Trader Optimization Reports
Using Log level data to optimize an ad campaign is common by advertisers & ad agencies. Advertisers use the log-level data to check how their users are behaving around an ad campaign and also assess the effectiveness of the ad campaign on their users. This data is used to understand the audience who are more likely to convert based on their characteristics & separate them out from the users who are not likely to convert. But Log Level data alone does not give the “Big Picture” into user buying pattern, preference and interaction with the advertiser website.
What is Log Level Data?
Log level data is the impression data on an ad that is collected by the DSP and mostly includes information of a user such as user geo, browser, os, device, domains visited by a user, inventory details, creative details, price quotes, etc.
At Miq, the granular details from DSP log-level data are used to understand the performance & characteristics such as geolocation, zip code, location, browser, OS and device of our target audience for a specific ad campaign. Also, this data is used to understand the performance of various creatives & publisher domains that gives back high CVR compared to others.
The log level data is combined with our proprietary advertiser data to trace back the return on the media cost paid for each impression which is then aggregated on publisher domain, zip codes & geo regions to understand the performance of each of these attributes. These aggregated reports are used to carry out mid-campaign & post-campaign analysis.
What is advertiser proprietary data?
Advertiser proprietary data of Miq is collected through pixels placed on the advertiser's website which sends us user impression-level details, pages they are browsing, keywords they are searching in the advertiser website, products they are looking for, purchase they made, the amount they spent, etc. Basically the pixels capture all the granular details of user interaction in the advertiser website.
In mid-campaign analysis, data-driven decisions are taken to create retargeting audience segments and optimize campaigns based on top-performing parameters & in post-campaign analysis, the performance & efficiency of these parameters are evaluated.
The advertiser level proprietary data, when combined with log-level data, gives a detailed view of the user navigation patterns, preference, purchase frequency, which help us dig deep into advertiser level KPI.
- For a travel advertiser, what are the most common routes the converted users are looking for? What is the spend and return for such an audience?
- What are the top products purchased by converters for a retail advertiser? What is the ROI for such an audience?
- What is the ideal type of hotel room (budget/ premium) preferred by my audience?
These questions when answered opens up new scope for data-driven & insight based targeting campaigns in which specific campaigns are set up for a specific audience which results in better user engagement, performance and ROI.
Co-Author: Sahil Khan