Blog Post:

In an increasingly complex programmatic world with view-ability, cookie deletion, cross device attribution and third party data enriched DSP’s to deal with, when you introduce a DMP to a marketer, they either shrug at the need of adding another layer to ad-tech, or just call it out as the last thing on that shopping list order of priority.

 And I don’t blame them, I read somewhere that for every dollar a marketer wants to spend, they are paying about 25-35 cents in being able to deliver that message. That’s creative services, DSP charges, attribution, ad verification, reporting all included. Add to that dynamic creative, a DMP, attribution and the cost goes up. And I haven’t even mentioned the pain of managing separate contracts. So while the industry is no where near consolidating around that problem, the campaign efficiencies you can drive by using your own data for re-targeting, audience extension and look-alike modeling based prospecting can deliver incremental ROI gains that pays up for the entire stack. As a Marketer, you are running campaigns to create buzz using attitudinal data, after creating that buzz communicating product features based off behavioral data, then following it up with retargeting to convert, and for the converts, execute cross sell/up sell campaigns. At each stage, you can use your own data to target, re-target, and extend audiences. You are sitting on a gold mine of the right first party data or second party data (someone else’s first party data). You can also buy data to fill in the gaps.

Segmentation Strategy

A segment strategy requires you to understand the source of the data (online signals or offline actions), the method of collection (first party, second party or third party), the process of creating a segment (Boolean rules based or algorithmic look alike) and understanding what the resulting segment attribute is (descriptive or predictive). Some segmentation strategies applicable to campaigns include :
  • Targeting Exclusions-If someone who was targeted based off attitudinal data using targeting data baked-in into a DSP or third party data in a DSP, and has seen a certain creative, then apply exclusions based on exposures or engagements with creative. Easy to start with, and paves the way for including more signals in that exclusion beyond just post impression campaign data, to add post click data. This is where you will see the most impactful gains in your campaigns, and finally stop wasting money on advertising to the same person. This also means you can run control /test campaigns based off attitudinal data using a research company to validate your digital campaigns and their potential buzz/lift. I am referring to the 'millward brown type' assisted campaigns.
  • CRM Based Targeting-You have the 'what' signals (what are users doing on your digital properties) and the 'who' signals (crm data containing age gender, NBO, churn Potential, loyalty status, product owned) that you now combine to create a segment that you can activate across programmatic display. You can even add a layer of geographical targeting, or in the mobile space geo-fencing data to make your targeting more specific to messaging based off location.
  • Frequency Capping-When building a segment, you can apply recency and frequency based filters. For example, a segment of men looking at a creative for Product A, exposed to a banner 3 times in the last 7 days. The advantage here is while DSP’s offer frequency capping across a campaign, you can apply frequency capping across multiple DSP’s, and even include channels beyond paid channels (paid display, on site personalization and email)
  • Cross Sell-Once you have a combined segment of behavioral (clickstream, products bought) and descriptive (lifestyle, education level, NBO, NPS Score) data, you will see segments qualifying for next best offer available for you to target at the right stage. This could be an output by the advanced analytics team that use product ownership and then applies market basket analysis to find associations between product ownership with high confidence and lift. This segment could now be targeted by executing a campaign via the DMP on your owned channels x(site /app/email) or a subtle targeting off site. This also means that you can now provide quants applying advanced statistical techniques on all offline data, a faster way to go to market in targeting those segments. This also gives them a way to learn quickly from the success of their targeting strategies and make changes to the recommendations coming in. In simple words the traditional market basket analysis now has a faster go to market on digital channels with a faster learning cycle, and the additional ability to add more data points beyond just product ownership when executing on models delivering NBO.
  • Retargeting-This is pretty straightforward, especially if you are a Travel and Hospitality, Retail or FMCG play, where you might have experienced this yourself where you searched for a sector, a destination or a hotel property only to see those ads follow you on the internet.
  But rather than just doing a simple re targeting based off the current search, you can apply a layer of signals you have on that person to change your re-targeting strategy to have segment based re-targeting where offers could change by loyalty status for instance. Rather than a simple “because you searched for this sector here is a message reminding you to book it” its now adding a layer of past purchases.
  • Web Behavioral Targeting- This leans towards building segments combining either data that’s coming in from a third party data provider which is tied to behavioral or predictive signals, or its based on signals from your own clickstream. The resulting segment could either be descriptive or predictive depending on campaign strategies that you want to use to target individuals based on what they have done in the past, or what the intention and hence the future behavior would be (In market automobile enthusiast because they looked at car reviews)
  • Dynamic Creative- Dynamic creative is a topic in itself with some simple offerings that allow users to upload a file containing parameters you want to change (Fare, Sector, Location, Price) and create multiple creatives at a low cost (Which is the main advantage here), to others which allow for real time decisioning to render a creative based off data coming in and a segment qualifying for some pre determined logic. The ability to make mid campaign changes, and change some parameters mid flight is what differentiates a true dynamic creative solution from a pretender.
The Combination of a Data Management platform powering segments that are tied to templates in a Dynamic creative solution, and also providing the real time data that is driving that decisioning rather than the need for separate retargeting tags, allows for the DMP to deliver incremental value, where although the person falls in a re targeting segment based on the on-site behavior, they are also then helping test and learn from thousands of creatives. These are creatives that are not just changing sector and price but perhaps copy, creative image, button color etc, and also serving as inbound data source for the DMP to qualify or disqualify segments after having been exposed to certain creatives, which paves the way for sequential messaging.
  • Sequential Messaging: As the name suggests the idea here is to target a segment by using the ad impression data as an inbound data source to change the targeting message after a certain amount of ad exposures. This helps with campaign efficiencies by attempting to avoid campaign fatigue and taking the prospect through a sequence of messages which could be changes based off number of exposures and more importantly, across devices. And these can be changed mid campaign.
  Final Thoughts A DMP powered campaign strategy can bring in efficiencies as programmatic ad spend  increases, and has the ability to help marketers focus on driving loyalty and customer lifetime value. 2016 is the year the marketing stack and the ad tech stack comes together and the DMP sits right in the middle of your marketing strategy making that possible. So understand campaign strategies, understand the use of descriptive versus predictive signals, empower your traditional quants, and get set for never before campaign efficiencies.
Author: Date Created:March 11, 2016 Date Published: Headline:“I’ve got a DMP, now what?” -Segmentation strategies in a data management platform world Social Counts: Keywords: Publisher:Adobe Image:https://blogs.adobe.com/digitalmarketing/wp-content/uploads/2016/03/AdobeStock_79035983-e1457550282940.jpeg

In an increasingly complex programmatic world with view-ability, cookie deletion, cross device attribution and third party data enriched DSP’s to deal with, when you introduce a DMP to a marketer, they either shrug at the need of adding another layer to ad-tech, or just call it out as the last thing on that shopping list order of priority.

 And I don’t blame them, I read somewhere that for every dollar a marketer wants to spend, they are paying about 25-35 cents in being able to deliver that message. That’s creative services, DSP charges, attribution, ad verification, reporting all included. Add to that dynamic creative, a DMP, attribution and the cost goes up. And I haven’t even mentioned the pain of managing separate contracts.

So while the industry is no where near consolidating around that problem, the campaign efficiencies you can drive by using your own data for re-targeting, audience extension and look-alike modeling based prospecting can deliver incremental ROI gains that pays up for the entire stack.

As a Marketer, you are running campaigns to create buzz using attitudinal data, after creating that buzz communicating product features based off behavioral data, then following it up with retargeting to convert, and for the converts, execute cross sell/up sell campaigns.

At each stage, you can use your own data to target, re-target, and extend audiences. You are sitting on a gold mine of the right first party data or second party data (someone else’s first party data). You can also buy data to fill in the gaps.

Segmentation Strategy

A segment strategy requires you to understand the source of the data (online signals or offline actions), the method of collection (first party, second party or third party), the process of creating a segment (Boolean rules based or algorithmic look alike) and understanding what the resulting segment attribute is (descriptive or predictive).


Some segmentation strategies applicable to campaigns include :

  • Targeting Exclusions-If someone who was targeted based off attitudinal data using targeting data baked-in into a DSP or third party data in a DSP, and has seen a certain creative, then apply exclusions based on exposures or engagements with creative. Easy to start with, and paves the way for including more signals in that exclusion beyond just post impression campaign data, to add post click data. This is where you will see the most impactful gains in your campaigns, and finally stop wasting money on advertising to the same person. This also means you can run control /test campaigns based off attitudinal data using a research company to validate your digital campaigns and their potential buzz/lift. I am referring to the ‘millward brown type’ assisted campaigns.

  • CRM Based Targeting-You have the ‘what‘ signals (what are users doing on your digital properties) and the ‘who‘ signals (crm data containing age gender, NBO, churn Potential, loyalty status, product owned) that you now combine to create a segment that you can activate across programmatic display. You can even add a layer of geographical targeting, or in the mobile space geo-fencing data to make your targeting more specific to messaging based off location.

  • Frequency Capping-When building a segment, you can apply recency and frequency based filters. For example, a segment of men looking at a creative for Product A, exposed to a banner 3 times in the last 7 days. The advantage here is while DSP’s offer frequency capping across a campaign, you can apply frequency capping across multiple DSP’s, and even include channels beyond paid channels (paid display, on site personalization and email)

  • Cross Sell-Once you have a combined segment of behavioral (clickstream, products bought) and descriptive (lifestyle, education level, NBO, NPS Score) data, you will see segments qualifying for next best offer available for you to target at the right stage. This could be an output by the advanced analytics team that use product ownership and then applies market basket analysis to find associations between product ownership with high confidence and lift. This segment could now be targeted by executing a campaign via the DMP on your owned channels x(site /app/email) or a subtle targeting off site. This also means that you can now provide quants applying advanced statistical techniques on all offline data, a faster way to go to market in targeting those segments. This also gives them a way to learn quickly from the success of their targeting strategies and make changes to the recommendations coming in. In simple words the traditional market basket analysis now has a faster go to market on digital channels with a faster learning cycle, and the additional ability to add more data points beyond just product ownership when executing on models delivering NBO.

  • Retargeting-This is pretty straightforward, especially if you are a Travel and Hospitality, Retail or FMCG play, where you might have experienced this yourself where you searched for a sector, a destination or a hotel property only to see those ads follow you on the internet.

 

But rather than just doing a simple re targeting based off the current search, you can apply a layer of signals you have on that person to change your re-targeting strategy to have segment based re-targeting where offers could change by loyalty status for instance. Rather than a simple “because you searched for this sector here is a message reminding you to book it” its now adding a layer of past purchases.

  • Web Behavioral Targeting– This leans towards building segments combining either data that’s coming in from a third party data provider which is tied to behavioral or predictive signals, or its based on signals from your own clickstream. The resulting segment could either be descriptive or predictive depending on campaign strategies that you want to use to target individuals based on what they have done in the past, or what the intention and hence the future behavior would be (In market automobile enthusiast because they looked at car reviews)

  • Dynamic Creative- Dynamic creative is a topic in itself with some simple offerings that allow users to upload a file containing parameters you want to change (Fare, Sector, Location, Price) and create multiple creatives at a low cost (Which is the main advantage here), to others which allow for real time decisioning to render a creative based off data coming in and a segment qualifying for some pre determined logic. The ability to make mid campaign changes, and change some parameters mid flight is what differentiates a true dynamic creative solution from a pretender.

The Combination of a Data Management platform powering segments that are tied to templates in a Dynamic creative solution, and also providing the real time data that is driving that decisioning rather than the need for separate retargeting tags, allows for the DMP to deliver incremental value, where although the person falls in a re targeting segment based on the on-site behavior, they are also then helping test and learn from thousands of creatives. These are creatives that are not just changing sector and price but perhaps copy, creative image, button color etc, and also serving as inbound data source for the DMP to qualify or disqualify segments after having been exposed to certain creatives, which paves the way for sequential messaging.

  • Sequential Messaging: As the name suggests the idea here is to target a segment by using the ad impression data as an inbound data source to change the targeting message after a certain amount of ad exposures. This helps with campaign efficiencies by attempting to avoid campaign fatigue and taking the prospect through a sequence of messages which could be changes based off number of exposures and more importantly, across devices. And these can be changed mid campaign.

 

Final Thoughts

A DMP powered campaign strategy can bring in efficiencies as programmatic ad spend  increases, and has the ability to help marketers focus on driving loyalty and customer lifetime value. 2016 is the year the marketing stack and the ad tech stack comes together and the DMP sits right in the middle of your marketing strategy making that possible.

So understand campaign strategies, understand the use of descriptive versus predictive signals, empower your traditional quants, and get set for never before campaign efficiencies.