In the advertising world, the data management platform is everything. And no wonder we need an efficient platform to manage and utilize data for the best interest of businesses.
While conducting an advertising campaign, all efforts will go in vain if you ignore the importance of data-driven results that are achievable through advanced analytics and granular targeting options.
What is a Data Management Platform?
In layman terms, the DMPs gather data, arranges it, and allows other online advertising components to make use of it.
It stores user and campaign data, offering a centralized hub to use this data and devise targeting segments for defined purposes.
The volume of data available out there is beyond the realms of our imagination, and it is growing faster than ever. In such a grandiose setting, getting the right data, and analyzing it is insuperable. DMP both classifies and analyzes data for better segmentation practices.
What Really Happens When Data is Collected?
The foremost step is — organizing the data in a neat fashion. The chaos of data demands restructuring, which helps marketers to pick the right data as per the requirements.
Data is usually collected using user ID saved in the browser cookies, which categorically defines the user’s gender, likes and dislikes, and even deeper insights like browsing events, session timings, etc. As the user keeps visiting more websites, machine learning algorithms paint a more clearer picture of the user.
Once the data is collected, it performs the following functions:
- Breakdown: A number of parameters are taken into account while breaking down the data into several segments. While its hard to define those parameters, it is safe to say these parameters cater to all advertising campaigns
- Analysis: Data analysis is one of the indispensable aspects of online advertising efforts. It helps in fathoming the swarm of collected data and how they interconnect and influence each other. Data-driven decisions are futile without analysis
How DMP Works?
The collected and analyzed data is then sent to DSPs, SSPs, and ad exchanges to perform their respective functions.
DMPs, at first, assemble disorganized data from multiple devices and sources such as mobile phones, desktops, apps, websites, surveys, etc.
The collection of data results in individualized profiling based on a range of data points. DMPs then helps marketing entities by serving the data in the name of third-party data. Marketers utilize this data in figuring out who to target, where to target, and what necessary measures are worth considering.
The most crucial role of DMPs in the Adtech ecosystem is — acting like a pipeline, connecting components involved in the ecosystem, and making sure data is effectively used and utilized at the right place. The Adtech ecosystem rests on the bedrock of Artificial Intelligence and Machine Learning algorithms — thus, relevant data offered by DMPs not only helps Adtech to run smoothly, but it also provides an impetus to the entire process.
DMPs for Marketers
Data can move mountains for you.
Considering that you now understand what DMPs are and how they work, let me tell you how lucrative DMPs are, and as a marketer, why you should always count on them.
The way DMPs work is nothing short of an accurate model that provides you all the things you need. It is designed to make things in the advertising world seamless. And if your marketing game-plan has not taken DMPs seriously, it is time to take DMPs under your priority radar.
DMPs, time and again, have proved how well-orchestrated data can take your business to new heights. The advertising industry relies on data, and data relies on DMPs. It’s a perfectly tied chain where the role of DMPs is becoming more critical than ever.
DMPs illustrate data in a squeaky clean way; it tells marketers what advertising approach to take and how to reach there. It also provides insights into the content engagement prospect and ways to communicate with the audience effectively.
Building Audience Using DMP
Marketers, I hope now you realize the preponderance of DMPs and how they can impact your business massively.
You already know that DMP, at its core, collects and analyses the data. This rich set of data then dictates hierarchy — where several data points are arranged in a way that automatically translates into segments.
Data resides in separate systems; hence, constructing an integrated picture becomes challenging for marketers. DMPs procure data from first, second, and third parties, and builds an audience (look at it as users holding points) for spot-on targeting and personalization.
Types of Data DMP Collects
DMPs collect data from three sources — first-party, second-party, and third-party.
This type of data is usually collected from those users who have engaged or interacted with the brand. It is received right away from the user, underscoring its value.
This type of data is agreed upon by two companies. The data is collected in the form of first-party data and then sold to another company under an agreement. Hence it is called as a second-party data.
The data collected from multiple sources is known as third-party data. Agencies that collect data aggregate it and turn it into valuable segments, which is then sold to data seekers.
Challenges and Future
Data security is one of the most critical challenges faced by DMPs. DMPs’ only role is to collect, analyze, and offer data to the entities in need. Therefore, managing and keeping the data under wraps is the foremost challenge faced by DMPs. Data loss becomes a ridiculously outrageous cost for companies. Besides, the increasing complexity surrounding data is also acting as a hurdle for DMPs. Data is becoming valuable and complicated at the same time, making it hard for DMPs to handle it.
Talking about the future — it’s equivocal. As data privacy is turning into concern among IT biggies, DMPs are taking careful steps in collecting the data, and they are making sure that their data collection mechanism should not appear as a privacy infringement. So the future of DMPs seems like a detour of data collection approach. Things might change for DMPs in terms of how they collect, implement, break, manage, and organize data.
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