographic attributes or online user behavior of users who visit the store directly without making an appointment online (offline CV users) and users who make inquiries or reservations online before visiting the store (Web CV users).
As a result, even without using offline CVs as learning material, we used information from Web CV users as learning material and delivered ads to similar users, which led to an increase in the number of new visits, so we believe that our predictions were correct to a certain extent.
In addition, as a future outlook, as the distribution budget is beginning to become biased by prefecture due to the consolidation of campaigns by prefecture, we plan to review the campaign structure and implement measures such as uploading offline CVs so that we can control costs in line with the number of clinic visitors (offline CVs) .
