The Display LUMAScape Explained

Ah, the LUMAscape, who in the digital marketing world doesn’t know it as an old friend at this point?

Display LUMAScape

First debuted in 2010 by the ad tech banker Terry Kawaja, the LUMAscape has been through many iterations at this point, adding companies, changing categories and noting acquisitions over the years.  From the very beginning this image was a hit with the digital marketing set as it provided a way to understand a complex industry, as well as a symbol for how difficult it is to work in a space so complex!  The LUMAscape was a great way for ad technology people to explain the growing industry within their own companies in a visual way, as well as understand how new companies were aligned and fit together. Kawaja & team’s image was also a solid way to understand what a whole lot of companies even did, so if you were say, shopping for a data management platform, you could get a quick sense who the four or five companies in the space were.  For Kawaja’s company, LUMA Partners, the LUMAscape was also a great way to show the sheer amount of fragmentation in the industry and possibilities for consolidation through acquisitions, on which his company specializes in advising.

Whatever the motivations, the LUMAscape is an iconic image in the digital marketing industry, and a must-know resource that Kawaja’s company has generously kept up to date for nearly five years now.  But the graphic itself only tells a high-level story and can oversimplify, as the LUMA Partner’s website readily admits, so I thought it could be useful to take this image down one more level and explain some of the nuances and sub-categories within each service.  This article describes what each category coves, what a lot of the companies on the LUMAscape actually do, as well as the differences between key services within specific categories.  For those that are new to the industry, I hope this post not only demystifies this graphic, but gives you a well-rounded sense of how the digital marketing industry functions, and for industry veterans who already know the basics there’s probably still a few things to learn. (more…)

Diagramming the Header Bidding Redirect Path

I’ve gone over how header bidding works with header tags in an earlier post and some of the key differences and points to understand, but I’ve always strived to technical bluntness on this site as well, hence the diagram and step-by-step path below.  It could be a good idea to re-familiarize yourself with how ad serving works and the standard exchange redirect path at this time as well.  Note that header bidding is also sometimes referred to as ‘advance bidding’, ‘tagless’, or ‘pre-bid’ integrations.

 

How Header Bidding Works

Header Bidding Step-by-Step:

  1. User requests a website
  2. Header tag script redirects user to one or many SSPs (or DSPs, or Exchanges)
  3. User calls one or many SSPs in parallel
  4. SSPs conduct auction with DSPs and internal network demand*
  5. DSPs respond with bids*
  6. SSP determines winning bid value and returns to User
  7. User passes bid value into ad request and calls Publisher Ad Server
  8. Ad server determines final line item to serve and redirects User to Marketer Ad Server (let’s assume the ad server determines a pre-bid SSP line item for this example)
  9. User calls Marketer Ad Server
  10. Marketer Ad Server returns final creative (via CDN)
  11. User calls trackback to SSP

* It’s hard to tell if everyone or no one actually runs an auction with their header tags because everything happens quite fast relative to your standard exchange process.  My sense is that there is either some kind of estimation process vs. a real auction, or some fancy stuff happening with the SSP’s CDN, but without the product people or engineers from these companies actually telling me what happens it’s impossible for me to know.  Here’s hoping a few visit the comments section on this article.  The important point is that the SSP determines a value for the impression that the publisher can use in their ad serving decision process. How precisely that happens and if it’s better / worse than the standard auction process is an open question. (more…)

Header Bidding: Holistic Ad Serving Is Here

It’s been nearly three years since I first wrote about the concept of holistic ad serving – the idea of seamless yield management across sales channels, namely direct sold and programmatic – but in the last six months or so this idea has quietly gone from the drawing board to reality via a mechanism known in the industry as ‘header bidding’, ‘tagless‘, ‘advance bidding‘ or ‘pre-bid’ integrations because they rely on a piece of JavaScript in the publisher’s header to work. Header bidding is a revolutionary enhancement to the way publishers have historically integrated with their SSP partners, and has wide ranging implications for nearly every part of the RTB ecosystem. The future is now!

Header bidding integrations, or something very close to it have been common for years with retargeting networks like Criteo seeking ‘first-look’ relationships with publishers, but it’s only recently that the major sell side platforms started to integrate this way. The major difference between the retargeters and SSPs is that retargeters have all the demand within their own platform, while the SSPs and Ad Exchanges rely on an auction with external parties to fill impressions. (more…)

Does Lazy Loading Ads Solve the Viewability Problem?

What Does Lazy Loading Ads Mean?

Traditionally in web design, a browser calls a web server, which returns all the HTML necessary to render the entire page to the browser in a source code file.  Now that file may contain redirects and references to other web servers that the browser has to call in order to fully render the page, but the general idea has been that browser fully loads the whole page for the user as quickly as possible.  Such that, if the page is extremely long, contains lots of images, or what have you, the whole thing is rendered at once, irrespective of the user’s navigation.  In other words, the browser renders everything, whether the user ever views that content or not.

“Lazy loading” (sometimes also known as “just-in-time loading” on the other hand, is a relatively new method of web design that renders the page on an as-needed basis, just as the user is scrolling their browser down to that piece of content.  If you’ve seen pages that contain an “infinite scroll” design, you have the general sense.  The content available to the user isn’t all loaded at once, because it would take forever; rather, the page renders as you the user scroll to it.  If you don’t scroll down, the content isn’t rendered.  So lazy loading any web content, ads included means the web server only provides the necessary source code to the browser as the user needs it. (more…)

Lookalike Modeling Your Ad Ops Team Can Build With a DMP

Digital Publishers and Advertisers that have access to a Data Management Platform (DMP) can bootstrap their own data modeling, or lookalike model capabilities with some simple index-based approaches.  That is to say, if you can understand both the total population of users for every segment and for any specific segment, how many users of every other segment overlap in that target segment, you can build a fast and easily understood audience model with a little legwork. It’s not the rocket science approach of a regression model or black box algorithm, but it works, and it’s pretty easy for people without a degree in data science to execute once you figure out how to get the right data out of your system.

How to Do Lookalike Modeling Yourself

The first step to building a lookalike segment is to first define what you are trying to model, that is, what audience you want want more of.  This will be your ‘target’ – for our example here, let’s consider the following audiences:

SegmentQualified Users% of Total
Women 20,000 20%
Pet Owners 5,000 5%
Coffee Drinkers 8,000 8%
Outdoor Enthusiasts 9,000 9%
Total Users 100,000 100%

Let’s say we’re trying to reach females.  Unfortunately, we only have 20,000 we can identify, out of a total population of 100,000.  Now let’s assume that our content isn’t skewed to one gender or another, and therefore there’s clearly some users in the 80,000 other users that we can expect would be female.  But we need to find a signal within that group that directs us to which other audiences are likely to be female. (more…)