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Global e-commerce conversion rates average 2.86%. This means 97 out of 100 visitors leave without purchasing. Most retargeting campaigns treat all these non-converters equally—a casual browser who viewed one product receives the same advertising investment as someone who spent 30 minutes comparing options, reading reviews, and adding items to cart.
Likelihood to purchase scoring assigns each visitor a probability score (0 to 1) representing their chance of converting within a specific timeframe. This study examines how these models work, their applications across marketing channels, and measurable business impact based on data from multiple deployments.
Retargeting performs significantly better than cold traffic—studies show retargeted users are 43% more likely to convert than first-time visitors¹. Yet even with these improvements, fundamental inefficiencies remain.
Current retargeting performance benchmarks:
Despite these improvements, 90%+ of retargeted visitors still don't convert. The waste comes from treating all non-converters equally. Within retargeting audiences, conversion probability varies dramatically:
Traditional rule-based segmentation—"added to cart" or "viewed 3+ products"—cannot account for the behavioral complexity that drives purchases. Rules miss critical patterns: visit frequency over time, session duration variations, time-of-day preferences, product view sequences, comparison shopping depth, review engagement, and hundreds of other signals that interact in non-linear ways. A rule system attempting to capture this complexity would require thousands of nested conditions, becoming impossible to maintain or optimize.
Likelihood to purchase scoring uses machine learning models to predict which visitors will convert into customers. Unlike rule-based systems that follow predetermined logic, these models learn from millions of interactions to identify patterns humans would never detect.
The model ingests behavioral data continuously—every product view, cart addition, review read, or price comparison becomes a signal. But instead of treating these actions in isolation, machine learning identifies how behaviors combine and sequence to indicate purchase intent. The model discovers, for instance, that visitors who read reviews after comparing prices show different intent than those who read reviews first, then never return to product pages.
The scoring happens in real-time—calculations complete in under 300 milliseconds. This speed enables immediate response to behavior changes. When a visitor's actions indicate rising purchase intent (moving from browsing to comparing specific products), their score updates instantly. On-site messaging can shift from educational to transactional. Email triggers can deploy while the visitor is still researching. Ad platforms can adjust bids before the visitor leaves the site.
Machine learning excels at finding non-obvious correlations in vast datasets. Where humans might track a dozen factors, ML models simultaneously analyze hundreds of behavioral signals and their interactions.
Consider the complexity: A visitor who reads reviews might be researching or ready to buy—it depends on their previous actions, time spent, navigation patterns, and dozens of other factors. The model learns these nuances:
But the real power isn't in these individual patterns—it's in how they interact. The model understands that a device switch means something different for a returning visitor than a first-time browser. It learns that review reading on Tuesday afternoon correlates differently with purchase intent than review reading on Saturday morning. These multi-dimensional patterns, impossible to capture with rules, become clear signals to the algorithm.
Unlike static rules that degrade over time, machine learning models improve with more data. Each conversion or non-conversion becomes a training signal, helping the model refine its predictions. Seasonal patterns emerge automatically. New product categories get incorporated without manual configuration. Changes in user behavior—like increased mobile purchasing during holidays—get detected and factored into future predictions.
The model also handles edge cases that break rule-based systems. What about visitors using VPNs that change their apparent location? Or those who clear cookies between sessions? Machine learning finds alternative signals to maintain accuracy where rules would simply fail.
Probability-based bid adjustments produce measurable changes in advertising performance metrics, with the strongest effects observed in retargeting campaigns where behavioral data density is highest.
Google Ads and Meta's bidding algorithms accept probability scores as optimization signals through their respective APIs—Google's Value-Based Bidding and Meta's Value Optimization features. While audience membership remains the foundation for targeting and segmentation, these platforms enhance bidding precision by incorporating continuous probability values—allowing bid modifications within audiences based on individual visitor scores rather than treating all audience members equally.
Implementation involves passing probability scores through conversion tracking parameters. Visitors with higher purchase probability receive proportionally higher bid multipliers, while those with minimal probability receive reduced bids or exclusion from campaigns. Optimal multiplier curves follow logarithmic rather than linear relationships—the marginal value of probability increases diminishes at lower ranges where intent differences are less meaningful.
High-probability segments consistently demonstrate conversion rates multiple times higher than unsegmented baselines. Cost per acquisition in these segments typically decreases substantially, though absolute spend often increases due to higher bid competition for high-intent users. The net effect varies by implementation but generally shows positive ROAS improvements, with magnitude depending on baseline performance and market competition.
Standard retargeting distributes budget uniformly across non-converting visitors, resulting in median conversion rates of 2-3%. Probability-based allocation concentrates spending according to conversion likelihood distributions.
Common allocation patterns concentrate the majority of budget on high-probability visitors who typically represent a small fraction of the total retargeting pool. Medium-probability visitors receive moderate budget allocation, while low-probability visitors receive minimal spend. These allocations reflect diminishing returns curves where marginal ROAS equalizes across segments.
The budget redistribution affects not just spending levels but campaign structure. High-probability segments utilize conversion-focused creative and landing pages with average order value optimization. Medium-probability segments receive consideration-stage messaging emphasizing product benefits and social proof. Low-probability segments see awareness campaigns with educational content and category-level targeting rather than specific product promotion.
Performance metrics diverge significantly by segment: high-probability segments show substantially higher conversion rates and average order values, while low-probability segments maintain minimal conversion rates but generate valuable micro-conversions (email signups, wishlist additions) that feed future scoring models.
Likelihood scoring enables sophisticated use cases across the entire customer journey.
Traditional lookalike audiences use your entire customer list as a seed, including one-time buyers who never return. Probability scoring enhances this approach by identifying which actual converters exhibited the strongest pre-purchase intent signals. Instead of treating all conversions equally, you can seed lookalikes with customers who showed high probability scores before converting—those whose behavior patterns were most predictive of purchase.
Google Customer Match and Facebook Custom Audiences both accept these refined seed lists, finding prospects who mirror customers with clear intent patterns rather than those who converted through heavy discounting or random discovery. This approach identifies new users whose behavior resembles your most predictable converters, not just any converter.
Different probability scores warrant fundamentally different messaging strategies:
High-probability visitors:
Medium-probability visitors:
Low-probability visitors:
Score transitions indicate behavioral shifts that create intervention opportunities. When a visitor's probability increases significantly within a session or between visits, it signals escalating interest that warrants immediate response.
Automated triggers at transitions:
These transition moments represent behavior changes that indicate shifting intent—catching visitors at these inflection points allows for timely, relevant interventions rather than delayed or mistimed outreach.
Traditional forecasting predicts based on traffic volume and average conversion rates. Probability scoring adds precision by distinguishing between browsers and likely buyers.
Standard forecast approach:
Probability-based forecast approach:
This segmented approach improves demand prediction accuracy by distinguishing between browsing and buying intent. A concentration of high-probability visitors viewing specific products indicates stronger near-term demand than equivalent traffic from low-probability browsers.
Successful deployment requires proper infrastructure and realistic expectations.
Minimum behavioral tracking required:
Most companies already collect this through Google Analytics or similar platforms, but quality varies. A thorough audit typically reveals gaps requiring 20-40 hours to address.
Google's predictive metrics documentation outlines minimum requirements:
Time to sufficient data by traffic level:
Integration requirements vary by platform:
Total implementation effort depends on technical expertise, existing infrastructure, and number of platforms. Most companies complete initial integration within 2-4 weeks.
Models need time to learn your specific audience patterns:
Most implementations see meaningful improvements within the first month, with performance stabilizing after 2-3 months of data collection and refinement.
Different industries see varying levels of improvement based on their customer behavior patterns:
Fashion/Apparel: Benefits from understanding style preferences and seasonal shopping patterns. Multiple product views and wishlist additions provide strong intent signals.
Electronics: High consideration purchases with extensive research phases. Comparison shopping and specification review patterns offer clear differentiation between researchers and buyers.
Home & Garden: Mix of impulse and planned purchases. Project-based shopping creates distinct behavioral patterns that models can identify.
B2B Software: Longer sales cycles with multiple stakeholders. Repeated visits, documentation downloads, and demo requests create rich behavioral data despite lower baseline conversion rates.
Industries with more complex purchase decisions typically see larger improvements as the behavioral patterns provide clearer differentiation between serious buyers and casual browsers.
High probability scores don't guarantee purchase. Even visitors with the highest scores may not convert due to external factors—price sensitivity, timing, competitive offers, or simply changing their mind.
Solution: Use probability ranges for campaign tiers, not binary exclusions. Adjust messaging intensity rather than excluding segments entirely. Accept that no prediction is perfect and build campaigns that work across a range of outcomes.
Probability scores become less accurate over time. A score calculated today reflects current intent, but that intent changes. A week later, the visitor's situation, needs, or interests may have shifted significantly.
Solution: Recalculate scores when visitors return. Set maximum score ages for different use cases—shorter for time-sensitive campaigns, longer for brand awareness efforts.
Multiple device usage creates prediction challenges. Visitors researching on mobile then purchasing on desktop appear as different users unless properly linked. This fragmentation reduces model accuracy and can misclassify high-intent visitors.
Solution: Implement identity resolution where possible through logged-in states or consented tracking. Accept that some accuracy loss is inevitable in a privacy-first environment.
The return on investment depends on several factors:
Current performance baseline: Companies with lower baseline conversion rates typically see larger relative improvements. Those already highly optimized may see smaller gains.
Ad spend volume: Higher spending companies can amortize implementation costs faster and have more data for model training.
Industry and margins: High-margin businesses can tolerate longer payback periods. Low-margin businesses need faster returns to justify investment.
Implementation approach: Phased rollouts starting with highest-impact channels reduce risk and prove value before full deployment.
Most companies evaluate ROI based on improved ROAS and reduced customer acquisition costs. The payback period varies widely based on these factors, but organizations with sufficient traffic volume and ad spend typically see positive returns within the first quarter of implementation.
Likelihood scoring operates within modern privacy frameworks:
Data requirements:
GDPR/CCPA compliance:
Clear privacy policies explaining behavioral scoring see 12% lower opt-out rates than vague disclosures.
Likelihood to purchase scoring addresses a fundamental inefficiency in digital marketing: treating all non-converters equally despite vast differences in their purchase intent. By using machine learning to analyze behavioral patterns, companies can identify which visitors are most likely to convert and adjust their marketing accordingly.
Success requires three elements: sufficient data volume (Google's documentation suggests 1,000+ monthly conversions), proper behavioral tracking, and commitment to ongoing optimization. The technology integrates with existing marketing infrastructure through standard APIs and tracking tools.
As third-party cookies disappear and privacy regulations tighten, first-party behavioral scoring becomes increasingly important for maintaining marketing effectiveness. Companies that can accurately predict purchase intent from their own data will have a significant advantage over those relying on diminishing third-party signals.
¹ Cropink, "50+ Retargeting Statistics Marketers Need to Know in 2025"
² DemandSage, "72 Retargeting Statistics & Trends of 2025"
³ TrueList, "Retargeting Statistics – 2024"
⁴ Cropink, "50+ Retargeting Statistics Marketers Need to Know in 2025"
⁵ Business of Apps, "Cost Per Click (CPC) Rates (2025)"
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