At the absolute core of optimized reporting is centralized data. Connecting all your data sources permits comprehensive visibility across your operations, allowing for exponentially more accurate analysis, planning, forecasting, and adjustments.
Let’s take ad server data as an example. This common component of the ad delivery chain includes the publisher ad server and agency ad server. To get the most from your reporting, you need to tie all your ad servers together to see what’s going on. While ad operations teams have experimented with many processes to establish successful methods of connecting publisher and agency ad server data, it’s an extremely complicated process and virtually impossible to do manually.
The key to connecting ad server data is understanding that the link between ad servers must originate from the publisher ad server. Trying to connect ad server data from the agency side is especially cumbersome, inaccurate, and has a low success rate. One way of facilitating the linking of publisher and agency ad server data is manipulating naming conventions from the publisher ad server. While this traditional method is effective, it’s also very time intensive and requires a well-defined process. What’s better than traditional naming manipulation is custom field creation, which is easier to establish a process for. In both scenarios, the most important element to consider is the amount of hours spent inserting naming conventions in order to execute mappings. Plus, the error rates are also very high as names can be unpredictable.
Advanced Tag Mapping
Leveraging tag data has proven to be exponentially more accurate than both traditional naming
and custom field creation. Tag data can be used in concert with other mapping strategies that can be derived from names in your line items or placements. But advanced tag mapping has proven to be the most effective way to see what occurs throughout the delivery chain.
Comprehensive tag mapping provides a reliable way to connect ad server data (this includes logins and reports), publisher ad server data (for example, DoubleClick for Publishers [DFP], FreeWheel, OpenX, and AppNexus), agency ad server data (for example, DoubleClick for Advertisers [DFA], Sizmek (formerly MediaMind), Conversant, and PointRoll), and ad-quality vendor data (for example, MOAT, Integral Ad Science [IAS], comScore, and DoubleVerify). With all these tied together, you can easily associate all data sources originating from the publisher ad server.
The negative of employing advanced tag mapping is that it can be a significantly slower and more complicated process, if not impossible, to do manually. To enjoy the consistent accuracy provided by advanced tag mapping, you should look into a product or service that can automate this process for you.
As vitally important as ad server data is, ad-quality vendor data can consistently add immense value to your reporting. Ad-quality vendor data supplies viewability information, which supplements the data you receive from ad servers.
Ad-quality vendor data essentially adds another angle to your reporting, allowing for greater accuracy through unique datasets. For example, from straight on, a cube can look like just a square. Adding ad-quality vendor data provides a whole other dimension of visibility. The more unique data sets you include, the clearer, more accurate the picture of your overall advertising, providing more actionable insights and helping you make more informed decisions. (Note: Here, unique data sets refer to ad server, ad-quality vendor, order management system (OMS), etc. In this case, they are not to be confused with adding as many dimensions as possible to an individual report, which can actually give you unnecessary data and make it difficult to gain clarity into specific ad performances.)
Order Management System (OMS) Integrations
You genuinely care about helping your clients. As such, you deserve to get paid quickly and fairly for your services. But it’s not just about making sure the billing numbers look right on your end. They need to be right for all parties involved. This is especially hard to do via manual billing processes. And for operations that naturally have more discrepancies than most industries, manual end-of-the-month billing can be painstakingly grueling. Oftentimes, the only way to ensure accuracy is to spend an immense amount of extra time. Even then, it’s hard to make all the numbers match up in order to prevent rejected invoices.
The success of your billing process largely depends on the efficiency and accuracy of your OMS. It holds exclusive sets of data not available in the ad server. Therefore, after you tie all your ad server data together, you also need to tie it all back to your OMS. You must do this in order to execute your billing.
You can expedite cash flow with integrated billing reporting. This allows you set up your order management data so that it automatically links your orders to your ad servers (for example, DoubleClick's DART Sales Manager [DSM] for proposal IDs, Operative One for external line item IDs, FatTail for drop IDs, and Salesforce for associating opportunity line items). For example, with DSM, proposal line items push lines across to DFP. Other systems use IDs to push into your publisher ad server.
It’s best to work with systems that have a clear establishment of how to tie ad server data to your OMS, whether it’s controlled through a process or an automated link. With the right platform, you can even automate the upload of reconciled billable metrics to your OMS.
Automatically collecting all ad server and billing system data in a single, central location consistently promotes optimal accuracy and time savings. This approach also serves as a key component to making your reports shareable across departments. Cross-department sharing streamlines your billing process, helps you get the most from limited resources, improves company-wide awareness, and enhances decision making.
Discrepancies: What to Analyze and How Often
Are you optimizing? In 2017, we’ve been seeing the following average discrepancies for the following platforms:
DFA: 5 percent, with some over 15 percent
Atlas: 12 percent
Sizmek: 13 percent
The caution here is that even after years of improvements to all the agency ad servers, there are still significant potential discrepancies even with the biggest and most reliable platforms. What do you do when you can’t rely on consistent performance across all agencies and platforms? Regularly monitor your discrepancies. There’s a significant cost associated with these discrepancies. Many publishers solve this problem by setting a 10 or 15 percent buffer with the hope discrepancies don’t exceed their buffer limits. Even if this buffering covers most cases, that’s still a lot of inventory that can go to waste.
The goal for discrepancy reporting is to see what inventory is potentially overbooked. When you book impressions with these flat-buffered amounts, it can make forecasting ineffective, as there is inventory allocated to impressions that may go to waste. Sufficient management of your forecasted inventory will have a positive impact on the pacing of your campaigns.
Even with campaigns that have low discrepancies, you still want to keep an eye out for pacing against your contracted amounts. If you’re consistently under pacing on a line item, it could point to inventory issues, which is the culprit in a majority of cases. For example, the line item may be targeted at segments that don’t have enough inventory to deliver against. One of the easiest ways to find extra inventory is to reduce overbooking buffers.
Frequently monitoring current and forecasted inventory as well as impressions will help you identify issues early on in your campaigns. The sooner you catch problems, the faster you can apply solutions, the more revenue opportunities you can generate.