Data Driven Wins the Axiom Awards!




Talking Integration at DMEXCO












The Identity Based Data Platforms of the Future







marchitecture cake



The next wave of data management





Let’s take a look at what that might look like:

federated ID





1. A single data segmentation engine




2. Data pipelining and governance capabilities




3. Leaping from DMPs to holistic data management





4 Better Ways to Sell Technology


A lot of you guys make your living selling technology in the advertising and marketing technology space. It’s a great and noble occupation, but not for everyone. Our industry moves very fast, and software is always a stutter step behind. We are trying to solve problems for big brands and media companies, and a lot of what we sell sounds pretty much the same as the competition. Even if you truly have the best product, it’s really hard to get people’s attention. When you finally get it, it’s very hard to truly differentiate yourself and your products. In first meetings and big pitches, you have to leave the meeting accomplishing three basics: your potential customer should like you enough to work with you, trust you to do the work, and believe that your company can solve their problem.

In first meetings and big pitches, you have to leave the meeting accomplishing three basics: your potential customer should like you enough to work with you, trust you to do the work, and believe that your company can solve their problem. Like, trust and belief are pretty simple asks—but very hard to establish in meetings.

Does your typical one-hour meeting look like this?

  • Get the monitor set up and internet access established (10 minutes)
  • Go around the room with introductions (5 minutes)
  • Salesperson introduces the meeting and explains why you are there (10 minutes)
  • Salesperson gives the standard “about the company” pitch (15 minutes)
  • Subject matter expert talks about some use cases and benefits (20 minutes)
  • Demo (0 minutes. Oops. No time left for demo).

I have been in many of these meetings as a potential buyer, and I have also presided over quite a few of these meetings. Some are better than others, but for the most part, they are pretty terrible. Here are four things you can change up for your next meeting.

Stop the Slides

Here’s what happens when you deliver a slide presentation. If you show a slide with text on it, your audience will start reading it. In fact, they will finish reading it way before you stop delivering the content, and then they start thinking about what they are going to do for lunch. Maybe you think you’ve built the most perfect slide ever…full of compelling content and gleaming with ideas? Well, perhaps you have but you’ve alienated half of the room; the slide is the perfect level for the folks who already get it, and way too technical for the newbies (or vice versa). The approach here is to use a good headline and a gigantic picture of something interesting. Show a hammer, elephant, or a guy jumping out of a plane. The internet is full of great options. “Why is there a picture of a guy jumping out of a plane?” your prospect wonders. Your potential client will listen to you until he figures it out.

Grab a Marker

In the technology space, we sell a lot of complicated stuff, and we have a lot of ‘splaining to do in meetings, to borrow the popular Desi Arnaz phrase. Many of our potential customers don’t really know how the Internet works, and that’s okay. A 23-year old media planner at an agency isn’t immediately required to grok the differences between data integration types, but they still have influence over considerable budget dollars. What they need is some education, and that’s where your friend the whiteboard comes in. Why do mediocre actors salvage their careers on the stage? Because it’s harder. You have to know your material, deliver your lines, and there’s nowhere to hide. People respect that, and they will respect you when you close your laptop, pick up a dry erase marker and start explaining what your technology does, why it’s different, and how it will solve a problem. Plus, the element of theater is fun. People know exactly what you are going to say when you deliver a slide, so you will likely be judged on your delivery and the cut of your suit. Pick up a marker, and you will be judged by the size of your brain.

Show, Don’t Tell

Similar to the educational nature of whiteboarding, there is magic in a good software demo. After explaining all of the wonderful problems you are going to solve over 40 minutes, you will likely have a highly skeptical audience. Every other vendor has rolled in and also promised to solve the age-old “right person, right message, right time” conundrum, and you are just the latest in the pack. Whenever there is an opportunity to go into the software and demonstrate exactly what you are talking about, you should take it. “Did you ask about my integration with Amazon? Great, let me pull that up in our UI and show you exactly what to do.” As an industry, we also seem to suffer from using solutions engineers as a crutch. Guess what? If you need a highly technical person to walk through a few screens, then your client just found out that you have a product that only his most technical people can use. That’s a gigantic loser. If you sell software, you should be capable of giving a basic UI demo.

 Tell Stories

People are people, and they communicate best with storytelling. You don’t need to be a latter-day Walt Disney at your next meeting, but you do have to be able to tell a story similar to this: “Ron from Big Company has the same exact problem you guys are having. We worked with Ron and his team for 18 months and figured out exactly how to solve it. Ron is now an SVP. Hey, we should get you out to lunch with Ron, and he can tell you all about it.”

An old boss used to tell me that a sale needs to get your client “paid or made” We can certainly help people get paid by saving the money through efficiency, and “make” their careers with a successful implementation. People love to hear that similar people are having the same issues, and they don’t want to feel left behind. By golly, if this was good enough for Ron at Big Company it’s good enough for me. A good story should be realistic, inspire, differentiate your technology—but also be referenceable.

Because they will call Ron.

Why Most of Today’s DMPs are Lousy

8022_sucklessHow Granular Data Collection and a Robust Second-Party Data Strategy Changes the Game

The world’s largest marketers and media companies have strongly embraced data management technology to provide personalization for customers that demand Amazon-like experiences. As a single, smart hub for all of their owned data (CRM, email, etc)—and acquired data, such as 3rd party demographic data —DMPs go a long way towards building a sustainable, modern marketing strategy that accounts for massively fragmented digital audiences.

The good news is most enterprises have taken a technological leap of faith, and embraced a data strategy to help them navigate our digital future. The bad news is, the systems they are using today are deeply flawed and do not produce optimal audience segmentation.  

A Little DMP History

Ten years ago, a great thing called the data management platform (DMP) started to power big publishers. These companies wanted to shift power away from ad networks (upon whom the publishers relied to monetize their sites) and give publishers the power to create relevant audiences directly for advertisers. By simply placing a bit of javascript in the header of their websites, DMPs could build audience segments using web cookies, turning the average $2 CPM news reader into a $15 CPM “auto-intender.” By understanding what people read, and the content of those pages,  DMPs could sort people in large audience “segments” and make them available for targeting. Now, instead of handing over 50% of their revenue to ad networks, publishers could pay a monthly licensing fee to a DMP and retain the lion’s share of their digital advertising dollars by creating their own segmented audiences to sell directly to advertisers.

Marketers were slower to embrace DMP technology, and they quickly grasped the opportunity too. Now, instead of depending on ad networks to aggregate reach for them, they started to assemble their own first-party data asset—overlapping their known users with publishers’ segments, and buying access to those more relevant audiences. The more cookies, mobile IDs, and other addressable keys they could collect, the bigger their potential reach. Since most marketers had relatively small amounts of their own data, they supplemented with 3rd-party data—segments of “intenders” from providers like Datalogix, Nielsen, and Acxiom.

The two primary use cases for DMPs have not changed all that much over the years: both sides want to leverage technology to understand their users (analytics) and grow their base of addressable IDs (reach). Put simply, “who are these people interacting with my brand, and how can I find more of them?” DMPs seem really efficient at tackling those basic use cases, until you find out that they were doing it the wrong way the whole time.

What’s the Problem?

To dig a bit deeper, the way first-generation DMPs go about analyzing and expanding audiences is through mapping cookies to a predetermined taxonomy, based on user behavior and context. For example, if my 17-year-old son is browsing an article on the cool new Ferrari online, he would be identified as an “auto intender” and placed in a bucket of other auto intenders. The system would not store any of the data associated with that browsing session, or additional context. It is enough that the online behavior met a predetermined set of rules for “auto-intender” to place that cookie among several hundred thousand other “auto- intenders.”

The problem with a fixed, taxonomy-based collection methodology is just that—it is fixed, and based on a rigid set of rules for data collection. Taxonomy results are stored (“cookie 123 equals auto-intender”)—not the underlying data itself. That is called “schema-on-write,” an approach that writes taxonomy results to an existing table when the data is collected. That was fine for the days when data collection was desktop-based and the costs of data storage were sky-high, but it fails in a mobile world where artificial intelligence systems crave truly granular, attribute-level data collected from all consumer interactions to power machine learning.

There is another way to do this. It’s called “schema-on-read,” which is the opposite of schema-on-write. In these types of systems, all of the underlying data is collected, and the taxonomy result is created upon reading all of the raw data. In this instance, say I collected everything that happened on a popular auto site like I would collect how many pages were viewed, dwell times on ads, all of the clickstream collected in the “build your own” car module, and the data from event pixels that collected how many pictures a user viewed of a particular car model. I would store all of this data so I could look it up later.

Then, if my really smart data science team told me that users who viewed 15 of the 20 car pictures in the photo carousel in one viewing session were 50% more likely to buy a car in the next 30 days than the average user, I would build a segment of such users by “reading” the attribute data I had stored.  This notion—total data storage at the attribute (or “trait”) level, independent of a fixed taxonomy—is called completeness of data. Most DMPs don’t have it.

Why Completeness Matters

Isn’t one auto-intender as good as another, despite how those data were collected? No. Think about the other main uses of DMPs: overlap reporting and indexing. Overlap reporting seeks to overlay an enterprise’s first party data asset with another. This is like taking all the visitors to Ford’s website, and comparing that audience to every user on a non-endemic site, like the Wall Street Journal. Every auto marketer would love to understand which high-income WSJ readers were interested in their latest model. But, how can they understand the real intent of users if they are just tagged as “auto intenders?” How did the publisher come to that conclusion? What signals contributed to having that those users qualify as “intenders” in the first place? How long ago did they engage with an auto article? Was it a story about a horrific traffic crash, or an article on the hottest new model? Without completeness, these “auto intenders” become very vague. Without all of the attributes stored, Ford cannot put their data science team to work to better understand their true intent.

Indexing, the other prominent use case, scores user IDs based on their similarity to a baseline population. For example, a popular women’s publisher like Meredith might have an index score of 150 against a segment of “active moms.” Another way of saying this is that indexing helps understand the “momness” of those women, based on similarity to the  overall population. Index scoring is the way marketers have been buying audience data for the last 20 years. If I can get good reach with an index score above 100 at a good price, then I’m buying those segments all day long. Most of this index-based buying happens with 3rd-party data providers who have been collecting the data in the same flawed way for years. What’s the ultimate source of truth for such indexing? What data underlies the scoring in the first place? The fact is, it is impossible to validate these relevancy scores with the granular, attribute-level data being available to analyze.

Therefore, it is entirely fair to say that most DMPs have excellent intentions, but lack the infrastructure to perform 100% of the most important things DMPs are meant to do: understand IDs, and grow them through overlap analysis and indexing. If the underlying data has been improperly collected (or not there at all), then any type of audience profiling by any means is fundamentally flawed.


What to do?

To be fair, most DMPs were architected during a time when it was unnecessary to collect data through a schema-on-read methodology—and extremely costly. Today’s unrelenting shift to AI-driven marketing necessitates this approach to data collection and storage, and older systems are tooling up to compete. If you want to create a consumer data platform (“CDP”), the hottest new buzzword in marketing, you need to collect data in this way. So, the industry is moving there quickly. That said, many marketers are still stuck in the 1990s. Older DMPs are somewhat like the technology mullet of marketing—businesslike in the front, with something awkward and hideous hidden behind.

Beyond licensing a modern, schema-on-read system for data management so marketers can collect their own data in a granular way, there is another way to do things like indexing and overlap analysis well: license data from other data owners who have collected their data in such a way. This means going well beyond leveraging commoditized third-party data, and looking at the world of second-party data. Done correctly, real audience planning starts with collecting your own data effectively and extends to leveraging similarly collected data from others—second party data that is transparent, exclusive, and unique.

Five Principles of Modern Marketing





Here are five:

Put People First




Collect Everything, Measure Everything




Be A Retailer



See The World Dynamically



Think Like A Technologist



