Post GDPR, Are Data Clean Rooms The Answer To Accessing Walled Gardens For Programmatic Buyers?

As GDPR enforcement becomes a reality not only in Europe but also here in the US, advertisers are struggling to find a way to scale the walled gardens and optimize their data assets.

As of May 25, 2018, Google announced that DCM users will be unable to use cookies or mobile device IDs to connect impressions, clicks and site activities from the DCM logs, users will be limited to Google’s own Ads Data Hub for those metrics.  For some, this means that they are satisfied to stay within the Google stack but not every brand’s solution will be and should be limited to Google.  But if media buyers want to analyze their spend outside of Google’s platform and offer up any attribution, then just using Google won’t work.

“Some marketers who spend 75 percent or more of their budgets on Google will be fine just letting Google do the analytics,” says Alice Sylvester of Sequent Partners.

Google wasn’t the only one to lock down their platform.  In response to the combined pressure of GDPR and the Cambridge Analytica scandals over its handling of personal information, Facebook decided that it would shut down ad tools called “Partner Categories” powered by outside data brokers. Those tools let Facebook advertisers target ads at people based on third-party data such as their offline purchasing history.  This means advertisers will have access only to their own data and data Facebook collects itself.  If an advertiser wants to pull campaign-level insights to inform future campaigns or use the data for the basis of an attribution model then they are out of luck.

Introduction of Data Clean Rooms

Data clean rooms allow large inventory partners like Facebook and Google to share customer information with brands, while still maintaining strict controls in place.  Data clean rooms were named for the completely airtight rooms where microchips and other sensitive materials get made.  In this case, the rooms enable a shared environment between two or more companies that is completely secure from external access (no wifi) where each company decides the level of visibility to their data.  This eliminates the possibility of data leakage for companies like Facebook which caused the Cambridge Analytica mentioned earlier.

“We and a partner combine a data set with very specific rules and controls around how each party can operate within the shared environment,” said Scott Shapiro, a product marketing director for measurement at Facebook, who noted that Facebook didn’t invent the clean-room concept.

The concept is to create a safe space where data can be share and manipulated without leaving the inventory partner’s environment.  Specifically for Facebook, a brand can create an audience based on first-party data, like a list of email addresses and then push that list into Facebook, match it, and grab a copy which they can later combine with their data as the basis for attribution, measurement and modeling.
How it happens in reality is that an advertiser will lead a clean or wiped laptop or device that has never been connected to the Internet with that advertiser’s first party data, which in most cases is an email list.  A second clean computer is loaded by Facebook or Google with impression-level and non-PII campaign data.

Maybe, The Answer to Scaling The Walled Gardens?

For advertisers with a lot of data and substantial programmatic advertising budgets this is a great opportunity to scale the otherwise elusive walled gardens.  The data clean rooms create a safe environment for data providers to share the marketing data that brands need and crave to model future media buys and advertising strategies. If managed in the right way, with the right methods and standards, this would be the tool for brands to really understand their walled-garden ad spends within the larger marketing ecosystem.  For advertisers and publishers there is a lot at stake in the post GDPR world of data governance.  There is no room for unintended data sharing because the consequences are too great.

Marketers have been eager to get more insights out of Facebook and other walled gardens but it’s unclear how many brands or agencies will take advantage of this opportunity to get more out of their spend with the largest inventory providers.  From Facebook’s perspective they are not advertising the data clean room solution because if they gave advertisers too much access to data buyers might eventually become less reliant on their platform for scale and identity data.  But Facebook and Google also don’t want to piss off their advertisers because they are demanding more data so this is the solution that they can offer for brands that pressure them to giving them more insights.  There is still the issue of the manpower involved and the fact that the data is limited to a snapshot in time but advertisers who buy into this solution are fully aware of what they are getting and have to decide if the value is worth the effort.

7 Things Brands Need to Know Before In-Housing Programmatic Media Buying

In 2018, brands and marketers have made it clear that they  want increased control of their programmatic advertising efforts. Digital advertising spend is estimated to overtake offline spend, with programmatic already surpassing direct digital buying. In more advanced markets, the media buying industry is aimed at a programmatic future.

Marketers have grown frustrated with the current business model; they want better control of their data and more financial transparency. A report by the World Federation of Advertisers (WFA) report finds that 90% of advertisers are reviewing and resetting both business models and contracts to achieve those goals. A survey conducted by Infectious Media found that over 70% of marketers think agencies have struggled to adjust to programmatic and they do not think the agencies accurately measure their programmatic media buys.

7 Things to Consider Before In-housing Your Programmatic Media Buys

With this loss of trust, it’s no wonder why brands are taking steps to bring their programmatic campaigns in-house. However, in order for them to be successful,  there are some steps they need to take. We’ve put together a check-list of what we think brand marketers need to know. Here are 7 things you need to consider if you want to bring your programmatic in-house.

1. Budgeting, resources, and hands on keyboard

The first thing to consider is evaluating your brand’s capabilities. Is the budget large enough? How many people will be on the programmatic team? Will they be able to stay up-to-date with the latest technology?

Brands must be spending at least $20 million programmatically before they even consider taking programmatic in-house, in order to generate a high enough level of savings to make the transition worthwhile.
Wayne Blodwell, CEO of The Programmatic Advisory

On top of a high cost, programmatic technology is complex; it requires a unique skill-set and it is hard to master. The technology requires an expert or multiple specialists at the helm. Hiring and training new recruits is not a simple process, especially if your office happens to be outside of New York, San Francisco or Boston.
After deciding which kind of technology stack is best for your brand (DSP, DMP, ad server, viewability tracking, dashboard, fraud protection) there are also other aspects to consider like licensing. This includes legal documentation, adherence to privacy regulations, etc.

Other forms of digital advertising, namely search, is dominated by a single player. Programmatic, on the other hand, lives in a complex environment that has many options of inventory to choose from. Several demand-side partners that can be used to access them and also several programmatic models to navigate through, like open exchanges to private marketplaces.

This goes back to having the right personnel for the job. New roles in the organization will open because of in-housing and it is up to you to have the right training methods for current employees. As mentioned before, programmatic technology is complicated and the right people must be on the job.

2. Objectives

After a programmatic team has been established it is time to understand the short and long-term goals of the business. Key considerations and questions at this point would include: Debating whether display, native and reach based advertising would help reach the long-term business goals, or whether inbound is a better fit, is an in-house team going to be more effective because of the increased frequency of campaigns?

3. In-housing goals

Besides long-term business objectives, identifying the end-goal of an in-house programmatic process is critical too.

  •       Do you simply want to purchase media in a more effective way?
  •       Do you want to maximize reach?
  •       Do you need better targeting and segmenting or are you looking to go wide?
  •       Do you want a broad mix of outbound- from display to native to video and mobile or are you limited to one or two formats?
  •       Or perhaps you also want to incorporate offline data to effectively take prospects along the typical buyer’s journey?

4. Big Data

One of the biggest and challenging tasks is being responsible for your own data. Collecting, managing, and then interpreting it for valuable insights can become rather tedious. If big data is too much to handle, hiring a separate data team can also be an option. Data-backed programmatic is extremely desirable today but it needs to be managed by disciplined professionals managing first and third party data

5. Cross-departmental Collaboration

It is important to make sure all departments are aware of the organization’s new programmatic team. Illustrate how an in-house programmatic process will benefit the whole business through increased sales, ROI, and customer satisfaction, not just the marketing department. Alignment with sales is also crucial in terms of making the most of leads generated via programmatic.

6. Implementation, testing, and execution

Different brands benefit from different programmatic models, determining which ones work best for you require testing.  Testing new tactics and programmatic strategies in-house for a short period of time may help your company adapt to the overall programmatic process before identifying a model that works best.

7. Consider a hybrid in-housing model


It may also be wise to consider using a hybrid approach. You may have a strong analytical data team, with data management experience, but not the talent or knowledge for programmatic execution. Or you might have built a strong digital marketing team, but they don’t have the skill or knowledge specifically in programmatic media buying. These are key skills that are worth outsourcing to a trusted agency of record while keeping strategy and data in house.

As much as having more control and transparency over programmatic media buying makes sense, the required investment in talent, expertise to navigate the ecosystem and budget size should not be overlooked. For now, if you are a brand considering starting the in sourcing process then you should consider a hybrid model where you own the contracts and data and your trusted partner, like Digilant, owns the rest.

Programmatic Media Buying 101: What is the Difference Between AI, Machine Learning & Programmatic?

The world of digital advertising and programmatic advertising has developed its own language in the last couple of years, full of terms that are commonly heard and used everywhere but mean something very specific when attached to the word advertising. Most recently it’s almost impossible to read an article or even talk about media buying without bringing up the terms Artificial Intelligence (AI) or Machine Learning. The terms AI and machine learning are often used interchangeably but they are different. What is the difference between the two and what should they mean to us or me as a marketer or CMO?

AI for Programmatic Buying

Artificial intelligence is the concept of reproducing human intelligence in machines so they can execute on activities that normally would require a human brain to be involved in, such as making data-based decisions.  By using AI-powered systems brands and advertisers case save money and time by completing tasks faster than us mere humans and make less mistakes.  When you apply this to the programmatic media buying industry, you bring efficiency to the media buying process, freeing people who’s job it is buy media from the more tedious and allowing them to focus on the strategic and creative elements of their jobs.

The reason digital media executives keep talking about AI technologies is that they allow us to have algorithms that analyze a user’s behavior, allowing for real time programmatic campaign optimizations towards consumers who are more likely to convert. Advertisers then have the ability to gather all this rich audience data to then use it to be more accurate with their media buys and overall targeting tactics – ultimately spending less money and time and bringing in a higher ROI.

Will Machine Learning Replace Media Buyers?

The words Machine Learning can conjure up images of old sci-fi movies in which someone develops an intelligent robot that then dominates its creator or destroys a large city… leading to many questions about how this technology could affect the digital media industry.
Machine learning is a type of Artificial Intelligence that provides computers or robots with the ability to learn things by being programmed specifically to take certain actions, improving their knowledge over time, much in the same way our brains do.
Computers using machine learning focus on imitating our own decision-making logic by training a machine to use data to learn more about how to perform a task.

Imagine you ride your bike to work every day. Over time, after trying different ways to get to work, you will learn which route is faster or maybe which road or path is better according to the day of the week or based on the weather outside. This is exactly how machine learning works. You feed the computer or algorithm with large amounts of data so it will analyze information from the past and learn from it to apply the learnings to any new data it receives in the future.

When applied to programmatic advertising, machine learning algorithms can analyze large volumes of data from difference sources and draw conclusions from it. It means you can almost replicate the brain of an experienced media buyer in a machine or algorithm so it becomes capable of  predicting, planning and optimizing media. Almost…. but not yet, though the machines can certainly make programmatic advertising more efficient, faster and easier to implement, there remain many factors which need human brains to input link the machine learning to an overall media buying strategy.

So How are AI and Machine Learning Connected to Programmatic Advertising?

Programmatic advertising is the automated process of buying and selling ad inventory through an exchange, connecting advertisers to publishers rather than having to make individual deals with each publisher. This process uses artificial intelligence technologies to improve efficiency and make better decisions for the advertisers with their budgets.
There is a lot of investment being made in marketing and ad buying technologies to leverage AI.  Companies like Xaxis, are betting heavy on AI for improving their future Programmatic Buying Platforms.  Fo right now marketers are using AI to stitch massive amounts of their data together, but it still hasn’t replaced human analysis.  For media agencies, Artificial Intelligence is still more a buzzword or a catchphrase to get peoples attention.

David Lee, programmatic lead at ad agency The Richards Group, said that he regularly gets pitches for AI-enabled products but the AI part of the products usually “doesn’t seem to affect performance outside of being a buzzword.”

You need Machine Learning to feed AI but you don’t need AI for Machine Learning. What that means is that machine learning is the technique — using algorithms to process data, learn from insights and make predictions for future programmatic campaigns which then trains the AI.
Both Machine Learning and AI are here to stay.  If you are a marketer or a media buyer, get familiar with these terms as they will continue to occupy the press and blogs like ours.  But for now they are not taking over for humans, that’s still in the sci-fi section of the video library.

Programmatic CRM + Perfect Pixel: Together in Action

Footwear and apparel brand Johnston & Murphy wanted to capture online customer activity to better assess the influence that their various media efforts were having on purchase behavior.  Coupling this data with their existing CRM profile would allow the retailer to prioritize their marketing and programmatic media buying investments and provide a complete view of customer activity to inform advertising content.


Download the full Case Study Here.

To achieve this, Johnston & Murphy combined the intelligence collected by Perfect Pixel with Digilant’s tracking cookie to close the loop across channels and create a personalized customer journey. Customer Portfolios then leveraged their Smart Cookie to deliver the CRM profiles, enhanced by Perfect Pixel, to Digilant’s tracking cookie. This provided Johnston & Murphy a complete view of all online and offline customer activity, letting Digilant activate this customer intelligence through programmatic media buying channels.

“This partnership allows J&M to take the holistic view of our customers and put it into action across our digital efforts, helping us be more efficient and effective with our media spend.” said Heather Marsh, Vice President of Commerce, Johnston & Murphy (Genesco).
“Our time and money is focused on delivering what our customers want; through the work of partners like Digilant and Customer Portfolios, we’re excited to be able to quantify the impact of our efforts through in-store sales.”

With the powerful Customer Portfolios CRM Profile — enhanced by Perfect Pixel — providing the intelligence for personalized content, Johnston & Murphy is now able to deliver digital messaging at a 1:1 level and complement all of their other marketing efforts. The partnership has led to a lift in sales for the audience exposed to media through Programmatic CRM. Moreover, Johnston & Murphy is able to attribute twice the amount of sales to these initiatives by including offline matchback delivered from Perfect Pixel.

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