The holiday season is a make-or-break period for many brands. With advancements in AI, brands are now equipped to unlock benefits ranging from increased efficiencies to enhanced insights and media optimization. As we approach the 2025 holiday season, success no longer hinges on just planning, but on successfully operationalizing an advanced, privacy-centric, and automated data infrastructure.
For many B2C partners, the holiday shopping period can determine their performance for the entire year. It’s crucial to leverage predictive analytics, machine learning, and durable data practices to elevate your marketing efforts.
1. Establishing the Durable Data Foundation: Server-Side Tagging and Identity
In 2025, the digital landscape is defined by the practical reality of cookieless browsing. All sophisticated predictive and measurement efforts rely on stable, high-quality first-party data.
Implement Server-Side Tagging (SST)
Client-side tags are increasingly blocked by browsers and privacy settings. Server-Side Tagging is now necessary, providing two critical advantages:
Durable Collection: SST allows you to collect data reliably, bypassing many browser restrictions. This is the bedrock for high-quality data feeds to Google Ads, Google Analytics 4 (GA4), and other media platforms.
Data Governance: SST provides a central point of control to manage the data passing to vendors, ensuring compliance and the exclusion of sensitive PII.
Advanced Identity Collection and Resolution
User ID: Set up the collection of a custom parameter for User ID in your analytics tool, such as GA4. This enables the creation of a single customer view, spanning cross-device tracking and integration with other data sources.
Enhanced Conversions: Ensure Enhanced Conversions are fully implemented, securely hashing first-party data (like email or phone number) to improve match rates for crucial conversion events in ad platforms.
2. Leveraging Predictive Audiences for Acquisition and Retention
Predictive audiences empower businesses to identify customers who are likely to convert based on relevant characteristics such as product preferences and spending habits. This strategy maximizes ROI by focusing on the most promising leads.
Common Types of Predictive Audiences
Propensity Modeling: This can enhance your retargeting efforts by identifying customers with a higher likelihood of conversion for various desired actions (a purchase, a sign-up, or a specific interaction).
Customer Lifetime Value (CLV): Understanding CLV guides your strategies to acquire new customers or nurture relationships with existing ones.
Churn Modeling: This not only helps prevent customer attrition but also enables marketers to identify customers who are likely to have churned, ensuring they aren’t included in campaigns to enhance overall efficiency.
Furthermore, utilizing capabilities like Customer Match offers a durable, cookie-free targeting solution, allowing marketers to deliver impactful campaigns at scale in a cost-effective manner.
3. Precision Bidding and Forecasting with AI
The competitive edge during the holidays comes from how effectively advertisers leverage their first-party data to inform automated systems.
Value-Based Bidding (VBB) Optimization
Value-based bidding (VBB) in platforms like SA360 and Google Ads utilizes sophisticated algorithms to optimize your bids based on your first-party data.
Maximize the Value of Consideration-Stage Tactics: Optimize for a conversion’s value in the overall buying journey by using modeling to measure the value of mid-funnel actions. Bidding based on the value of these consideration-stage interactions drives more high-quality site traffic.
pCLV-Driven Acquisition: The most advanced strategy moves beyond simple conversion value to truly bidding based on Predicted Customer Lifetime Value (pCLV). By analyzing purchase histories and predicted future revenues, marketers can bid more aggressively — and profitably — on users who represent high long-term value.
Real-Time Forecasting
Forecasting is a quantitative approach to predicting future events based on historical data. It adds significant value across the organization:
Revenue and KPI Prediction: Forecasting helps predict revenue and other key performance indicators (KPIs). By comparing actual performance against forecasts, marketers can better evaluate campaign effectiveness.
Demand Forecasts: Product and category-level forecasts are valuable for aligning promotional strategies with expected sales, allowing businesses to manage physical inventory and campaign strategies proactively.
4. Omnichannel Attribution
Holistic Regression-Based Attribution (RBA)
As the digital landscape evolves, it’s essential to transition to attribution models that do not rely on third-party cookies. Regression-Based Attribution (RBA) offers a more durable method compared to traditional multi-touch models.
Attributing Cookieless Conversions: RBA helps marketers address crucial questions, such as how to attribute conversions to digital media for both offline and online sales in a world where cookies are less effective.
Scenario Planning for Budget Optimization: Run What-If Analysis and an optimization algorithm to determine the optimal budget allocation across channels and tactics.
By increasing campaign efficiency using predictive audiences, optimizing bids with data-driven insights, integrating forecasting into real-time automation, and utilizing robust, cookieless attribution methods, marketers can achieve a competitive edge and successfully navigate the complexities of the 2025 holiday season.
Get in touch with our experts today to discuss options for optimizing your ad performance.
