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Advanced Analytics for SAP Marketing Cloud

10 min read


Advanced Analytics for SAP Marketing Cloud

In this article, you will learn about the advanced analytics terminologies, roles, and advanced analytics scenarios in SAP Marketing Cloud. 

Table of Contents

Overview About Advanced Analytics


In the area of Intelligent Marketing, there are many terminologies such as Advanced Analytics, Predictive AnalyticsMachine Learning or Artificial Intelligence. Sometimes these terms are used interchangeably, but in fact they are not synonymous. Let's review the terminologies that are used in this article. 

Based on their glossary and recent publications, Gartner defines Advanced Analytics as an umbrella term for a variety of underlying techniques. 

image one: Advanced Analytics Terminology

Within Advanced Analytics, there are three techniques or disciplines:

  • Predictive Analytics: Employs technologies such as statistical modeling or simulation
  • Prescriptive Analytics: Includes optimization, heuristics and rule-based expert systems following business rules
  • Artificial Intelligence: A subset of Advanced Analytics that includes machine learning, natural language processing, and cognitive advisors

SAP Marketing Cloud covers scenarios and features within the Prescriptive Analytics (Rule-based Scores/Score Builder) and Artificial Intelligence (Predictive Scores, Recommendation) areas.

Roles for Advanced Analytics 

To break down the complexity of Advanced Analytics, let's look at the typical roles within Advanced Analytics projects or sub-projects.

The business users are responsible to utilize the results to target consumers provided by business analysts. Typical examples are: 

  • Use predictive "buying propensity score" in segmentation to target consumers which are most likely to buy a specific product
  • Provide feedback on product recommendation model results to business analysts or data scientists

The business analysts use, adjust, and enrich analytical models based on standards typically provided by the data scientists. Some examples are: 

  • Fine-tune offer recommendation models to provide better results based on business user feedback
  • Select predictors for predictive score based on model quality

The data scientists or data analysts perform complex analytics and develop standards mainly for business analysts. 

  • Evaluate best algorithms for use in recommendation models (data scientist)
  • Support business analysts in fine-tuning of recommendation models (data scientist)
  • Build predictive scenarios including own data sets with pre-defined algorithms (data analyst)

image two: Advanced Analytics Roles

Advanced Analytics in SAP Marketing Cloud

The following table explains which scenarios in SAP Marketing Cloud can be mapped to which technique and role. 

Scenario Technique Involvement of Business Users Involvement of Business Analysts Involvement of Data Scientists or
Data Analysts
Rule-Based Scores Prescriptive Analytics (Rule-Based Expert Systems) Utilizing the scores for targeting consumers Building and fine-tuning simple or complex rule-based scores -
Predictive Scores Artificial Intelligence (Machine Learning) Utilizing the scores for targeting consumers Adjusting and fine-tuning standard predictive scores Developing custom predictive scores and predictive scenarios
Offer and Product Recommendation

Artificial Intelligence (Machine Learning)

Previewing recommendations and providing feedback on recommendation quality Adjusting and fine-tuning standard recommendation algorithms Developing custom recommendation algorithms

The next chapters cover these scenarios in more detail. 


Scores are essentially key figures or aggregated values that help to characterize or classify the interaction contact for different aspects. See the following example for a score on account level - the account engagement score. 

image three: Scores in SAP Marketing Cloud

In general, there are two types of scores in SAP Marketing Cloud - predictive (propensity-based) scores and rule-based (heuristic) scores. 

Rule-Based Score Predictive Score
Concept Based on rules and conditions Based on predictive modeling
Learning Best Practice: Rule sets are defined based on company policies, experience, and best practice. A rule set can contain several rules. Each rule consists of multiple conditions. Data-Driven: The predictive model is trained on historical data to detect patterns in customer behavior.
Score Value Calculation Calculated as aggregation of the outcomes from all valid rules. A rule is valid if all conditions are met. Calculated from trained predictive model
Target Role Business Analyst

Business Analyst or Data Scientist

App Score Builder Predictive Studio

Rule-Based Scores

As explained above, rule-based scores are explicitly defined by best practice. Some real-life examples for rule-based scores are:

  • E-mail Affinity  (standard)
    • This score represents the response affinity to the e-mail channel. The score is calculated by looking at the e-mail open rate and if an e-mail opt-in exists among others.
  • Best Push Notification Sending Time (standard)
    • This score helps to send push notifications at the right time. The score represents the "best sending time" by looking at the peak times when push notifications are most viewed by the contact.
  • Lead Quality Score  (custom)
    • A lead quality score can tell you how well you know your contacts based on data completeness. 
  • RFE Score  (custom )
    • A RFE score can help you to measure loyalty or brand value by looking at the three dimensions recency, frequency, and engagement. 

An article about RFE scoring for SAP Marketing Cloud is available here. This article explains in detail the use cases for an RFE score, and how such a score can be implemented and utilized in SAP Marketing Cloud.

All rule-based scores are built and provided to the business users through the Score Builder app. This app is explained in the following video: 

Predictive Scores

In a nutshell, predictive scores are trained through predictive models on historical data. The idea is to detect underlying patterns in the customer's behavior. The predictive models are then used to score interaction contacts. Some real-life examples are: 

  • Consumer Buying Propensity (standard)
    • This score indicates how likely it is that a consumer is going to buy a specific product. The score is calculated based on their interactions within a given time period among other attributes. 
  • Insurance Churn Propensity(custom)
    • Such a score can indicate which customers tend to cancel their insurance policy. This score is calculated based on customer master, product and interaction data within a given time period. 

All predictive scores are built through the Predictive Studio app. The following video will help you to understand how to use this app. 


Recommendations allows you to provide consumers with relevant recommendations for offers and products across multiple channels. Business analysts can work on recommendation models based on standard recommendation algorithms or on custom algorithms provided by data analysts or data scientists. Recommendations are always provided for a certain context. For example:

  • For specific consumers (based on preferences or order history)
  • For certain products (such as the products currently viewed or in the shopping cart) 

image four: Recommendations (in bottom section)


  • Recommend products often bought together with the products in the shopping cart (standard)
  • Recommend more expensive products from the same product category (custom)
  • Recommend eligible offers based on leading products (standard)
  • Re-rank offers based on expected margin (offer)

An article about custom recommendation algorithms for SAP Marketing Cloud is available here. This article explains in detail the use cases for custom recommendation algorithms, and how such algorithms can be implemented and utilized in SAP Marketing Cloud.

The following video shows you how you can integrate SAP Marketing Cloud with SAP Commerce Cloud to provide product and offers recommendations for a personalized shopping experience. Product recommendations are discussed after 3:12 and offer recommendations are discussed after 6:41: 


This article introduced you to advanced analytics for SAP Marketing Cloud. Now, you should have an overview about the terminologies, roles, and advanced analytics scenarios in SAP Marketing Cloud. 

The articles below will help you dive deeper on advanced analytics topics: 

If you are interested in learning more about our services for SAP Marketing Cloud click here.