Features

Crossing the Channel Part 1: Digital Display Legos

Written on
Jan 27, 2015 
Author
Katrin Ribant  |

A new series by Datorama Cofounder and Chief Solutions Officer Katrin Ribant

As industry professionals, we are suffering from a silo-syndrome epidemic, and the symptoms run deep.

Before we get any further, I’m just going to say it: The days of siloed data are over. If you want to keep your marketing silos separate, you can stop reading now. This is a multigrain series.

Silos make reports, and reports, in turn, make more reports. Reporting takes valuable time out of marketing practitioners’ days, keeping them from focusing on what they should be doing anyway: extracting insights. There is intense industry pressure to marry omni-channel data in a coherent and actionable way, and, up until recently, it has presented a significant challenge for everyone involved – agencies, technology providers, publishers and brands alike. We need to cut down the amount of time it takes us to sift through and join disparate data and focus on what actually creates value: critical understanding of the information created by it.

Crossing the Channel will dissect the complexities of assessing marketing performance across channels – particularly, and perhaps most crucially, on advertising platforms. We will begin, in this post, by looking at the advertising landscape from a top-level perspective.

The omni-channel marketing technology landscape is massive, fragmented, and growing rapidly. Everyone can agree on just complicated the ecosystem has become.

Digital Display Legos

Making sense of the online display ecosystem can be a time-consuming task for even a seasoned marketing veteran. Among the countless questions that can arise, a few usually stand out.  For instance, are ad servers and DSPs better off existing in their own separate realms? How is buying affected across desktop and mobile platforms? Where do retargeting, verification, DCO (dynamic creative optimization), and data overlays enter the picture?

When approaching the ecosystem from an analytical perspective, measurement is always key. Aside from human error, fragmentation of technologies and data streams is, more often than not, the cause of ineffective measurement and inefficient analytical insight.

For most of the industry, the ad server functions as the glue that holds display media plans together. DSPs, in their own right, work as execution platforms focused on enabling buys on ad exchanges.

 

 

 

 

Except it’s never that easy, is it?  We see data come in and data go out. Creatives get placed and metrics are reported from every platform and technology. This introduces a lot of complexity to today’s media buyer’s world, and it’s their job to make sense of it all. At the heart of understanding performance is the ability to interpret spend at the most granular level and across a holistic media plan.

 

 

 

 

 

 

 

 

Planned vs Actual Media Spend

Typically, this is what the hierarchy between an ad server and a DSP looks like: Here’s just a taste of the type of metrics that can sometimes take many man hours to understand and explain:

  • Planned versus actual media spend – used to optimize your DSP targeting strategies
  • CPA for DSP strategies
  • CPM across the media plan
  • Cost per video fully played and other intermediary metrics

In order to match DSP media cost to each adserver, you need to use the ad server’s “tag” (placement) ID that was trafficked in the DSP. Most DSPs expose this field.  Then, you need to know which entity in the DSP maps to this entity in the ad server.

 

Assessing CPA Performance Across Premium End RTB

At this point, you discover what entities in the DSP roll up into each placement.

è This is the step that informs your optimization.  From here, you understand which targeting strategy is getting delivery and how you need to optimize.

In order to achieve this and understand how you are pacing towards your budget on a daily basis—which is essential—you need to perform the extracting, copying and collating every single day. The challenge lies in the massive amounts of rows of data and the lack of time you have before you must start optimizing. But if these challenges are not met, how do you effectively balance your targeting strategies to deliver your targets? Join us next time for a deep-dive.





After more than eight years at Havas Digital, Katrin Ribant left to cofound Datorama, for which she serves as chief solutions officer. Using data to answer real-world questions, and building technology that helps more people solve more unknowns, is what makes her tick.

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