11 Best Cookieless Advertising Solutions for B2B Marketing

Navigating the Future Without Third-Party Cookies

For years, third-party cookies have been central to programmatic advertising, enabling advertisers to deliver targeted ads, track user behavior, and measure campaign success.

Everything was fair and square on the surface, except that users’ data were collected, stored, and shared without their consent.

Now this violation of users’ privacy has led to growing concerns, prompting regulations like GDPR and CCPA, and most recently, Google, phasing out these cookies.

The Impact on B2B Marketers

First, let’s roll out the stats:

• 75% of marketers rely heavily on third-party cookies.
• 41% of businesses expect a negative impact from losing third-party cookies, with 16% predicting it will “devastate” their marketing.

Now to the challenges:

• Attribution becomes difficult. Determining which touchpoints lead to conversions is harder without tracking cookies.
• Content personalization suffers. Tailoring content to different decision-makers becomes less effective when behavioral data is not readily available.
• Retargeting is less precise. Without cookies, it’s more difficult to engage users who have interacted with the brand, leading to a potential drop in conversions.

With all of these, it’s evident that the deprecation of third-party cookies will disrupt traditional tracking and targeting methods that many businesses have relied on for years.

The Solution: A Cookieless Future

The transition to a cookieless world is challenging, but it also presents new opportunities for B2B marketers to embrace alternative strategies.

By shifting towards cookieless solutions, businesses can maintain relevance and continue delivering the personalized experiences that buyers expect.

Top Cookieless Advertising Solutions B2B Marketers Should Consider

1. First-Party Data

First-party data refers to information that a company directly collects from its own customers or audience.

This data can come from various interactions, such as website visits, app usage, customer transactions, surveys, CRM systems, or other customer interactions.

How It Works

• Data Collection. When users interact with your content (e.g., filling out a form, subscribing to newsletters, purchasing products), the data is captured and stored in systems like CRM software or Data Management Platforms (DMP).
• Data Organization. The data collected is then organized and categorized, allowing for easy access and utilization in marketing campaigns. This can include user segmentation, tracking buyer journeys, and more.
• Data Activation. Once data is organized, marketers can use this information to create personalized marketing campaigns, retargeting, or better understand customer behaviors to optimize marketing strategies.

How to Use First-Party Data in Your Marketing Strategy

• Personalized Campaigns. Use the data to tailor personalized experiences for your audience. For example, Demandbase makes it easy to get more from your first-party data. It helps you connect different data sources like CRM and social platforms, creating a complete view of your customers. You can then use this data to run highly targeted campaigns and reach new audiences that look like your best customers. Check it out.
• Segmentation. Divide your audience based on behaviors, interests, or demographics, and deliver content that resonates with each group.
• Retargeting. Since first-party data captures user behavior on your platform, it allows for highly effective retargeting, encouraging prospects to take the next step in the buyer journey. For example, target users who abandoned their carts with special discounts or reminders.
• Lookalike Audiences. Use first-party data to create lookalike audiences on advertising platforms, expanding your reach to similar potential customers who share traits with your current audience.
• Enhanced Customer Experience. Leverage the data to improve customer journeys by providing more relevant content and interactions, ultimately fostering loyalty and trust.

Pros

• High Accuracy and Relevance. The data comes directly from your users, meaning it’s more accurate and relevant compared to third-party data.
• Privacy-Compliant. Customers have consented to provide their information, making it more compliant with privacy laws (like GDPR and CCPA).
• Better Personalization. With direct data, you can create more personalized and targeted marketing efforts, improving user experience and conversion rates.
• Cost-Efficiency. Since first-party data is collected in-house, there are fewer expenses associated with acquiring data, making it a cost-effective solution in the long run.

Cons

• Limited Data Reach. First-party data is limited to your direct interactions with users, meaning you won’t have access to data from users who haven’t visited your website or engaged with your brand yet.
• Data Silos. If your collection methods are not well-structured, you could face issues with incomplete, outdated, or inaccurate data, affecting your marketing outcomes.
• Requires Infrastructure. Collecting, organizing, and managing first-party data requires investment in data infrastructure and tools, such as CRMs, DMPs, or CDPs (Customer Data Platforms).

Did you know? Demandbase makes it easy to get more from your first-party data. It helps you connect different data sources like CRM and marketing automation platforms, creating a complete view of your customers.

You can then use this data to run highly targeted campaigns and reach new audiences that look like your best customers.

2. Contextual Advertising

Contextual advertising is a form of digital advertising that targets users based on the content they are consuming rather than their personal data or browsing history.

Here, ads are placed on relevant websites or alongside content that matches the keywords or topics a user is currently reading.

Unlike behaviorally targeted ads, which rely on cookies to track users across the web, contextual advertising focuses on aligning the ad content with the surrounding content.

How It Works

• Content Analysis. An algorithm scans and analyzes the content on a webpage, identifying keywords, phrases, and topics.
• Ad Matching. Once the algorithm understands the context of the page, it matches relevant ads to appear alongside the content. For instance, if a user is reading an article about marketing strategies, an ad for marketing software or services might be shown.
• Ad Placement. The ads are placed within the webpage in areas like banners, sidebars, or in-article placements. Since the ads are highly relevant to the user’s current interest, they tend to achieve better engagement.

How to Use Contextual Advertising in Your Marketing Strategy

• Keyword Targeting. Identify relevant keywords and phrases that align with your product or service offering. When these keywords are present in the content users are consuming, your ads can appear next to them.
• Topic Targeting. Focus on broader topic categories related to your business. For example, if your company offers B2B SaaS solutions, you could target content on business technology, or productivity.
• Contextual Networks. Leverage ad platforms like Google Display Network or third-party networks that specialize in contextual advertising. These platforms analyze site content and automatically place your ads in relevant environments.
• Dynamic Creatives. Use dynamic ads that adjust based on the context they’re displayed in. This helps create a more personalized and engaging experience for users based on the content they’re reading.

Pros

• Privacy-First Approach. Contextual advertising doesn’t rely on tracking cookies, making it compliant with privacy regulations like GDPR and CCPA. It’s a solution that aligns with the cookieless approach.
• Highly Relevant Ad Placements. Since the ads are placed in a relevant content environment, they’re more likely to resonate with users and achieve higher engagement rates.
• Brand Safety. You can control where your ads appear, ensuring that they’re only shown on content that aligns with your brand values and industry.
• Increased Engagement. Ads that match the context of the page have a higher chance of grabbing the user’s attention, as they’re directly related to the content being consumed.

Cons

• Limited Personalization. Since contextual advertising doesn’t consider individual user behavior or preferences, it lacks the level of personalization that cookie-based ads provide.
• Lower Granularity. While it can target broad topics and keywords, it doesn’t offer the same level of precise targeting that behavioral advertising does, potentially leading to less granular audience segmentation.
• Dependence on Content Quality. The success of contextual ads depends on the quality and relevance of the content it’s placed next to. If the algorithm misinterprets the content, the ad may appear in irrelevant contexts.

DB Pro Insights → Use DemandBase’s real-time insights to ensure your ads reach the right decision-makers. With its precise targeting, you can align your ads with what B2B buyers are actively looking for, driving better engagement and more conversions.

Launch a B2B Advertising Campaign for Free

3. Universal IDs (Shared IDs)

Universal IDs, also known as shared IDs, are assigned identifiers that work across different domains and platforms, allowing advertisers and marketers to recognize users without relying on third-party cookies.

How It Works

Universal IDs typically work by assigning a unique, anonymized identifier to a user when they interact with a digital property (such as visiting a website or using an app).

Here’s a detailed process:

• User Consent. Universal IDs are often created when users opt-in by providing consent for data collection in compliance with privacy laws such as GDPR or CCPA.
• ID Assignment. Once consent is given, a universal ID is assigned to the user, either based on their email address, login data, or first-party data. This ID is encrypted and shared between different platforms and domains.
• Cross-Platform Recognition. The universal ID can be recognized across participating platforms, enabling advertisers to track users’ journeys across websites without using third-party cookies. This allows marketers to continue measuring performance, frequency capping, and retargeting.

How to Use Universal IDs in Your Marketing Strategy

• Partner with Reliable Identity Providers. To implement Universal IDs, you need to work with providers that offer shared ID solutions.
• Audience Targeting. Use Universal IDs to improve your audience targeting by connecting user data across multiple platforms. This ensures that your ads reach the right people at the right time, even without third-party cookies.
• Frequency Capping. Implement frequency capping by using Universal IDs to control how often users see your ads across various platforms, preventing ad fatigue and optimizing the user experience.
• Retargeting. Universal IDs enable effective retargeting strategies, allowing you to re-engage visitors who have previously interacted with your website or content, even as they move across different websites.
• Attribution. Marketing attribution platforms can track user interactions across platforms, Universal IDs help improve attribution models, giving marketers better insights into the user journey and campaign performance.

Pros

• Privacy-Compliant. Universal IDs are designed to comply with privacy regulations like GDPR and CCPA, ensuring that user data is protected while still allowing for effective marketing strategies.
• Cross-Platform Targeting. They allow for continued cross-platform and cross-device tracking, enabling marketers to recognize users across multiple touchpoints without relying on third-party cookies.
• Improved Accuracy. Universal IDs can offer more accurate tracking than cookies, especially when they are based on deterministic data (e.g., user logins or email addresses) rather than probabilistic models.
• Ad Personalization. The assigned user ID acts as a persistent identifier across the web, helping marketers deliver more relevant content to their target audiences.

Cons

• Limited Adoption. Universal ID solutions require widespread adoption among publishers, advertisers, and platforms to be fully effective. The lack of standardization across the industry could limit their effectiveness.
• User Consent Needed. Since Universal IDs rely on user consent and first-party data, they may not be as effective if users opt out of data tracking or refuse to provide consent.
• Implementation Complexity. Integrating Universal IDs into existing marketing systems can be complex and may require partnerships with identity providers or additional technological infrastructure.
• Dependence on Login Systems. Some Universal ID systems are heavily dependent on login data (e.g., email addresses). If users do not log in or create accounts, it can be challenging to assign a universal ID.

DB Pro Insights → When integrating Universal IDs into your marketing strategy, ensure you’re leveraging first-party data collection to its fullest potential by encouraging user sign-ups and engagement through valuable content, loyalty programs, or gated resources.

This enhances your ability to assign universal IDs and also strengthens the accuracy of your audience targeting.

4. Cohort-Based Targeting

Cohort-based targeting is an advertising method that groups users into cohorts (or groups) based on shared behaviors, interests, or characteristics, rather than tracking individual users.
It allows marketers to target ads to groups of people with similar behaviors while maintaining user anonymity and reducing reliance on personal data.

How It Works

• Data Aggregation. Platforms collect data from users’ interactions across the web, such as the types of websites they visit or the content they engage with. This data is anonymized and used to form behavioral patterns.
• Cohort Creation. Users are then grouped into cohorts based on shared interests or behaviors, without any personally identifiable information (PII) being stored or shared. Each cohort is assigned an identifier, which advertisers can use for targeting.
• Targeting. Instead of tracking individual users, advertisers target these cohorts as a whole. For example, if a cohort is made up of users who frequently visit B2B SaaS product pages, an advertiser can serve ads related to SaaS solutions to that cohort.

Since cohort-based targeting relies on group behaviors rather than individual tracking, it offers a higher level of privacy compliance, reducing the risk of infringing on users’ personal data rights.

How to Use Cohort-Based Targeting in Your Marketing Strategy

• Behavior-Based Targeting. Focus on cohorts that share behaviors or interests relevant to your business. For example, a B2B SaaS company could target cohorts made up of users who frequently engage with tech or enterprise software content.
• Broader Reach. Instead of targeting niche audiences or individual users, use cohort-based targeting to reach broader groups of users that align with your industry and product offerings.
• Contextual Alignment. Combine cohort-based targeting with contextual advertising to enhance relevance. By placing ads in environments where your target cohorts frequently interact, you can increase ad performance.
• Testing and Optimization. Regularly test different cohort groups and optimize your campaigns based on the behaviors or engagement metrics of each cohort. This helps fine-tune your messaging to resonate with the broader characteristics of each group.

Pros

• Scalable. It allows you to reach large groups of users who share similar behaviors or interests, increasing the scale and impact of your campaigns without the need for detailed individual data.
• Efficient Targeting. By targeting user groups that exhibit behaviors relevant to your product or service, cohort-based targeting can result in more relevant ads with higher engagement and conversion rates.
• Cost-Effective. Since it relies on aggregated user data, cohort-based targeting can often be less expensive than behaviorally targeted ads that use detailed, individual user data.

Cons

• Less Personalization. Since cohort-based targeting deals with groups rather than individuals, it lacks the granularity and personalization offered by individual-based targeting methods.
• Limited Insights on Individuals. While cohorts can provide general behavioral trends, you won’t be able to gain deep insights into individual user behavior, making it difficult to personalize marketing efforts or track individual journeys.
• Dependent on Platform Data. Since cohorts are created by ad platforms, your access to granular insights or control over cohort formation is limited. You may need to rely on the platform’s algorithms to define relevant cohorts.

DB Pro Insights → To maximize the effectiveness of cohort-based targeting, combine it with first-party data from your CRM or website analytics.
This allows you to refine your understanding of broader cohort behaviors while still tapping into your internal insights to create more contextually relevant ads.

5. Data Clean Rooms

A Data Clean Room is a private, secure environment where multiple parties — typically brands and publishers — can collaborate and analyze anonymized, aggregated customer data without sharing personally identifiable information (PII).

Data clean rooms allow ad tech companies and marketers to perform advanced analytics, audience targeting, and measurement across datasets from different sources while maintaining strict privacy controls.

How It Works

• Data Aggregation and Anonymization. Each party uploads its first-party data to the clean room, where it is anonymized and encrypted. This ensures that no personal information (like names, emails, or individual identifiers) is exposed or shared.
• Data Matching. The clean room uses privacy-safe methods, like hashing, to match records between datasets based on common, non-identifiable attributes. For example, it might match customer behaviors from one dataset with sales data from another, without revealing individual identities.
• Analysis and Insights. Marketers can perform joint analytics to understand user behavior, campaign performance, and other insights. For example, brands can determine how many people viewed their ad on a publisher’s platform and then made a purchase on their website.

How to Use Data Clean Rooms in Your Marketing Strategy

• Cross-Channel Attribution. You can match customer journeys across platforms (e.g., seeing an ad on a publisher’s site and completing a purchase on your website) and measure the effectiveness of campaigns.
• Audience Overlap Analysis. Collaborate with publishers or partners to understand audience overlaps. For example, if you run ads on multiple platforms, a data clean room can reveal which audience segments are common across those platforms, helping you optimize ad spend and reduce redundancy.
• First-Party Data Enhancement. Data clean rooms allow you to enhance your first-party data with external datasets. You can combine your internal customer data with a publisher’s or partner’s anonymized data to gain richer insights into customer behaviors and preferences.

Pros

• Privacy-First. Data clean rooms are designed to meet privacy regulations, such as GDPR and CCPA, by anonymizing data and ensuring that no PII is exchanged between parties.
• Improved Analytics. Data clean rooms provide a rich source of analytics for understanding customer behaviors, marketing performance, and audience overlaps without violating privacy laws.
• Cross-Platform Measurement. Advertisers can track the performance of their campaigns across multiple platforms, ensuring they accurately measure their reach and effectiveness while maintaining privacy compliance.

Cons

• Limited Data Access. Data clean rooms don’t allow raw data extraction. Marketers only get aggregated insights, which means you can’t conduct in-depth analysis at the individual level.
• Complex Setup. Setting up and managing a data clean room can be technically challenging, requiring advanced infrastructure, privacy protocols, and collaboration with third-party providers.
• Partner Dependency. To make the most out of a data clean room, you need to collaborate with partners or publishers that are willing to share anonymized data, which can limit the available insights if there are fewer participants.
• Data Quality and Alignment. The success of data clean rooms depends on the quality of the datasets provided by each party. Mismatches or inconsistencies in data can affect the accuracy of insights.

DB Pro Insights → When leveraging data clean rooms, work with a trusted partner who has experience in managing privacy-compliant data environments.

Focus on integrating your first-party data with high-quality third-party data sources to gain more meaningful insights into customer behavior, all while maintaining strict adherence to privacy laws.

6. Intent Data

Intent data is information collected about a user’s behavior, signaling their potential interest or readiness to make a purchasing decision.
It is typically gathered from various interactions, such as website visits, content downloads, email engagements, or social media activities, indicating potential buying intent.

How It Works

Intent data is collected by tracking users’ interactions across digital channels, both first-party (direct interactions with your brand) and third-party (interactions on external websites or platforms).

The process of using intent data generally follows these steps:

• Data Collection. User activities such as downloading white papers, visiting product pages, or consuming specific content are tracked.
• Behavior Analysis. Algorithms analyze these behaviors to determine patterns that indicate intent, such as repeated visits to competitor pages or specific content consumption related to your industry.
• Segmentation and Scoring. Intent data is segmented and scored based on the intensity of the behaviors (e.g., multiple downloads or repeated visits signal higher intent). The higher the intent score, the more likely the prospect is in the consideration or decision stage.

Marketers use this data to create personalized, targeted campaigns, reaching out to prospects showing high intent with relevant messaging, product recommendations, or sales follow-ups.

How to Use Intent Data in Your Marketing Strategy

• Lead Prioritization. Use intent data to prioritize leads by focusing on those showing higher intent. This enables your sales and marketing teams to concentrate their efforts on prospects that are more likely to convert.
• Account-Based Marketing (ABM). Intent data helps you identify companies actively researching your product category. You can target specific accounts with personalized outreach and tailored content based on their intent signals.
• Personalized Campaigns. Segment your audience based on their intent levels and behaviors. For example, prospects who have downloaded multiple white papers on a specific topic might be sent targeted follow-up emails with a relevant case study or demo invitation.
• Sales Enablement. Equip your sales team with intent data to inform their outreach strategies. If a prospect has been showing intent signals around your product, sales can reach out at the right time with the most relevant messaging.

Pros

• Enhanced Targeting. Intent data allows marketers to target prospects who are actively showing interest, improving the chances of engagement and conversion.
• Better Personalization. By understanding a prospect’s intent signals, you can create more personalized marketing campaigns that resonate with their current needs and interests.
• Competitive Advantage. Intent data helps you stay ahead by understanding what prospects are searching for in your industry and how your competitors are engaging with them.

Cons

• Data Accuracy. Third-party intent data might not always be accurate, as the signals can be vague or misinterpreted, leading to targeting inefficiencies.
• High Costs. Accessing high-quality intent data from third-party providers can be expensive, especially for smaller companies with limited marketing budgets.
• Data Privacy Concerns. Depending on how third-party intent data is collected, it could raise privacy issues if not handled in compliance with regulations like GDPR or CCPA.

DB Pro Insights → For best results, combine first-party intent data (from your website and CRM) with third-party data (from external platforms) to create a more complete picture of buyer intent.

For example, you can use Demandbase to identify companies that are ready to buy. With this, you can prioritize the best leads, tailor your messaging, and reach out at the right time, making your marketing efforts more effective and driving more sales. Try it out now!

7. Publisher-Provided IDs (PPIDs)

Publisher-provided IDs (PPIDs) are unique identifiers that publishers create and assign to individual users based on their interactions with the publisher’s content or platform.

These IDs allow publishers to recognize users across sessions without relying on third-party cookies and share anonymized user data with advertisers in a privacy-compliant way.

PPIDs enable targeted advertising while respecting user privacy, making them a viable solution for cookieless advertising.

How It Works

• • ID Creation. When a user visits a publisher’s site, the publisher assigns a unique ID to that user based on their interactions. This ID is tied to the publisher’s first-party data, such as user logins, content consumption patterns, or subscription details.
  • User Behavior Tracking. PPIDs track user activities across the publisher’s properties. This data may include what content the user reads, how frequently they visit, or what products they browse.
  • ID Sharing with Advertisers. The publisher can share PPIDs with advertisers, allowing them to target ads to these users without exposing any personal information. Advertisers use PPIDs to deliver personalized ads based on user behavior on the publisher’s platform, ensuring relevance while maintaining privacy.

How to Use Publisher-Provided IDs in Your Marketing Strategy

• Targeted Advertising. Use PPIDs to target users based on their behavior on publisher platforms. This allows you to deliver more relevant ads without relying on third-party cookies or tracking across different websites.
• Audience Segmentation. PPIDs enable you to create detailed audience segments based on user behavior and preferences, such as frequent content readers or users who consistently engage with certain product categories. You can then target these segments with personalized messaging or offers.
• Cross-Platform Campaigns. Leverage PPIDs across multiple properties owned by a publisher to track user engagement. For example, if a publisher owns both a news website and a mobile app, PPIDs can ensure that users are consistently targeted across both platforms with relevant ads.

Pros

• First-Party Data Utilization. Since PPIDs are tied to a publisher’s first-party data, they enable advertisers to use valuable, consented information, improving ad relevance and effectiveness.
• Accurate Targeting. With PPIDs, advertisers can deliver more personalized ads to users based on detailed, first-party insights from the publisher, enhancing engagement and conversion rates.
• Control Over Data. Publishers maintain control over the PPID system, ensuring that user data is not shared or exploited by third parties. This helps protect users’ privacy while still allowing for targeted advertising.

Cons

• Limited Reach. PPIDs are only applicable within the publisher’s ecosystem. If a user leaves the publisher’s site or app, advertisers won’t be able to track or target them unless they return to the same platform.
• Publisher Dependency. The effectiveness of PPIDs relies heavily on the quality of the publisher’s first-party data. If the publisher’s data is incomplete or inaccurate, it can affect the accuracy of targeting.
• Cross-Publisher Limitations. PPIDs don’t work across different publishers, limiting their use for advertisers who want to target users across multiple websites or apps.
• Data Fragmentation. Since PPIDs are publisher-specific, data can become siloed. Advertisers might need to manage multiple PPIDs from different publishers, making it difficult to create a unified view of the user.

DB Pro Insights → Maximize the potential of PPIDs by partnering with publishers that have strong first-party data capabilities and rich audience insights.
By doing this, you can ensure that your PPID-based campaigns are more accurate and effective, providing you with valuable targeting opportunities in a privacy-compliant manner.

8. AI-Driven Attribution

AI-driven attribution refers to the use of artificial intelligence and machine learning algorithms to analyze customer interactions across various channels and touchpoints, assigning value to each in order to determine which marketing efforts contribute the most to conversions.

It goes beyond traditional attribution models (such as first-touch or last-touch attribution) by dynamically analyzing complex customer journeys to deliver more accurate, data-driven insights.

How It Works

• Data Collection. AI-driven attribution models gather data from all marketing channels, including website visits, social media engagement, email opens, ad clicks, and offline interactions. This includes first-party data as well as third-party insights.
• Pattern Recognition. AI analyzes the data to identify patterns in customer behavior and interactions with different marketing channels. It looks for common conversion paths and determines which touchpoints are more influential in driving customer actions.
• Weight Assignment. Instead of using a fixed rule, such as assigning all value to the first or last touchpoint, AI assigns varying levels of credit to each touchpoint based on its actual impact on the buyer’s journey. This dynamic weighting is based on the real performance of each channel.

How to Use AI-Driven Attribution in Your Marketing Strategy

• Understanding Complex Buyer Journeys. Helps you understand the multi-touch, multi-channel journeys typical of B2B marketing, where buyers often interact with multiple pieces of content before making a decision.
• Improving Personalization. Reveals which types of content or messaging resonate with different segments of your audience. You can use this information to tailor more personalized experiences that drive engagement and conversions.
• Cross-Channel Attribution. Allows you to measure performance across both online and offline channels. For example, it can help attribute credit to a webinar or conference in conjunction with digital channels like email and social media (e.g., from LinkedIn posts to webinar attendance).
• Real-Time Campaign Adjustments. Since AI-driven attribution models provide real-time insights, you can adjust campaigns on the fly based on performance. If a certain channel is underperforming, you can reallocate budget or test different messaging to improve outcomes.

Pros

• Accuracy and Precision. Provides a more accurate understanding of how each marketing touchpoint contributes to conversions. This leads to better decision-making regarding budget allocation and campaign optimization.
• Improved Multi-Channel Tracking. AI models can track and analyze data across multiple channels, providing a holistic view of customer behavior. This is especially useful for B2B companies where the customer journey involves multiple interactions.
• Deeper Insights. AI can analyze large datasets and identify patterns or trends that may not be visible through traditional attribution models. This enables real-time adjustments to advertising strategies.

Cons

• Data Dependency. Requires access to large volumes of high-quality data across all touchpoints. If your data collection processes are incomplete or inaccurate, the AI model may deliver flawed insights.
• Lack of Transparency. AI models can be seen as “black boxes,” meaning it’s not always clear how they arrive at certain conclusions. This can make it difficult for marketers to fully trust or understand the attribution outcomes.

DB Pro Insights → To get the most out of AI-driven attribution, combine it with customer segmentation and intent data to refine your targeting and personalization efforts.

You can do this with Demandbase to identify exactly which marketing activities lead to conversions. This helps you focus on what’s working, so you can get the most out of your budget and continuously improve your marketing strategies.

9. Identity Management Solutions (IDMS)

IDMS is a platform designed to manage, track, and leverage user identities across different platforms to enhance ad targeting, personalization, and measurement — all without relying on third-party cookies.

How It Works

• Identity Resolution. IDMS gather various user identifiers (hashed emails, device IDs, mobile IDs, etc.) and connect them to create a single, unified user profile across different devices and platforms.
• Data Matching and Linking. Using deterministic (exact match) and probabilistic (statistical inference) matching, IDMS associates different identifiers with one user, ensuring consistency in tracking and personalization.
• Privacy-Enhancing Technologies. IDMS employ privacy-centric techniques like hashing, encryption, and anonymization to comply with regulations (GDPR, CCPA) while enabling accurate identity resolution.
• Unified Customer View. The result is a comprehensive and up-to-date profile of the user, allowing businesses to track user journeys and behaviors across digital touchpoints.

How to Use Identity Management Solutions in Your Marketing Strategy

• Enhanced Personalization. Use IMS to securely collect and manage first-party user data to personalize experiences and tailor content based on verified user profiles. By knowing who the users are and their roles, marketers can deliver more relevant content, offers, and recommendations.
• Retargeting and Remarketing. Use IDMS to identify users who interact with your content across platforms, enabling highly effective retargeting strategies. Since third-party cookies are phased out, IDMS ensures you can still engage with known users.
• Cross-Channel Marketing. Leverage identity management to ensure your marketing messages stay consistent across channels, whether users engage via mobile, desktop, or social media. IDMS ensures the same user experience is delivered at every touchpoint.

Pros

• Unified Customer View. Gives you a 360-degree understanding of your users across all touchpoints, improving personalization and marketing accuracy.
• Cross-Device Tracking. Accurately tracks users across multiple devices without third-party cookies, ensuring consistency in campaigns.
• Improved Retargeting. Enables better retargeting campaigns by identifying users even as they move across platforms and devices.

Cons

• Complex Implementation. Requires robust infrastructure and technical expertise to integrate IDMS into your existing marketing stack.
• Cost Concerns. Implementing and maintaining an IDMS can be expensive, especially for smaller businesses with limited resources.
• Reliance on First-Party Data. Requires a strong foundation of first-party data, which may be challenging for businesses that haven’t invested in data collection.
• Privacy Concerns. While IDMS uses privacy-enhancing technologies, there’s still the challenge of balancing personalization with user privacy.

DB Pro Insights → Before implementing an IDMS, ensure you have a solid first-party data collection strategy in place.

Engage users with valuable content and incentives that encourage them to provide accurate data. This ensures your IDMS has the best possible information to work with and can deliver the most accurate and effective identity resolution.

10. Google Privacy Sandbox

Google Privacy Sandbox is an initiative designed to enhance user privacy while still enabling targeted advertising. It introduces new APIs that allow advertisers to target and measure user behavior without directly tracking individuals.

How It Works

• Topics API. Replacing Google’s earlier FLoC initiative, the Topics API allows browsers to share information about a user’s general interests (e.g., “technology,” “sports”) based on their browsing history.
• FLEDGE API. FLEDGE (First Locally-Executed Decision over Groups Experiment) enables interest-based remarketing without using third-party cookies. It runs ad auctions locally on the user’s browser, allowing advertisers to serve personalized ads based on a user’s previous interactions with their website, all while keeping the user’s data private and on their device.
• Attribution Reporting API. This API allows advertisers to measure conversions (e.g., when a user sees an ad and then purchases a product) without tracking the user across websites.
• Aggregated Reporting API. This API helps advertisers collect performance data from their campaigns, such as ad impressions and clicks, in an aggregated and anonymous way. This ensures that marketers can optimize their strategies without accessing detailed individual-level data.
• Trust Tokens API. The Trust Tokens API helps differentiate between bots and human users without tracking users across websites. This feature is designed to combat ad fraud while ensuring that legitimate users aren’t identified or tracked.

How to Use Google Privacy Sandbox in Your Marketing Strategy

• Interest-Based Targeting with Topics API. Use the Topics API to target users based on their broad interests, such as “business technology” or “software development.” This allows you to reach relevant audiences while keeping their data private.
• Retargeting with FLEDGE. Implement FLEDGE for remarketing campaigns that don’t rely on cookies. You can retarget users who previously interacted with your site by serving ads based on local browser data, without ever accessing personal information.
• Conversion Tracking with Attribution Reporting API. Use the Attribution Reporting API to measure campaign performance and conversion data without tracking individual users across different websites. This will help you understand how effective your ads are while maintaining compliance with data privacy regulations.
• Ad Campaign Optimization with Aggregated Reporting API. Leverage aggregated, anonymized reporting to understand your campaign’s overall performance. By using data from the Aggregated Reporting API, you can refine ad targeting and budget allocations without accessing specific user data.
• Fraud Prevention with Trust Tokens API. Use the Trust Tokens API to reduce ad fraud by ensuring that bots are filtered out, allowing you to spend your advertising budget more effectively on real users while maintaining privacy.

Pros

• Effective Targeting and Personalization. Despite prioritizing privacy, the Privacy Sandbox still allows marketers to deliver personalized and relevant ads to users based on their interests and interactions.
• Accurate Conversion Measurement. Tools like the Attribution Reporting API allow for accurate measurement of campaign performance and conversions while protecting user anonymity.
• Fraud Protection. Trust Tokens API helps mitigate ad fraud, ensuring that advertisers target real users, which improves campaign efficiency and reduces wasted spend.

Cons

• Limited Individual-Level Targeting. Since the Privacy Sandbox focuses on protecting user privacy, marketers lose the ability to target individuals based on specific browsing behavior, limiting personalization.
• Less Granular Data. The anonymization of user data means that marketers will work with broader, aggregated datasets, reducing the granularity of insights compared to cookie-based tracking.
• Dependence on Google Ecosystem. Privacy Sandbox is controlled by Google, meaning businesses relying on these tools must operate within Google’s ecosystem and guidelines, potentially limiting flexibility.

DB Pro Insights → Focus on building your first-party data through direct user interactions. Use privacy-friendly strategies like consent-based email collection, surveys, and customer loyalty programs to enrich your audience insights.

Combine this with Privacy Sandbox APIs to maintain effective targeting and attribution.

11. Second-Data Partnerships

Second-party data refers to the data that another company has collected directly from its audience, which is then shared with your business through a partnership.

Unlike third-party data, second-party data is typically shared in a trusted relationship, ensuring accuracy and relevance for your marketing efforts.

Companies often use second-party data for joint marketing efforts, such as co-branded campaigns or shared audience targeting.

A common example is airline and travel companies sharing customer booking and travel data for cross-promotional offers and personalized travel experiences.

How It Works

• Data Sharing. You partner with another business (e.g., a publisher or vendor) that collects first-party data from its own audience. This data can include website behaviors, purchases, or customer preferences.
• Data Integration. The second-party data is integrated into your systems, allowing you to enhance audience segmentation, targeting, and campaign personalization.
  How to Use Second-Party Data in Your Marketing Strategy
• Audience Expansion. Use second-party data to expand your audience by reaching customers from your partner’s network who are similar to your current audience but may not have interacted with your brand yet.
• Enhanced Personalization. By combining your first-party data with second-party insights, you can deliver more personalized content and offers based on known customer behaviors and preferences.
• Targeted Advertising. Leverage the second-party data for more accurate targeting in ad campaigns, ensuring your ads reach relevant users who are likely to be interested in your products or services.

Pros

• High Quality and Accuracy. Since second-party data is sourced directly from a trusted partner, it’s more reliable and relevant than third-party data.
• Extended Reach. It allows you to target new audiences beyond your existing customer base, expanding your marketing efforts.

Cons

• Limited Availability. You’re reliant on your partner’s data collection practices, and if their data is not well-managed, it may limit the usefulness of the insights.
• Trust Dependency. Partnerships need to be built on trust, as data sharing can be sensitive and require legal agreements.
• Data Integration Challenges. Merging second-party data with your systems can require infrastructure and data management tools to ensure smooth integration.

DB Pro Insights → Maximize the value of second-party data by partnering with businesses that have similar target audiences but offer complementary products or services.

This ensures the data you receive is relevant and useful for your marketing efforts, helping you achieve better personalization and targeting.

Demandbase: Your Advertising Partner in a Cookieless World

Demandbase offers a future-proof solution that enables you to thrive in this new era without sacrificing the insights and targeting precision you’ve come to rely on.

With advanced intent data, account-based marketing, and AI-driven analytics, Demandbase empowers you to reach the right audiences while staying compliant with privacy regulations.

How Demandbase helps:

• Zero in on High-Value Accounts. Stop wasting time on low-quality leads. Demandbase pinpoints the accounts actively showing buying intent, so you can focus your efforts on the deals that matter.
• Drive Engagement with Personalized Experiences. Deliver the right message to the right person at the right time. Demandbase uses real-time intent data to tailor content and offers that speak directly to your prospects’ needs.
• Launch Laser-Focused Advertising Campaigns. Stop wasting ad spend on broad targeting. Demandbase helps you reach the right B2B audiences with precision, ensuring your message hits the mark.
• Align Sales and Marketing for Maximum Impact. Get sales and marketing on the same page. Demandbase provides a unified view of the customer journey, empowering both teams to collaborate effectively and close more deals.
• Measure What Matters. Get the insights you need to make data-driven decisions. Demandbase tracks your campaigns across channels, revealing what’s working and what’s not.

Demandbase isn’t just another tool—it’s your unfair advantage in the cookieless world.

Ready to future-proof your B2B marketing? 

Get started with Demandbase