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.

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Media and ad tech conferences have been dominated by discussions about AI and cookie deprecation over the past couple of years. These are important topics, but one equally important topic gets less attention: data strategy.

Everyone wants the mystical data lake that will solve all their data needs and finally make them “data-driven,” the thing everyone claims to be but few actually are.

A data lake can be a great thing but, not unlike a normal lake, it can also be filled with toxic waste and be more like a dump than a beautiful lake anyone wants to touch. Just putting your data in a data lake doesn’t actually fix anything. A data lake is just a fancy marketing term for a database. 

The key to enabling your organization to make data-driven decisions is to make the data accessible to the whole organization and different stakeholders, including those who don’t have a computer science or data science background. 

For example, your ops team may want to know the latency of ad loading or be able to see how many impressions an ad unit generated for a certain audience. They shouldn’t need to know SQL to achieve that.

A SQL prompt (even though it is powerful and one of my favorite tools) won’t help, and a static dashboard won’t help either because you can only think of and design so many things ahead of time. You need something else.

3 Steps to Unlock Data for the Entire Publisher Organization

So, how do you make your data truly accessible — and understandable — to every relevant person within your organization?

1. Ensure you have a solid ETL pipe. You want all the data in one place, but more importantly, you want it normalized across your sources so you are actually comparing apples to apples when reporting. A business user shouldn’t need to know how Magnite or Index Exchange defines their ad types. Their tools should account for these differences.
2. Make the data accessible. Enable the data to be queried with easy-to-use tools that take care of the logic in the background. People are strapped for time, and if it is a hassle to get to the data — maybe they have to submit a ticket to the data science team and wait two weeks to hear back — they are probably not going to do it.
3. Monitor the data for accuracy. One thing that will definitely kill a data strategy is inaccurate or out-of-date data. If users can’t trust the data, they will not use it, instead retreating to manual Excel spreadsheets or other less effective methods.

A data lake won’t make publishers data-driven. But getting all their data in one place is indeed the first step to more efficient, data-driven decisions.

Normalizing the data, making it easy to query, and shoring up its accuracy will help publishers get the rest of the way so that “data-driven” is a real way of doing business and not just a nice-sounding slogan.

The post Data Lakes Won’t Make Publishers Data Driven. Here’s What Will appeared first on AdMonsters.

The key is understanding all the data surrounding ad buys to ensure publishers know exactly what they’re selling and receiving in return, reconcile discrepancies between their books and those of advertisers, and identify areas that need improvement.

But let’s get more specific. Here are five things publishers should be doing to shore up their finances.

5 Financial Best Practices for Publishers

Reconcile Discrepancies

Publishers get billed on third-party ad server numbers, so they should monitor and reconcile discrepancies between their first-party ad server and third parties. Then, they need to send the finalized numbers to their finance team for billing, often via an order management system.

Publishers should be able to automatically reconcile these discrepancies so that they can more quickly identify campaigns not meeting their delivery goals and resolve issues. Automated reconciliation also avoids disconnects between advertisers and publishers, as well as between ad ops and billing, saving everyone time and improving both relationships.

Automate the End-of-month Process

By automating the EOM process, publishers can send invoices to their customers weeks earlier, leading to earlier payments. For example, by automating the usually manual process of consolidating third-party data with first-party data, publishers might be able to push up invoicing from the middle of the month to the first few days. They can also streamline and verify the transmission of financial data from ad operations to the finance team. 

Another manual task that publishers can automate to speed up EOM is making adjustments for over-delivery. For example, if you delivered 50,000 more impressions than you were supposed to, you’d be able to automatically take that out of the invoice. 

Finally, they can automate assembling different metrics (such as impressions, viewable impressions, or co-view impressions) for different campaigns based on OMS information. 

Get Good at Forecasting

Publishers who forecast their direct-sold inventory with high accuracy maximize their yield — which is particularly true if their business is susceptible to seasonality or external events. For example, it’s really difficult to know how to optimally sell inventory based on Black Friday traffic if you don’t have a model capable of anticipating an unusual spike in inventory availability.

Besides driving higher sellthrough rates and revenue, accurate forecasting also provides publishers with higher confidence in their overall financial planning. Use industry benchmarks and seasonality to try and forecast programmatic yield as well.

Predictable revenue is critical to making virtually all other business decisions. 

Automate Tasks Where Possible

Publisher teams, especially ad and revenue operations, are generally mired in a lot of rote work, such as data aggregation, reconciliation, and reporting, that can and should be automated. By automating as many tasks as possible, publishers will keep their teams happy (because they won’t be saddled with busy work), allocate talent optimally, and increase their bandwidth to perform more revenue-driving tasks, like taking care of clients.

Brush up on Revenue Reporting

Periodically, review which aspects of publisher data are included in executive reporting. This allows rev ops teams to align management on the KPIs they should prioritize. For example, it might be pertinent for management to know which sites perform relative to others. Or there may be shifts in display and video revenue. Ad ops people should regularly review which facts they’re putting on the executive team’s radar to facilitate strategic adjustments.

Reconciling discrepancies, automating EOM, mastering forecasting, automating tasks where possible, and updating executive revenue reporting will help publishers solidify their finances, identify prime growth opportunities, and ultimately instill greater confidence in their decision-making. That’s what financial literacy is all about.

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We’re living in the era of the AI gold rush in advertising. Every tech company will claim to be using AI in its solutions. Every publisher and advertiser will be curious about AI — but also wary of snake oil salesmen in an industry infamous for a lack of transparency.

Here are five ways publishers can actually use AI to drive user engagement and traffic while simplifying operations.

5 Ways Publishers Can Actually Use AI

1. Chatbots to Drive Engagement and Referral Traffic

OpenAI generated massive attention and user adoption via a large language model that could chat intelligently and extensively with users about queries on various topics. Publishers will be able to take advantage of similar solutions, building chatbots on their sites that surface interesting content for readers and answer questions that may arise.

Off their sites, publishers are developing chatbots for AI-first platforms like OpenAI’s GPT store, for example. These chatbots will be experts on their publisher’s content and can therefore chat with users about specific topics. For example, a ChatGPT user curious about sports could speak with an MLB chatbot.

2. Generative Ads to Keep up With Generative Content

The death of third-party cookies has gotten the advertising community excited about contextual advertising again. Ads will be more effective, the thinking goes, if they are relevant to the content audiences are consuming. But how do advertisers optimize contextual ads for content that is itself dynamically generated?

Most ad tech isn’t built for generative ads, but the need is inspiring new startups such as OpenAds, which is building an ad engine for precisely this use case. For example, if I ask the generative AI chatbot what the chances are that Rafael Nadal will win the 2024 French Open, it delivers an ad for a sports betting site.

3. Ad Ops Gpts to Automate Fact-Finding

In the same way consumers are widely using LLMs like ChatGPT to satisfy their curiosities, ad ops professionals who currently spend many hours per week sifting through data will increasingly be able to use AI-powered chatbots as assistants to save time. A simple example is asking a bot, “How much revenue did we generate last quarter?” Eventually, bots might be able to satisfy a deeper query: “How much did open market programmatic CPMs change from Q2 to Q3, and how does that compare to available benchmarks for the rest of the industry?”

AI will take over fact-finding and pattern recognition, freeing up ops professionals to focus on better serving clients and identifying growth opportunities.

4. Entity Resolution to Eliminate Discrepancies

One of the biggest challenges for publishers is eliminating discrepancies among their systems: ad servers, SSPs, the OMS, CRMs, business intelligence tools, and the dreaded Excel spreadsheet where they may be manually recording data. With AI, publishers can automate entity resolution by unifying data records that refer to the same entity but contain minor discrepancies.

This might not seem like a big deal. But for any ad ops professional who’s spent half a Monday tracking down a single record across multiple programmatic advertising systems, it’s a game changer that will free up time and make their workday more enjoyable.

5. Forecasting to Predict Even Anomalous Traffic

Publishers forecast traffic so that they can optimize yield and sell the optimal number of ads to the right advertisers. But this is especially hard for publishers that experience traffic irregularities. For example, sports publishers don’t know weeks ahead of time which games will turn into nail-biters or record-breakers and how they’ll affect traffic. Or ecommerce sites might struggle to capture just how big their Black Friday traffic spike will be.

With AI and machine learning, publishers can predict patterns with much greater accuracy and account for irregularities. This means fewer frantic emails from advertisers wondering why their ads haven’t run — and fewer disappointed CROs who were expecting a level of revenue that didn’t come to fruition based on available yield.

AI Isn’t All Hype for Publishers

There will be plenty of AI charlatans, but there are also simple use cases where generative content and superior pattern recognition will enable publishers to save time and money while delivering a better experience to their users.

We don’t have to call it revolutionary. But it will save ad ops professionals time and make publishers more money. And in the publishing world, that is the bar for advertising technology.

The post 5 Ways Publishers Can Actually Use AI appeared first on AdMonsters.

Back then she was a senior manager working in Digital Ad Sales and Finance Business Intelligence at Viacom and was responsible for providing the company with data regarding its digital ad sales revenue. 

Her team was tasked with creating ongoing management, sales, and financial reports for the ad ops team, as well as monthly and quarterly reports that rolled up to the CFO and were delivered as part of the quarterly earnings. 

“On a monthly and quarterly basis, I had to pull and consolidate data from so many sources and my spreadsheets were really large with a lot of tabs,” explained Naveh. “It took me more than 40 hours, which is well over a week. I spent probably closer to two weeks wrangling data, doing vlookups, and ensuring the data is in the same format.”

The Race to Report

All of the reports required accurate revenue and impressions data across the multiple platforms that served ads to Viacom viewers. For instance, to gather display data, the team pulled data from the Google Ad Manager instances for BET, CBS, and the rest of Viacom’s properties. The team also pulled video data from Freewheel, as well as data from myriad SSPs and ad networks with whom Viacom worked, such as Rubicon, SpotX, and OpenX.

All of the manual data pulls and subsequent formatting and uploading to spreadsheets left Naveh, who majored in Business & Corporate Finance, with little time to do the things she went to school for, specifically analyzing the data and gleaning stories from its meaning.

“I spent so much time just getting the data that I didn’t even have much chance to think about it or ask questions. Obviously, I want to know why things happen, and what they mean for the business. But by the time the numbers were ready, it was like time to deliver them up, that was it.”  

With the data due before she had any time to think about it, Naveh’s team never uncovered the core drivers behind the trends reported (e.g. “Why did this business behave this way this quarter?”).

More People Not the Answer

Would having a larger team have given Naveh the time she needed to analyze the data and tease out the stories she knew it contained? 

“Not really,” she said. “Working with spreadsheets is a one-person job. Perhaps we could have hired an analyst to pull the needed files from the different sources and then another analyst to aggregate into a spreadsheet, but version control quickly becomes a nightmare. Which spreadsheet is the most accurate? What is our shared version of truth?”

Introduction to Burt Intelligence

In August 2019, CBS and Viacom announced a merger, and Naveh was trained on CBS processes. It was then that she discovered Burt Intelligence, a platform that harmonizes disparate advertising data, and applies machine learning to spot anomalies and emerging trends. 

“I loved the user interface, and the ability to drag and drop new data into Burt, which was one of the biggest challenges we faced at Viacom,” Naveh said. What once took 20 hours now takes less than 30 minutes.

The visualizations she saw were also game-changing. Naveh had been using Power BI for a number of years but found the tool difficult to use. In fact, her team frequently hired consultants to work on visualizations projects for them.  With Burt Intelligence, she saw the possibility of wholly democratized data science. Any team member can build a dashboard, visualize trends and get to the core drivers behind critical trends.

“When I was at Viacom, I saw a shift in revenue, from mostly display to 50% display, and eventually to mostly video. This is the kind of trend that would have been great to visualize. The same is true for the platforms our viewers use to consume content. Our audiences went from desktop to mobile to CTV, and being able to spot and validate those trends early on would have helped us make better decisions about the channels to invest in.”

A New Job; New Insights

Eventually, Einat Naveh left Viacom to join the Burt Intelligence product team, where she primarily focuses on Burt Advisor, a curated, daily email that prioritizes how ad ops professionals should make decisions to preserve as much revenue as possible and deliver the best results for brands. 

She also helps clients build end-of-campaign wrap-up reports, but her goal is to empower the people who are in charge of a campaign to do all of the reporting themselves. 

“This is an important step for the industry because it will mean that we have achieved a true democratization of data science,” Naveh explained. “If you work in this business, you have a natural curiosity and an impulse to answer questions about your campaigns. That requires easy and seamless access to data.”

Democratization of data requires pre-built templates and dashboards that serve as a starting point for analysis. The templates are based on a range of campaign criteria and goals, such as click-through rate, viewability, or video completion rate. The templates automatically pull information from the sources, whether it’s GAM or OpenX, and populate the fields with real-time data. 

AI is then deployed to spot anomalies in the data, such as discrepancies in impressions served, that are difficult to spot due to the enormity of campaign datasets. Machine learning helps campaign managers assess which aspects of a campaign are performing better than anticipated, and which are delivering disappointing results so that they can redeploy media spend to optimize overall performance.

“AI is great for first finding things that don’t make sense, and then helping us get to the root of the issue.” Error prevention is a top concern for Naveh. 

But AI isn’t just about problem-solving. It’s also a critical tool for understanding an audience, and how its consumption patterns shift from day to day. This is the kind of insight that can help an advertiser drive efficiency in their programmatic ad spend. 

If a client’s top priority is viewability, is the media plan acquiring the desired number of impressions that are in view? Or is it time to swap out some publishers in order to meet the campaign’s criteria? Without AI, the right answers may not come until long after the campaign has ended.

But AI isn’t just about problem-solving. It’s also a critical tool for understanding an audience, and how its consumption patterns shift from day to day. This is the kind of insight that can help an advertiser drive efficiency in their programmatic ad spend. 

“When trends are displayed daily on a dashboard, the campaign manager can pivot in time to materially affect a campaign’s performance,” Naveh explained.

Put another way, AI allows campaign managers to focus less on what happened, and more on the leading indicators that predict what will happen as a campaign unfolds. In the future, Naveh would like to see tools like Burt Advisor do more forecasting of trends that affect individual campaigns. 

She envisions a scenario where AI helps a campaign manager predict where an audience is going, how the impression mix will change, and how best to spend a budget as the campaign progresses.

Tools that harmonize campaign data and apply machine learning to it are very much in demand. The digital advertising industry has always been data-heavy and has seen a plethora of point solutions and platforms that generate it in abundance. But data is only as good as the analytics that we can apply to it.

The next challenge is to find ways to harness that data so that regular people can make sense of it, and use it to make smart decisions ASAP. This will be the next frontier of decision intelligence.

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