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Paid Media Activation: CDP Use Case

Organizations today looking to expand their brand reach and increase website traffic and clicks often turn to paid media, which involves purchasing ad space on one or more media channels–search engines, blogs, social media sites, etc.–and using that space to promote content or broadcast messages relevant to a specific audience.

Paid media can be an effective marketing strategy; the challenge with it, however, is maintaining the balance between conversion volume and cost efficiency. Using BlueConic to address the logistical considerations for paid media activation can help you maximize your reach without compromising the efficiency of your marketing investment.

Value-Based Outcome

All BlueConic use cases are associated with one or more Value-Based Outcomes, which are defined areas of focus that help you measure the value of a CDP to your overall business. The paid media use case can be closely related to the Value-Based Outcomes of Smarter Customer Engagement and Increased Agility and Flexibility.

Before you begin

Before you begin implementing paid media activation, make sure you are familiar with what a CDP use case is and all of the components that will make yours more successful.

Then, start defining your “Before You Begin” requirements, which includes identifying key stakeholders and target audiences, gathering data sources and credentials, and defining what success looks like. For this use case, your starting requirements may look something like this:

  • Key stakeholders:
    • Project lead: The person who owns project planning and ensures that the use case gets implemented.
    • BlueConic power users: The experts on your team responsible for setting up the use case in BlueConic.
    • Marketing stakeholders: Those with marketing expertise devoted to individual paid media channels.
    • Business intelligence/analytics: Those responsible for use case measurement and reporting.
  • Target audience: Those you intend to reach through this paid media activation, which is different for each organization and specific to your industry and the product/service you offer.
  • Platforms you might use:
  • Measurement and ROI:
    • Cost per click
    • Number of audiences pushed
    • Size of audiences pushed
    • Return on Ad Spend (ROAS)
    • Conversion rate

Use case discovery

To help build out your use case, you can also start your own use case discovery process by answering some of these detailed questions about paid media:


  1. Objective
    • What outcome are you looking to achieve with this program?
    • Does this paid media use case ladder up to broader objectives or a larger roadmap for the business?
    • Are you currently reaching these audiences today? How are you targeting these audiences, and what processes are currently in place that you’re trying to improve upon?
  2. Target Audience
    • What audience do you intend to reach through this paid media activation?
    • How does a user qualify for this audience, in business terms?
    • Is there any research or persona work informing the target audience?
  3. Existing Customer Data
    • Consent
      • What consent information is used today to enable targeting of users on paid media platforms?
      • How is this information gathered, and how is it stored?
      • Are there any legislation zone specific considerations (e.g., CCPA/CPRA)?
      • If a user provides no information regarding cookie consent, how does the brand treat this user (e.g., implicit opt-in, implicit opt-out)?
    • Purchase History
      • Is purchase history currently something that the brand utilizes for paid media activation?
      • What is the source of truth, how far does it go back, and how often is it refreshed?
      • What are the features available for this data?
    • Onsite
      • What information is currently utilized from onsite behavior for your paid media audiences?
      • What are the features available for this data?
    • Third-Party Data Assets
      • Are there any third-party data assets in use that need to be considered (Experian/IRI, Demandbase)?
    • Exclusions
      • What data sources are in use today to manage audience exclusions?
      • How are these attributes applied?
  4. Customer Data Gaps
    • Consent
      • Has your legal team advised you on this topic?
      • Are there any other consent/opt-in data sources that need to be considered?
      • How are opt-outs managed?
    • Onsite
      • Do you currently associate onsite browsing behavior with offline purchase behavior?
      • What identifiers are available to enable profile merging?
    • Purchase
      • What identifiers are available to enable profile merging? Do these match with what is available onsite?
    • Exclusions
      • Are there other signals or user attributes that should be considered for exclusion?
  5. Segment Definitions
    • In specific terms, how would you propose building this segment?
    • What profile properties and associated values will be used to build the segments?
  6. Marketing Program Scope
    • Do you currently work with any external teams to manage your paid media activations?
    • Do you currently work with any external or internal teams to manage reporting for these efforts?
    • Has your legal team advised you on this topic?
  7. Measurement
    • How do you report on your paid media campaigns and at what cadence?
    • What internal stakeholders are involved when it comes to performance reviews?
    • How do you intend to measure the performance of this use case?
    • How are paid media tactics typically measured?
    • What is the primary KPI?
    • What are the diagnostic KPIs?
  8. Activation
    • To which external platforms will these users need to be exported?
    • How frequently will these users need to be exported to external platforms?
    • How frequently will these users be targeted?
    • How will frequency be managed?
    • What messaging will be delivered to these users?
    • Who has access to the credentials of the ad platform?

Getting started

Paid media activation through BlueConic involves exporting segment data and profile information from BlueConic to specific media platforms (e.g., Google Ads) to reach your targeted audience (and exclude segments of visitors who are not a good fit for your ad campaigns).

As you get started, you will need to address how you will capture consent for those receiving your advertising. Consent is becoming increasingly critical for both U.S. and EU brands with GDPR, CCPA/CPRA, and other relevant legislation. In BlueConic, you use Objectives to acquire consent data; this is discussed more in the first step below.

Tip: Contact BlueConic Support to determine how you can use the OneTrust Privacy Management Listener to sync OneTrust consent goals with BlueConic profiles.

Use case configuration

To execute this use case, you will follow these basic steps, which will vary depending on your specific goals and resources:

  1. Set up legislation zones and create a consent objective
  2. Create a BlueConic export connection and associate it with your objective
  3. Import an online identifier through the data layer (or other source)
  4. Configure an Interest Ranker 2.0 Listener
  5. Import order data through a BlueConic import connection
  6. Build Timeline event rollups
  7. Create your segment
  8. Export your data

We will demonstrate each of these steps in the sections below using a common paid media use case in which a retail brand, Taylor Store, intends to use Google display ads to target users interested in the product category “Jeans.” For this example:

  • The retailer’s goal is to increase the conversion volume generated by its current Google Ads audiences while also maintaining its blended Cost Per Acquisition target.
  • Its target audience is users who have expressed interest in the product category “Jeans” over the last 30 days but have not made a purchase in the last year.
  • The retailer confirmed with its legal team that the following consent data flow must be accounted for:
    • Users in California (U.S.) must explicitly opt-in to receive these digital ads.
    • Other users in the U.S. do not have to opt-in but must not explicitly opt-out of receiving these digital ads.
  • The retailer will target these users with a specific promotion for this product category using banner ads across the Google Display Network.

Step 1: Set up legislation zones and create a consent objective

You will need to begin this use case by assessing available consent data. First, navigate to the Privacy page (BlueConic settings > Privacy) to determine which legislation zones you want available for selection in your BlueConic Objectives. Configure the opt-in and opt-out designations for all zones you check on this page:

  • Opt-in: A visitor within an Opt-in zone meets the objective only if they consent to that objective.
  • Opt-out: A visitor within an Opt-out zone meets the objective in all situations unless they explicitly refuse consent.

Then, create a BlueConic Objective (More > Objectives) and select the legislation zones for which you need consent.

Taylor Store Example

Taylor Store ensures that two zones are configured appropriately on the Privacy page and then creates an objective named “Display ads” with those two zones checked:

  • “U.S. - California (CCPA/CCRA)” set to Opt-in.
  • “Rest of the World” set to Opt-out.

Step 2: Create a BlueConic export connection and associate it with your objective

Next, you will need to create a BlueConic Connection for the paid media channel where you want to export your data. (BlueConic has a variety of popular options for paid media, including Google Ads Customer Match, Facebook Advertising, and The Trade Desk.) You’ll then need to associate that connection with the objective you just added. You can do this right from your connection by opening the sidebar at the far right, clicking Add objective, and selecting your objective from the gallery.


Note: You do not have to fully configure the connection at this stage; you will do this in a later step.

Taylor Store Example

Taylor Store creates a Google Ads Customer Match Connection and associates it with its “Display ads” objective.

Step 3: Import an online identifier through the data layer (or other source)

Next, you will need to bring together onsite behaviors with offline purchase data. To do this, import a common identifier so the platform can match existing profiles or merge profiles together.

Typically, when a user is logged in on a retailer’s website, several identifiers are exposed via the data layer. As a result, you’ll want to create a Data Layer Connection in BlueConic. Add an import goal for that connection, and in step 2, leverage the visual picker to explore all the variables present in the data layer as you navigate the website experience.


Then, in that same step, assign your identifier to a profile property. (If you need to create a new property, make sure it is set as a unique identifier.)

Tip: For the cleanest data, create profile merging rules to ensure that profiles containing the same value for that property are merged.


Taylor Store Example

For Taylor Store, a value for encrypted_id is made available once a user logs in, which represents hashed email addresses. So in step 2 of the Data Layer Connection, encrypted_id is mapped to a profile property named Hashed Email.

Step 4: Configure an Interest Ranker 2.0 Listener

Your website should have tags somewhere in each article to indicate the categories it is associated with. Therefore, configure the following steps in an Interest Ranker 2.0 Listener:


  1. Keep it set to listen on all pages of your website. (You can always reduce the places where the listener runs after you’ve reviewed what content is collected.)
  2. Bypass this second step to detect or exclude interests, unless there are specific interests you want to capture or remove.
  3. Click the Add capturing method button to select one or more methods for detecting customer interests. For simplicity, add one point for each interest upon page load.


  1. Set up a few profile properties to store these customer interests. For example, you can store the top 1, 5, and 10 interests with a decay rate of X days. Setting up separate top interest profile properties allows you to create and evaluate segments based on how interested someone is in a topic.

Taylor Store Example

Taylor Store uses the "Meta tags" capturing method and enters a tag for “product_category." To target users interested in Jeans over the last 30 days, at least one profile property is configured with a 30-day decay rate.

Step 5: Import order data through a BlueConic import connection

From here, you will need to import order data and ensure that profiles are being properly merged. BlueConic has a variety of connections available for data import, but most retailers use the SFTP Connection.

Tip: Typically, when importing, you’ll need to account for three file types:

  • Customer level file
  • Order level file
  • Product level file

Note: Before importing the required data–since order data is event-level data, meaning the Timeline is required–you should open the “Order” Timeline event type in BlueConic (More > Timeline events > Timeline event types) and make any required mapping adjustments to ensure the structure matches the data you anticipate importing.


With the required mapping adjustments made, follow these steps for your SFTP import:

  1. Bypass this first step to select a BlueConic domain group for the import.
  2. Navigate to the directory in which the three file types are contained, select each file, and use the link icon to specify the linking identifier across your files.
  3. Add any data processor to correct your data.
  4. Define file and field handling.
  5. Select the link identifier that will be used to match to existing profiles or serve as an identifier for a newly generated profile.
  6. Map any relevant properties at the profile level.
  7. Import Timeline event data. (This will be the information that corresponds to the Timeline event mapping conducted earlier.)
  8. Run your connection by going to the Set up and run page (using the cogwheel icon to select a run cadence). For this type of data import, running the connection every 24 hours is advised.

Taylor Store Example

Taylor Store follows the steps as such:

  1. ---
  2. The links will be as follows:
    • Customer→ Order: Customer_ID (example)
    • Order → Product: SKU (example)
  3. Taylor Store wants to merge offline and online profiles, so it needs to hash email addresses using SHA256 encryption. As a result, the Hash values data processor will be added, with the field of interest set to “EmailAddress,” the hash function set to SHA256, and the target field name set to “Hashed Email.”
  4. ---
  5. Taylor Store uses “email address” for the link identifer, ensuring that “Allow the creation of new profiles in BlueConic” is checked so that a new profile is created whenever an email value from the imported files is not matched to an existing profile.
  6. Taylor Store maps these properties:
    • Order Date (all)
    • Order Date (most recent)
    • Order Date (first)
    • Store ID (most recent)
    • Store ID (all)
    • Ordered from Division
    • Ordered From Department
    • Ordered from Product Category
  7. Taylor Store maps these critical values and BlueConic fields:
    • Transaction date →  Event Date (BC)
    • Transaction ID → Event ID (BC)
    • Product_category → Product Category (BC)
  8. ---

Step 6: Build Timeline event rollups

Now that you have imported order data to the profile Timeline, you need to build profile properties that “roll up” values from the Timeline to the profile property level. For instance, a profile Timeline for a retailer may contain 30 instances of a particular SKU purchase in the last five years, so that information would need to be made available through the Timeline Query Connection or the Timeline event rollups feature (More > Timeline events > Timeline event rollups).

Taylor Store Example

Taylor Store chooses to use the Timeline Query Connection. It adds a new goal, completing the following sections: 

  1. The retailer selects a segment it set up of users who have an email address with an Order Date in the last 365 days.
  2. To reduce connection runtime, the retailer inputs “Order Date (most recent)” for the date property filter and 365 days for the event filter.
  3. Taylor Store constructs the query for Jeans purchases in the last 365 days and writes this to a new profile property for “Last Jeans Purchase Date.”

Step 7: Create your segment

With your Timeline data and onsite behavioral data in place, you can now generate your segments using the Segments tool (accessible by clicking Segments from the BlueConic navigation menu).

Taylor Store Example

Taylor Store creates a segment of profiles who have demonstrated interest in the Jeans category onsite (via the Interest Ranker 2.0 configuration) but not made a purchase in the last 30 days (via the imported order data).

To do this, it creates a new segment from the Segments page with the following profile property conditions:

[The Interest Ranker 2.0 profile property from Step 4 above]
contains any of (default) “Jeans”


Order date (most recent)
NOT IN RANGE Within last 30 calendar days (UTC)

Step 8: Export your data

Return to the export connection you created in step 2, complete setup (including mapping the BlueConic segment you just created to a custom audience in the ad platform), and run the connection to send your data to the appropriate platform.

When completing setup for your export connection, there are a few important considerations:

  • Associated segments in connection mapping
    • With whatever connection you use, you’ll need to specify how your segment data is exported to your destination; you’ll be prompted to select your segment of interest, and map it to the corresponding audience name in your platform of choice. For real-time export goals, you have the ability to export “all associated segments”’ or “only selected segments” for a given base segment, depending on how you’d like this information to be passed to the platform.
  • Match rates and initial audience size
    • When BlueConic matches via link identifier (e.g., email address) to a platform like Google Ads, that platform will attempt to match BlueConic values to its own identity graph; as such, it is normal to experience a drop-off in audience size (often referred to as platform “match rates”). Because of this, it’s important to consider your initial audience size.
    • For instance, if your initial segment contains 100,000 profiles, that audience will be more targetable than a segment of 10,000.

Note: With The Trade Desk, PII is not directly supported for matching. As a result, when setting up batch exports to The Trade Desk, you might consider using cookie sync functionality from The Trade Desk Connection or going with the Unified ID 2.0 Connection. The former allows you to store The Trade Desk IDs in the unified profile when a visitor is onsite; the latter allows you to obtain Unified ID 2.0 values from The Trade Desk for offline profiles, using PII.

Taylor Store Example

Taylor Store exports data through the Google Ads Customer Match Connection, making sure to complete two mandatory configuration steps:

  • Establish a communication channel between Google and BlueConic by authenticating on the Set up and run page.
  • Signify what accounts need to be selected. (Once you authenticate, when you create an export goal, you’ll immediately be presented with a list of detected Google accounts in step 1.)

Next steps

Once your ad campaigns are running, measure your success by examining metrics such as customer acquisition cost and customer lifetime value using BlueConic Insights. You can then make any pivots or adjustments to your use case as necessary and even introduce new strategies (such as managing a formal suppression list to avoid targeting those unlikely to convert).

When ready, you can also move on to other CDP use cases for retail to deepen and measure engagement, including:

  • Smarter product recommendations: Showing personalized and relevant product recommendations on your website based on past purchases and/or browsing history and behaviors. For more, review the article Deliver 1:1 Product Recommendations.
  • Deeper analysis of cart abandonment: Tracking your online shoppers’ browsing behavior to lessen instances where they add products to their carts but do not complete the purchase. For more, review the article Minimize Cart Abandonment.

Retail use case resources

Case studies



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