Unlock the full potential of Google Ad Manager’s log-level data with these five actionable analyses. Learn how to optimize your ad strategies and increase revenue using Data Transfer Files.
Google Ad Manager’s Data Transfer Files (DTF) aren’t a new offering; many tech-savvy publishers already use them.
However, in many conversations with publisher adops professionals, they confide in me that while they want to utilize the data treasure stored in the DTF, they’re just not exactly sure what to do with it. Many industry publications and conference keynote speakers praise the value of log-level data, but few explain exactly what you should and could do with it.
So, as a quick guide for the perplexed, I have gathered five ways I think you should be working with your DTFs:
5 Analyses You Can Perform with Google Ad Manager Log Level Data
1. Segment Analysis
With the log-level granularity, you can see how targeting parameter combinations perform better than the API or UI-based reporting can offer. For example, you can answer questions such as:
- How much does a certain segment increase my CPM? Compare it by itself vs in combination with other segments; maybe it only increases the CPM when combined with another third-party ID for example, and thus, it is the other third-party ID that is providing the lift, but you’re paying a fee for both
- Do certain segments only work on certain parts of your inventory? Or does it give a boost to all of it?
- What other targeting parameters do segments work well with? Or, is it not necessary with certain parameters?
2. Key-Value Pairs Analysis
In the DTF, all the key values you have set up on your site are available for you in a deduplicated manner so you don’t get overlaps between combinations giving you flexibility to combine them freely and see how different combinations perform. This allows you to investigate combinations of key-key-values to figure out:
- Which combination of positions and custom parameters leads to a higher CPM?
- What targeting combinations are prebid vendors bidding on?
3. Latency Checks
Given the granularity of the data, you can measure the latency of your bid process to ensure you aren’t leaving money on the table and creating a bad user experience. For example, this could allow you to test latency when adding new bidders or turning on Google’s Protected Audience API.
4. Incremental Revenue Analysis
Compare your winning bids with other bids to determine potential efficiencies in your ad stack. Do you have a slew of bidders bidding within $0.01 on most auctions? Do all your vendors bid on the same auctions, and none on others? Well, all of these might be signs you should look over your ad tech stack and make it leaner.
5. Loss Reason Analysis
In the GAM UI/API reporting, you can get some basic metrics for loss reasons. However, to understand what really happened, you need to dig deeper and see all the targeting and other parameters that were set on the request. The only way to do this – is by digging into the log data.
Data Done Right: Navigating Log-Level Analysis with Ease
So now that we’ve established how powerful and useful it is to use the logs, how do you actually do it?
You need to have a data solution that can manage billions of rows of data each month. In addition to storing all your log data in one place, it is also recommended to aggregate subsets that you can query quickly for things you do a lot. For example, do you often have to query certain key values with e.g. the order dimension? Great. Pre-save that to ensure that the query runs quickly and you don’t spend hours waiting for results and waste money processing the same data over and over.
Given the technical nature of the log files and the complexity due to the size and lack of organization of the files, if your data team does not have specific expertise, it is recommended to partner with someone who has experience with DTFs and knows how to manage them.
Harnessing Data for Smarter Ad Operations
Google Ad Manager’s log-level data offers a treasure trove of information that can significantly enhance your ad strategies. By performing analyses, such as those suggested above, you can gain a comprehensive understanding of your ad inventory and your sites’ performance.
This enables you to make data-driven decisions, optimize your ad stack, and ultimately achieve more revenue. Embrace the power of log-level data and transform your ad management approach into a finely tuned, high-performing operation.