In this day and age, information is, probably, one of the most valuable things in the world. No wonder — it is the stuff dreams are made of, something of a new universal currency. However, one should know how to use it in order to make it work for his own benefit.
There is no industry where this is more apparent than in the advertising industry and AdTech in particular, where competition is so tough one false move can derail everything. This is where Data Management Platforms (DMP) come in and work some sweet AdTech Magic.
The thing is – modern advertising is extremely complex and multi-faceted operation. Another important thing to note is that the ad industry is massive. Not just very big, but Godzilla-like imposing and intimidating massive. Every little thing counts and makes difference. There are many moving parts involved and all of them are equally important.
Modern DMP’s are required to process millions upon millions of events in a short time span. Just think about that number — it is ridiculous. And it takes a significant computational capacity and very flexible scalability to make it all click.
But first, let’s sort things out.
What is a Data Management Platform (DMP)?
Data Management Platform (DMP for short) is a type of centralized tech platform that gathers data from a set of sources, segments it to predetermined categories, and further transfers in order to attain certain goals of a marketing campaign.
To put it simply, the Data Management Platform is one of the foundations of Ad Tech operation. It is one big dashboard of tools that gives you the big picture of what is going on with your efforts and provides instruments to turn the tide to your benefit.
DMP control panel is basically a war room where you can check the status of the situation and plan your next moves.
DMP and AdTech: How do they work together?
The primary purpose of DMP in Ad Tech operation is to keep a firm grasp on the proceedings of the campaign. The use of information gathered by the Data Management Platform provides additional agility to the Ad Tech operation — it improves the definition of the target audience and subsequent ad distribution.
That gives marketers agility in adjusting the campaign as it goes on according to the target audience’s reactions.
The end result of its DMP process is more efficient and precise targeting to the target audience that generates conversions that in turn enhance ROI from ad spending. This is a very important thing because gestation and hesitation are rather destructive approaches in marketing.
Here’s what Data Management Platform can handle:
- Manage and adjust ad campaigns;
- Provide with stats that can help to increase conversion rates, improve user experience, and establish the brand;
- Make effective use of ad budget with a higher probability of return on investment;
- Personalize content shown to the users in order to increase the probability of conversion and establish the brand;
- Study the behavior and preference of the target audience in order to create a credible user profile for more efficient targeting.
What types of data does DMP collect?
Data Management Platform is capable of collecting data from the selected source. All you need to do is to define what kind of information you are interested in.
Basically, a Data Management Platform is attached to the source of information (for example, a website) and gathers information regarding certain user activity.
This information is subsequently merged together into one big picture that can help marketers to understand how to build the campaign and what kinds of approaches will be the most effective with the selected target audience.
All data is divided into first-, second-, third-party. Let’s break it down:
First-party data includes:
-
- Web / App data
- Data coming from the analytics tools (such as Google Analytics)
- CRM
- Transaction systems
- Subscriptions
- Audience information
Second-party data is exactly the same as first-party except it is given to another company involved in the operation.
Third-party data is collected and segmented independently of the company and later sold to it.
Self-Hosted or Third-Party?
The difference between custom and third-party data management platform is rather peculiar. It all depends on the need of your business operation.
In the case of the third-party solution — you get a full package with a bob and bunny. It is ready-to-use and guaranteed to operate adequately. But there is one very important thing to note. When you implement the third-party solution — you pay for lots of features that might not need at all. That is not exactly cost-effective. In fact, it can actively bleed your marketing budget if the turnaround will be big enough.
A custom solution is a more complicated but more reasonable approach. Sure, you need to do the heavy lifting of developing the foundation. But you develop the platform exactly fitting to the needs of your AdTech operation. Which makes it far more effective and capable of generating rapid ROI. However, in the case of self-hosted DMP you need to find providers of third-party data on your own.
Ways to use DMP for AdTech
Retargeting
Retargeting is a method of delivering relevant advertising content to the users based on a digital footprint and collected user data such as preferences and on-site behavior.
Retargeted ad content is based on the actual interests of the users calculated out of their behavior on the source site. This makes ad content significantly more relevant to the users. That peculiar detail ups the chances of getting those sweet conversions i.e. purchases or downloads.
You can read about it in detail here.
Data Analytics
You can’t build an ad campaign without having a clue of how your target audience perceives your brand, behaves on your websites, and consumes your ad content. That is what Data Analysis is for.
DMP is useful for Data Analytics because of its scope. You get the big picture and thus you can act accordingly. With a steady stream of data going through DMP you can easily spot every little change in behavior, all while discovering trends and preferences, points of drop-offs, and so on.
That gives you a critical advantage as you can adjust on the fly without bouncing off.
Audience Research
Audience Research is such a significant element of Data Analytics that it deserves a separate spot.
The thing is — targeting requires very clearly defines customer identity in order to click. Since users are not bound to use the only type of devices — they are often visiting one place over multiple devices. All those visits can be processed as a separate ones which is not very helpful.
However, with a little help of DMP, you can construct a unified cross-device user profile (AKA single customer view) and target a particular or all platforms he is using. How? DMP matches cookies coming from the user and assigns them to a single profile.
SEO Optimization
Another huge area where the Data Management Platform is extremely useful is SEO optimization. How? The whole audience research thing can be used not only for more efficient delivery of ad content for your internal needs too.
Basically, DMP helps on three SEO fronts:
- Content — a better understanding of what your target audience is interested in;
- Keyword Research — profiles can help to find more fitting keywords for content;
- Link Building — profiles can help to find better spots for guest posts;
Data Monetization
There is also another method of effective use of DMP. You can just gather information and sell it to other companies — i.e. you can be a third-party data provider.
In that case, you don’t need to worry about anything and just maintain a steady flow of data.
How does DMP work?
Data Collection
The initial stage of DMP operation is to collect data from the selected sources (i.e. first-party) and implement data from second and third-parties.
In the case of first-party data, the operation is performed in a variety of methods. Let’s count them down:
- Tags — with a little help of Tag Manager (Google’s is a fine one) you can insert snippets of code into a website’s pages that will be tracked according to determined function;
- Cookies via Cookie Syncing — mapping and unifying user’s ID over the multiple platforms
- Pixel Tracking
- Integration with second-, third-party data suppliers;
Data segmentation
The next Stage of DMP Operation is Data Segmentation. Once information is gathered — DMP organizes it according to the present taxonomy. It includes a variety of parameters. Some of them involve the user’s personal data, others include data regarding their interaction with monitored entities.
Segmentation taxonomy is wholly dependent on the selected marketing model and includes only those elements that are vital to efficient targeting.
Data Analysis
Once data is segmented, it is processed to construct a clearly defined customer profile for targeting.
Usually, this operation involves an analysis of users’ past activity on-site, their events and impressions (clicks, etc), preferences, and response to ads.
Data transfer
Once information is gathered, segmented, and analyzed — it can be transferred to ad exchanges, Supply-Side Platform (SSP), and Demand-Side Platform (DSP) which in turn will deliver the goods to the advertisers.
This information will help to perform more accurate ad buys during real-time bidding (RTB) operations.
Possible Challenges with DMP
Integration
Before the whole data management starts — you need to set up the connection between sources. Your ultimate goal is to maintain a steady transmission of data from multiple sources without missing a beat and stumbling into a mess.
In order to make that happen — you need to be sure that all the sources meet system requirements and are compatible with others.
Dealing with Scalability
DMP is as good as its scaling capacity. That is one element essential to its successful operation. The thing is — standard Ad Tech operations consist of millions upon millions of various events happening on sites. And every single bit of this information must be collected.
And if the system can’t handle such a workload — troubles ensue.
One of the most effective solutions for DMP scalability is to use a cloud platform. The majority of services provide automatic scaling features that will seriously ease up the challenge.
Data Storage
Managing data is one thing. But you also need to store it somewhere and that is a challenge. You have an infinite stream of incoming information. It is constantly collected, processed, segmented, and transferred. You have data in active use and data that was already used. All these things have to be stored safely.
One solution is by having your own server network. It is not exactly cost-effective but it can be.
The much more feasible solution is by maintaining the data in cloud storage. Since you have to pay only for used space — it is more or less cost-effective. It also covers the necessity of maintaining backup storage as the cloud is basically an ultimate backup.
In order to make sense of storage spending, you need to apply a multi-faceted approach. In essence, it is further segmenting of the data according to its current value and relevance. In that case, that challenge lies in a correct definition of time windows for data transfer.
Refining Automation
DMP deals with large quantities of data coming from various sources — that creates a necessity of creating a set of automated scenarios that will handle the operation with its own settings. We’re talking about millions of events per second — there is no chance a human being will be able to deal with it manually.
On the other hand, automation is far more reliable in comparison with the error-prone manual approach.
The challenge comes in determining where automation is necessary and where it might be abundant or ineffective.
Woes of Data Analysis
The thing with Data Analysis is that you have to know what are studying data for in the first place in order to make use of it. If you don’t really know what kind of data you need — you will end shooting in the dark which is not exactly the most effective use of time and money.
In order to understand the most effective and feasible approach to analyzing incoming data, you need to understand the nature of the sources of the data and its credibility. Next, go to audience research which gives you an understanding of target audience attitude, and then comes specification of data segmenting according to audience research.
Each step of the way must be thorough and through and through. The price of insufficient or downright faux data can be utterly devastating to the company.
Personal Data, Privacy & GDPR
The full adoption of GDPR is a game-changing moment for the Ad-Tech industry.
On one hand, GDPR’s expansion of the definition of the user’s personal data is forcing drastic changes of approaches to deliver ad content. On the other, it is a chance to bring transparency and trust to a rather murky realm of Ad Tech.
In the long run, GDPR will turn Ad Tech’s use of DMP on its head. The thing is — GDPR compliance is not a joke.
Technically, that means you need to ask yourself the following questions:
- What kind of data are you going to collect and from what sources?
- Where is collected data going to be stored? How long is it going to be stored?
- Who will have access to it?
- Are third-parties involved in the operation privacy-compliant?
Another important thing is to have a system that will store information about the use of information. That is a critical factor in building a fully transparent and trustworthy operation.
Conclusion
At the moment, Data Management Platforms are one of the most effective ways of delivering quality advertising content to the target audience.
However, DMP is an entity that requires to be handled with care.
If done right, DMP is smooth and precise and greatly helps in keeping the pulse on the proceedings of the marketing campaigns.
We hope this article explained what’s, how’s, and why’s regarding this technology.
