Leveraging Propensity Models: Marketing that Thinks Like Your Customer

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In today’s digital landscape, personalized marketing is crucial for maximizing customer engagement and conversion rates. Propensity scores help marketers understand consumer behaviours, determine the likelihood of performing certain actions, or even the likelihood of interacting with specific communication channels. By leveraging Adobe Real-Time Customer Data Platform and its Customer AI feature, businesses can enhance targeting and optimize marketing campaigns. This helps in achieving the following outcomes:

Traditional marketing strategies often rely on event/intent-based targeting without considering a customer's preferred channel. This can lead to overcommunication across multiple channels, resulting in customer fatigue and lower engagement rates.

As part of the digital journey, users often switch between different browsers, and each browser generates its own Experience Cloud ID (ECID). This creates multiple ECIDs for the same customer, making its interactions stitched across different touchpoints.

Customer AI models operate at the touchpoint level, capturing and analyzing data from various customer touchpoints. These models assess customer interactions across multiple ECIDs to generate propensity scores that predict the likelihood of engagement through specific channels.

It is possible that a single profile can have multiple ECIDs, and it is important to unify the touchpoint of customer-to-single ECID. The Data Distiller offering, available in Adobe Experience Platform, enables users to execute PostgreSQL queries to curate data within the platform’s data lake and prepare it for use in platform-based applications. It helps resolve fragmentation issues caused by multiple Experience Cloud IDs (ECIDs) per user, which leads to incomplete customer profiles, inconsistent propensity scores, and inefficient marketing efforts.

Introduction to Adobe Real-Time CDP’s Customer AI feature & Adobe Experience Platform Data Distiller

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Adobe Real-Time CDP integrates data from multiple sources to create a unified customer profile. The Data Distiller offering consolidates customer events into one ECID by running Distiller scheduled SQL jobs. The Customer AI feature enhances this by generating propensity scores, predicting customer behavior, and enabling personalized marketing.

Key Features of Customer AI in Adobe Real-Time CDP:

Other key technical capabilities of Customer AI include:

Key technical challenges in propensity generation

Key technical challenges are:

Additional technical challenges include:

The solution lies with Adobe Experience Platform Data Distiller, which standardizes and unifies data in the datalake, allowing Customer AI to generate a single propensity score per customer profile, ensuring accurate and efficient targeting.

How does Customer AI in Adobe Real-Time CDP generate marketing preference?

Customer AI leverages machine learning to predict the most effective communication channel for each customer.

Process Flow:

    from
  	(
    	select
      	identitymap['ecid'] [0].id as PrimeECID,
      	explode (identitymap['ecid']),
      	identitymap
    	from
          profile_snapshot_export_e13c0e86_da84_4f2a_abe3_73554c45f0b9
  	)
  ) profilesnapshot
  inner join customer_digital_platform_dataset on profilesnapshot.ecid = identitymap['ECID'] [0].id
where
  timestamp <= current_date and timestamp >= (current_date + interval '-

Channel propensity: The following parameters are set while creating the Customer AI models from an event dataset, with eligible population being records where utmmedium contains "sms”, “email”, “whatsapp”:

These models generate datasets with propensity scores for each channel:

  1. customer_ai_scores_channel_propensity_sms
  2. customer_ai_scores_channel_propensity_email
  3. customer_ai_scores_channel_propensity_whatsapp

Technical deep dive: The machine learning models employ gradient boosting with XGBoost and handle class imbalance through various sampling techniques. Feature importance is calculated using SHAP values, enabling explainable AI outcomes.

This classification helps marketers deliver targeted campaigns through the most effective channels.

Data flow from Adobe Real-Time CDP to Adobe Campaign

Seamless integration between Adobe Real-Time CDP and Adobe Campaign ensures propensity scores are leveraged effectively for targeted marketing campaigns. The enriched profiles with propensity scores are sent to Adobe Campaign, which uses these scores to deliver messages via preferred channels, ensuring higher engagement and conversion rates.

Architecture diagram

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The high-level architecture of the Channel Propensity Model ensures a seamless flow from data collection to campaign execution, enabling real-time decision-making and personalized marketing.

These channel propensity-based audiences can be further strategically extended across other Adobe platforms and third-party platforms to enhance personalization, reduce marketing waste, and improve return on investment.

This data-driven approach not only enhances the relevance of communication but also helps reduce campaign costs by minimizing inefficiencies, ultimately driving higher conversion rates and greater marketing ROI.

Troubleshooting Common Issues: Low model accuracy (AUC < 0.65)

Future scope & enhancements

Personalized marketing is evolving fast. With AI, brands can tailor not just timing and channels, but also tone, visuals, and content to match individual customer traits and life stages. By orchestrating messages across inbound, outbound, and offline touchpoints—using both behavioural and contextual data—marketers can drive deeper engagement. Adobe Real-Time CDP empowers this next-gen personalization. Reach out for a demo.

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Aradhana Padhiary and Parth Hetamsaria also contributed to this article.

Special thanks to Pawan Sevak, Arava Sai Kumar, Sameeksha Arora, and Alyssa Espiritu for their valuable review and contributions to the tech blog.