SAS for DIY & DIFM Customer Segmentation
The content shared in this article for the application area of segmentation 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.
(Image 1: Technology user personas)
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.
(Image 2: Transform knowledge to action)
In the context of customer segmentation, 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 segmentation effectively
These concepts focus on how information & derived insight are used to make intelligent decisions regarding customer treatments, targeting and personalization.
Image 3: SAS for customer segmentation
Moving on, if the marketing & customer analytics market were a soccer game, we'd be in the second half. The first half was dominated by general-purpose toolkits that data scientists used to build custom models from scratch. Due to the scarcity of these data scientists, the martech ecosystem saw the emergence of solutions with prepackaged analytics geared towards marketers. Now we are in the second half of the game. The commoditization of many core analytical capabilities is well underway. But the game is far from over. SAS recognizes the critical importance of serving multiple enterprise personas.
This spectrum ranges from business users who want out-of-the-box analytics to savvy analysts who want to build models from scratch. It is extremely challenging for any brand or supporting vendor to predict if a do-it-yourself (DIY) approach vs. a do-it-for-me (DIFM) approach will be more effective. SAS constantly observes, accepts and uses this challenge to inspire our software’s design principles to enable capabilities to reflect the balancing needs between marketers, analysts and data scientists within an organization.
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 four demo examples.
The first demo will focus on a feature entitled Segment Discovery which supports SAS Customer Intelligence 360's testing capabilities. For example, when users desire to leverage A/B testing, they are ultimately comparing the conversion rates of two or more versions of a creative/message by tracking customer response rates. SAS adds value by auto-analyzing and determining at the conclusion of the test which variant led to the most conversions. In addition, SAS analyzes metric data to determine whether there are sub-segments of your target audience that respond better to a challenger variant rather than the winning variant.
Demo 1: DIFM Customer Segmentation Using Segment Discovery
It is a frequent occurrence that SAS users desire a segmentation solution that supports the analysis of customer, interaction, and contextual data through data visualization and exploration. In other words, users require features that support no- and low-code user profiles who benefit from a hybrid of automation and customizable functionality in completing their analyses. A few value propositions that we will demonstrate in Demos 2 and 3 below include:
- Single-click auto-explanations which expose predictors & results for a customizable business outcome (or target variable) using C4.5 decision trees including the generation of relationship plots & high/low likelihood segments.
- Auto-unsupervised learning which determines the optimal number of clusters when performing K-Means by using the ABC method systematically.
- Parallel coordinate plots to provide users transparent interpretability in understanding segment profiles as well as portraying data stories for business presentations.
Demo 2: DIFM + DIY Customer Segmentation Using Automated Explanation
Demo 3: DIFM + DIY Customer Segmentation Using Visual Clustering
Across the supervised segmentation spectrum, data scientists have been pushing the boundaries with forests, gradient boosting, neural networks and other algorithms. However, practical internal business processes within brands frequently require typically require segment definitions through rules and analytical scoring. Let's explore a new user feature in SAS called Interactive Decision Trees for users who prefer the customization and power of model pipelining yet require flexibility to support their brand's unique segmentation deployment requirements.
Demo 4: DIY Customer Segmentation Using Interactive Decision Trees
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.
