Blog Post:A data management platform (DMP) is an important part of an organization’s marketing strategy. We often talk about the lingo surrounding a DMP without diving in and explaining every term. Here I’ve outlined some of the basic concepts to help you build a foundation of understanding around data management platforms. DMP: A DMP, or data management platform is a tool used to consolidate the disperse data sets of a company across first, second, third-party channels. It manages the segmentation and identity definition of user to then provide cohesive audience targeting across all of the various channels where you might be engaging with a consumer. Forrester Research defined a DMP as “a unified technology platform that intakes disparate first-, second-, and third-party datasets, provides normalization and segmentation on that data, and allows a user to push the resulting segmentation into live interactive channel environments.” In other words, it pulls data from a bunch of different, potentially unrelated sources and allows organizations to define specific audience segments to which they can provide distinct marketing experiences. Why is a DMP important? A successfully implemented DMP is critical to the definition of a cohesive core dataset and enables different marketing teams to draw from it. This will ensure that end consumers have consistent and repeatable experiences across a multitude of various marketing channels. Without a DMP, communications sent to your end consumers through different channels — such as social, display, direct mail, and email, among others — become fragmented due to the varying degrees of personalization or segmentation needed to create a more unified experience.

1st, 2nd, & 3rd party data

Three types of data can be fed into a DMP: first-, second-, and third-party data. To develop a successful DMP, it is important to understand when each type of data should be utilized and what benefits each can offer to your organization. 1st Party Data: First-party data is proprietary data that has been captured by your company. This could be through channels such as onsite analytics audience information, the capture of various web behaviors in relation to specific experiences, or any offline first-party data such as customer-relationship management (CRM) data or data from a brick-and-mortar store. Why do you care? This is your most important asset, as it allows you to build a strong foundation from the vetted information that you have access to about your consumers that helps to establish a truthful baseline. In addition to traditional first-party data types, it is also important to consider more unique types of first-party data — such as survey information, media performance, or data that is not collected directly from traditional channels — as these information sources can provide valuable insights during the definition of that baseline. Once you have a strong grasp of what this first-party data looks like, you can pull in second- and third-party data to supplement your understanding of the customer base. It is also important that you are aware of the potential benefits and shortcomings of using other people’s data in your DMP. Generally, second-party data — a partner’s first-party data — is more reliable than third-party data. 2nd Party Data: Essentially, second-party data is a partner’s first-party data. Why do you care? Second-party data may be the greatest untapped resource in many DMPs today. Oftentimes, businesses already have broad contracts in place with existing copartners, allowing for the transfer and use of co-marketing and co-branding initiatives. Many times, those second-party data repositories are readily accessed and can be used to take a full inventory of partners and all their available datasets. Marketers can use second-party data to begin identifying really unique datasets that are meaningful to their businesses and much more defined and tailored to the consumers they want to attract than some of the more commoditized third-party data. Once you have established a solid customer base from your first-party data — and expanded your understanding of that base using second-party data available to you through partnerships or other directly shared customer data — it is time to take a step back and objectively define gaps in your data foundation. Are you missing specific demographic data or other types of information that could supplement your understanding of your broader customer base? Here, you can determine the best place to plug in third-party data to fill those specific areas where you lack enough of this data foundation. 3rd Party Data: Any data that you can get your hands on, that isn’t first- or second-party data, can be called third-party data. Third-party data is data that is gathered by businesses that typically sell customer data to other business, that is used to supplement gaps in first- and second-party data. One example of this type of data supplementation is prospecting initiative. When marketers are trying to find new customers who have never made purchases from them or their partners or visited their websites, there is no first- or second-party data available for those users. At this point, they can leverage third-party datasets to fill in the gaps and help to better define those audiences. Why do you care? Be aware of the potential downfalls of relying too heavily on third-party data. This information is mined by businesses that sell customer data to other businesses, and it may not be as reliable as first- and second-party data. Third-party data has become somewhat commoditized over the years, meaning that multiple businesses and industries have access to the same datasets. Relying too heavily on data that is not specific to your industry or customer base can mislead your overall marketing strategy or dilute the insights developed through your DMP.

DMP Strategy

Many businesspeople who understand the value of implementing a DMP — and what types of data can be integrated with it — wonder to what level the DMP should be used to define their overall marketing strategy or how they properly govern and operate a DMP. A DMP should not be used to define your strategy; the DMP is a tool to help you achieve your strategy. Building a comprehensive strategy is less about having a super-skillful data scientist who can run all sorts of algorithms and more about interacting with different groups that are working with data in one way or another and bringing all those ideas together to collectively define a single strategy for an organization. It is very challenging to incorporate all of the different requirements and use cases for each of the different groups utilizing the DMP within your organization, but that is how you really set a successful strategy. Once you have determined your strategy, and all those different groups are in agreement regarding what type of strategy it is, you can bring in the data scientists or algorithmic tools or Big Data analysis to capitalize on the scientific analysis of information; however, the analytics should not define the data strategy. The strategy should be defined across all the different parts of the organization that are working with data. Strategy is defined at the management level to identify what the different groups are doing and how these groups must work with data at different levels. From here, you can build a comprehensive strategy, and then use the DMP to actually execute that strategy, combining all the data to unify the message across the different channels. In the end, that is the true value of the DMP. It consolidates datasets. It manages identity at the master's level, pulls first-, second-, and third-party data — both online and offline — into the system, and effectively creates segments and subsegments of your audience that accurately reflect your customer base across a broad range of channels. The much more challenging aspect of a DMP strategy is determining what datasets you need to put into the DMPs. Which audiences should be prioritized across all those different marketing channels, and how will they impact existing marketing initiatives? These are the business decisions to be made as part of the strategy, as opposed to the technical decisions that can be made by the DMP.
Author: Date Created:February 10, 2016 Date Published: Headline:Digital Marketing 101: DMP Dictionary Social Counts: Keywords: Publisher:Adobe Image:https://blogs.adobe.com/digitalmarketing/wp-content/uploads/2016/02/AdobeStock_747948-e1454958207319.jpeg

A data management platform (DMP) is an important part of an organization’s marketing strategy. We often talk about the lingo surrounding a DMP without diving in and explaining every term. Here I’ve outlined some of the basic concepts to help you build a foundation of understanding around data management platforms.

DMP: A DMP, or data management platform is a tool used to consolidate the disperse data sets of a company across first, second, third-party channels. It manages the segmentation and identity definition of user to then provide cohesive audience targeting across all of the various channels where you might be engaging with a consumer.

Forrester Research defined a DMP as “a unified technology platform that intakes disparate first-, second-, and third-party datasets, provides normalization and segmentation on that data, and allows a user to push the resulting segmentation into live interactive channel environments.”

In other words, it pulls data from a bunch of different, potentially unrelated sources and allows organizations to define specific audience segments to which they can provide distinct marketing experiences.

Why is a DMP important?

A successfully implemented DMP is critical to the definition of a cohesive core dataset and enables different marketing teams to draw from it. This will ensure that end consumers have consistent and repeatable experiences across a multitude of various marketing channels. Without a DMP, communications sent to your end consumers through different channels — such as social, display, direct mail, and email, among others — become fragmented due to the varying degrees of personalization or segmentation needed to create a more unified experience.

1st, 2nd, & 3rd party data

Three types of data can be fed into a DMP: first-, second-, and third-party data. To develop a successful DMP, it is important to understand when each type of data should be utilized and what benefits each can offer to your organization.

1st Party Data: First-party data is proprietary data that has been captured by your company. This could be through channels such as onsite analytics audience information, the capture of various web behaviors in relation to specific experiences, or any offline first-party data such as customer-relationship management (CRM) data or data from a brick-and-mortar store.

Why do you care?

This is your most important asset, as it allows you to build a strong foundation from the vetted information that you have access to about your consumers that helps to establish a truthful baseline. In addition to traditional first-party data types, it is also important to consider more unique types of first-party data — such as survey information, media performance, or data that is not collected directly from traditional channels — as these information sources can provide valuable insights during the definition of that baseline.

Once you have a strong grasp of what this first-party data looks like, you can pull in second- and third-party data to supplement your understanding of the customer base. It is also important that you are aware of the potential benefits and shortcomings of using other people’s data in your DMP. Generally, second-party data — a partner’s first-party data — is more reliable than third-party data.

2nd Party Data: Essentially, second-party data is a partner’s first-party data.

Why do you care?

Second-party data may be the greatest untapped resource in many DMPs today. Oftentimes, businesses already have broad contracts in place with existing copartners, allowing for the transfer and use of co-marketing and co-branding initiatives. Many times, those second-party data repositories are readily accessed and can be used to take a full inventory of partners and all their available datasets. Marketers can use second-party data to begin identifying really unique datasets that are meaningful to their businesses and much more defined and tailored to the consumers they want to attract than some of the more commoditized third-party data.

Once you have established a solid customer base from your first-party data — and expanded your understanding of that base using second-party data available to you through partnerships or other directly shared customer data — it is time to take a step back and objectively define gaps in your data foundation. Are you missing specific demographic data or other types of information that could supplement your understanding of your broader customer base? Here, you can determine the best place to plug in third-party data to fill those specific areas where you lack enough of this data foundation.

3rd Party Data: Any data that you can get your hands on, that isn’t first- or second-party data, can be called third-party data. Third-party data is data that is gathered by businesses that typically sell customer data to other business, that is used to supplement gaps in first- and second-party data.

One example of this type of data supplementation is prospecting initiative. When marketers are trying to find new customers who have never made purchases from them or their partners or visited their websites, there is no first- or second-party data available for those users. At this point, they can leverage third-party datasets to fill in the gaps and help to better define those audiences.

Why do you care?

Be aware of the potential downfalls of relying too heavily on third-party data. This information is mined by businesses that sell customer data to other businesses, and it may not be as reliable as first- and second-party data. Third-party data has become somewhat commoditized over the years, meaning that multiple businesses and industries have access to the same datasets. Relying too heavily on data that is not specific to your industry or customer base can mislead your overall marketing strategy or dilute the insights developed through your DMP.

DMP Strategy

Many businesspeople who understand the value of implementing a DMP — and what types of data can be integrated with it — wonder to what level the DMP should be used to define their overall marketing strategy or how they properly govern and operate a DMP. A DMP should not be used to define your strategy; the DMP is a tool to help you achieve your strategy. Building a comprehensive strategy is less about having a super-skillful data scientist who can run all sorts of algorithms and more about interacting with different groups that are working with data in one way or another and bringing all those ideas together to collectively define a single strategy for an organization.

It is very challenging to incorporate all of the different requirements and use cases for each of the different groups utilizing the DMP within your organization, but that is how you really set a successful strategy. Once you have determined your strategy, and all those different groups are in agreement regarding what type of strategy it is, you can bring in the data scientists or algorithmic tools or Big Data analysis to capitalize on the scientific analysis of information; however, the analytics should not define the data strategy.

The strategy should be defined across all the different parts of the organization that are working with data. Strategy is defined at the management level to identify what the different groups are doing and how these groups must work with data at different levels. From here, you can build a comprehensive strategy, and then use the DMP to actually execute that strategy, combining all the data to unify the message across the different channels.

In the end, that is the true value of the DMP. It consolidates datasets. It manages identity at the master’s level, pulls first-, second-, and third-party data — both online and offline — into the system, and effectively creates segments and subsegments of your audience that accurately reflect your customer base across a broad range of channels. The much more challenging aspect of a DMP strategy is determining what datasets you need to put into the DMPs. Which audiences should be prioritized across all those different marketing channels, and how will they impact existing marketing initiatives? These are the business decisions to be made as part of the strategy, as opposed to the technical decisions that can be made by the DMP.