SAS for Acceleration of DIY Customer Propensity Analysis

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The content shared in this article for the application area of propensity analysis represents an exciting opportunity to showcase technology and approaches across SAS Customer intelligence 360 and SAS Viya for different profiles of users (marketers, analysts and data scientists) in the context of customer analytics. While we attempt to maximize diversity, it should be noted a reasonable, non-exhaustive number of examples will be shared.

For readers interested in recent efforts by SAS to deliver automation of DIFM propensity analysis for our marketing software's users, please go here. This specific article will extend on automation with examples where SAS is accelerating analyst and data scientist workflows with the objective of performing customizable DIY propensity analysis.

To begin, SAS provides functionality without constraint through a no/low/high code software experience. Across the user spectrum, SAS enables DataOps, ModelOps & Customer Experiences. This includes, but is not limited to, data access, preparation, exploration, reporting, machine learning, AI, model management, decisioning and multi-channel journey orchestration. Our promise is to help users overcome business problems by gaining deep customer knowledge that extends to action by seamlessly enhancing the activation of customer data. 

In the context of customer propensity analysis, SAS strives to provide:

• Algorithms that produce better analytic scores and accuracy
• Automation of machine learning that aligns with greater productivity
• Embedded analytics, making AI more impactful and consumable
• Human-like interfaces, creating approachability
• Trust, which is critical when using propensity scoring effectively

These concepts focus on how information & derived insight are used to make intelligent decisions regarding customer treatments, targeting and personalization.

Image 2: Customer propensity analysis principles in SAS

For every question a senior leader poses to their broader team, SAS delivers decision-oriented solutions that accelerate the timetable to actionability, as well as customizable modeling recipes and patented procedures that optimize the in-house AI talent your brand employs. Let’s gently walk through these capabilities through two demo examples.

The first demo will focus on a feature entitled Automated Prediction. This analysis object runs several models on a response variable (or business outcome) that the user specifies. After specification, the remaining data items the user has access to are automatically added as underlying factors for auto-model construction. A champion model is chosen by the software, and the model prediction and the underlying factors are displayed. In other words, the software auto-builds an interactive summary report.

It is a frequent occurrence that SAS users desire performing propensity-based analysis through data visualization and interactive exploration. In other words, functionality that supports no- and low-code user profiles who benefit from a hybrid of automation and customizable features in completing their analyses. But enough chatter, let's get on with the show!

Demo 1: DIY Customer Propensity Analysis Using Automated Prediction

How can we improve our conversion rate going forward? This is the question of the decade for analysts and data scientists, and I do not view your leadership team changing their interest in this topic any time soon. You can report, slice, dice, and segment away in your analytics platform, but needles in haystacks are not easily discovered unless we adapt. I know change can be difficult, but allow me to make the case for AutoML and hyperparameter tuning within the discipline of customer propensity analysis. A trendy subject for some, a scary subject for others, but my intent is to lend a practitioner's viewpoint.

Automated machine learning (commonly referred to as AutoML) involves automating the tasks that are required for building a predictive model based on machine learning algorithms. These tasks include data cleansing, feature engineering, variable importance, model selection and hyperparameter tuning, which can be tedious to perform manually. Platforms that provide this capability offer many benefits, such as empowering analysts by giving them a start at a machine learning workflow, as well as allowing data scientists to spend less time on model design and more time on making an AI-enhanced marketing campaign a reality.

Automation is not intended to replace the role of data scientists; ideally, there should be support for intervention in these systems to allow the performance of tasks such as domain-specific feature engineering, which can be a critical component of improving the performance of predictive modeling. These systems should be transparent with regard to the algorithms being used, so that users can be aware of, understand, and trust the insights being generated.

SAS provides different levels of automation that can be included in the machine learning pipeline-building process. Users can do any combination of automated tasks, such as having the system determine variable roles and levels, create the best transformation for numeric features, generate new features, and more. Alternatively, the entire process can be automated, through a graphical user interface as well as using a REST API. 

With that said, I invite you to view a second demonstration that will address AutoML and hyperparameter autotuning with data captured and contextualized from SAS Customer Intelligence 360.

Demo 2: DIY Customer Propensity Analysis Using AutoML and Hyperparameter Autotuning

We look forward to what the future brings in our development process – as we enable marketing technology users to access all of the most recent SAS analytical developments. Learn more about how SAS can be applied for customer analytics, journey personalization and integrated marketing here.