Introduction to SAS 360 Marketing AI (Part 1)
Marketers, advertisers and data-driven brands face growing challenges in making sense of complex data to drive actionable insights. This article introduces recent SAS development efforts to release a solution-oriented software application to this cited gap, offering prescriptive recipe-oriented experiences to address trending use cases for B2C (and B2B) brands.
The last few years have showcased the world's insatiable (and growing) appetite in how data science and AI can bring forth incremental value across every imaginable industry. Given this momentum, the martech ecosystem is a wonderful space for SAS to innovate within. Our intent is to create synergy improvements between marketers and data scientists while elevating self-sufficiency in running analytics at scale through use case-driven solutions that package the best of SAS capabilities in a simple-to-use interface.
In this article:
- Introduction to SAS 360 Marketing AI
Forthcoming in Parts 2 and 3 of this article series:
- The Role of the Data Person and Configuring Actionable Recipes
- The Role of the Business/Marketing Person and Leveraging Projects
Brands aspire to strategically manage their business through prioritizing customer convenience. This involves anticipating and responding to customer needs, while manifesting in proactively delivered, seamless, and unobtrusive interactions. The intent is to provide personalization, assistance and valued services.
However, there is a little secret in the customer analytics ecosystem that practitioners frequently will admit to when pressed for honest feedback. A massive proportion of customer & marketing analysts continue to skew towards the wrong end of this workflow spectrum:
"I spend more than 80% of my time preparing data, and less than 20% actually performing analysis."
For some us, that quote never changes, year after year. It's finally time to do something about it. Speed bumps like this usually emerge when customer experience teams require advanced insights for propensity scoring, algorithmic segmentation, retention strategies or next-best-actions. Those who have experienced this have witnessed firsthand when working with (1st, 2nd or 3rd party) data extracts that they are typically not formatted for machine learning or AI use cases, and the time-to-value expense becomes heavily negative in the technical efforts to re-engineer that information.
Accelerating through this challenge has been a key area of interest at SAS, and the development of 360 Marketing AI has been influenced by this theme. But there is more to efficiently delivering analytically-driven value downstream to teammates involved with customer experience management. It isn't just about getting customer data ready for modeling, and also involves domain expertise & applied use cases. Think about the magnitude of requests that come in from customer experience and marketing teams to their supporting analysts.
The list of viewpoints could go much longer, but as many readers recognize, the point remains the same. Customer experience management has an insatiable appetite for data intelligence. This myriad of desires stratifies further when considering industry context. For example, retail brands commonly desire to optimize their app's shopping experience and increase the lifetime value of mobile users. Alternatively, financial service brands want to deepen customer relationships and improve stickiness through recommendation systems for upsell opportunities. Finally, entertainment brands with subscription services obsess about retention (or churn), identifying meaningful friction points within customer journeys, and alter strategic treatments on minimizing these customer events.
At SAS, we believe marketers should spend more time shaping strategies and less time wrestling with data and tools. Yet for many teams, turning customer data into insights, recommendations, and optimizations has remained a highly technical process. Building models, preparing data, and interpreting results often required specialized expertise --until now.
SAS 360 Marketing AI is our latest software module offering that will be part of the broader SAS Customer Intelligence 360 SaaS solution offering. The objective of releasing this new application is to enable users with set of capabilities reimagined with the following modern benefits:
- User experience
- Streamlined interfaces that reduce clicks and make everyday tasks faster and more intuitive.
- Smart defaults and guidance that help users get started quickly without needing deep training.
- Clean, simplified workflows designed to minimize friction and support user goals.
- Streamlined interfaces that reduce clicks and make everyday tasks faster and more intuitive.
- Composability & flexibility
- Access data where it lives to eliminate duplication, preserve accuracy, and enable real-time insights.
- Seamless integrations with existing MarTech tools ensure we complement—not replace—your ecosystem.
- Open APIs and connectors empower teams to build custom solutions and extend capabilities as needed.
- Access data where it lives to eliminate duplication, preserve accuracy, and enable real-time insights.
- Agentic AI
- Intelligent collaborators working alongside users to streamline tasks and automate actions.
- Embedded within the full user experience spanning across use-case recipe configurations and projects.
- Continuously learn to deliver more personalized and proactive support.
- Intelligent collaborators working alongside users to streamline tasks and automate actions.
Let's share a visual summary of how SAS 360 Marketing AI will fit into the broader SAS Customer Intelligence 360 portfolio.
Transforming marketing teams into analytical factories is a bold vision we challenged ourselves to innovate for. Numerous well-known martech vendors have attempted to aspire to this vision over the years. In the world today, there are a large volume of marketers, moderate amount of analysts, and a smaller subset of data scientists. Generally speaking, the theme at major martech vendors has been to automate analyses on behalf of marketing users using templates to provide AI insights while masking/hiding the manual workflow steps. While this can provide benefits in regard to perceived speed-to-market acceleration, the auto-analysis behind these templates typically do not offer customization features to conform to a brand's unique business model. The data science community understands incremental opportunity is being left on the table with solutions like this.
This trend has resulted in a compelling insight for us at SAS, and a deep exploration of the Marketing AI landscape has resulted in the realization that there is a different way to approach this emerging paradigm.
SAS recognizes the critical importance of serving multiple enterprise personas through augmentation (for example, embedded Agentic AI and machine learning to assist users). This spectrum ranges from business/marketing users who want out-of-the-box benefits to savvy analysts and/or data scientists who want to build assets 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, as well as improve team member interactions with one another.
Let's pause for moment, and envision how a typical business meeting involving the collection of campaign requirements from the marketing team is explained to the supporting analyst/data science team. Does it go smoothly? Well, it matters depending on how well each side understands and appreciates the other side.
The language of marketers and customer experience is rooted in use cases and outcomes. Domain expertise, acceleration and simplifying the process of analytically injecting data-driven intelligence into marketing workflows is the desire, and year after year, SAS clients share feedback on this challenge.
For example, the marketing and CX teams responsible for the tactics between a brand and everyday consumers speak one language. The data science and analyst groups likely speak another. Terms like acquisition, cross-sell, churn, targeting, personalization, A/B tests, conversions, and impressions are the common tongue of the martech universe. Alternatively, words such as misclassification, precision, average squared error, confusion matrices, outliers, auto tuning, neural networks, and random forests represent the language of data science.
In other words, marketers do not typically think in terms of algorithms, and analytical jargon creates confusion, friction and inefficiency for those not trained in the discipline. This can be intimidating for many working professionals, and why data and analytical literacy across the enterprise is increasing in relevance. If this is what the martech community craves, this is a call-to-action to my brothers and sisters practicing data science across all industries.
You want to see your analytical assets bring rewarding impact to your brand, right? You want to observe your efforts making a significant positive difference in customer journeys, correct? Then the democratization of marketing team enablement via customer journey orchestration and prescriptive analytics benefits from speaking their language. Further, SAS AI development efforts targeting the martech community is to bring forth software and technology that removes technical jargon and adoption intimidation.
It’s no surprise that senior executives are prioritizing technology, data, and digital strategies in their brand's budgetary spending plans. However, technology investment isn’t replacing people. Reducing headcount remains a low priority; in fact, forward-thinking organizations view technology as a way to enhance human capabilities, not replace them. Leading organizations recognize that success takes more than just adopting new tools.
The widening gap between the AI “haves” and “have nots” is especially visible in marketing, where teams face rising expectations. Accountability is intensifying as marketers face mounting demands to deliver more of everything in the future as compared to the past. Organizations are acutely aware of the need to bridge the gaps in their customer experience. But even with AI, delivering memorable experiences is getting tougher. Personalization is not just a name in a subject line; it’s about creating deep connections. Brands stand out and build loyalty when they successfully deliver relevance and recognition at the right moment.
A summary of the analytical challenges facing marketing organizations today is summarized below.
Let's start on the topic of resource limitations. According to recent research completed by Econsultancy, the qualitative question that caught our attention was framed as:
How fragmented or siloed data holds back personalization and impacts experience?
Looking past the real-time marketing desire, we were amazed by the following trends summarized in Image 11 below.
- 75% of the research population responded with challenges related to "unable to engage customers at critical moments."
- 73% of the research population responded with challenges related to "not knowing enough about customers to adequately personalize/tailor content."
The desire for insights related to "customer moments that matter" or "knowing more about customers..." is directly related to actionable analytical intelligence, scoring and activation. SAS views this as an opportunity to enable, not constrain, our client partners with impractical limitations.
The root of every analytical marketing use case continues to be hampered by data engineering, highlighting that CDPs have largely fallen short on their promise, and enterprise data management feature/functionality is necessary to overcome these data asset concerns. Our second focus area in rolling out SAS 360 Marketing AI relates to painful data preparation. It is frequently mentioned in the martech industry that brands must maximize the potential of theirs 1st party customer data sources. We believe that brands should treat all of your owned data assets (Zero, 1st, 2nd and 3rd party) as a priority.
The Econsultancy research explored this topic by asking:
What are the barriers to connecting customer data across functions?
The top two responses (summarized in Image 12 below) focused on:
- 64% of the research population responded with challenges related to "privacy, security and governance concerns."
- 48% of the research population responded with challenges related to "disorganized, poor quality or inaccurate data."
However, what was even more troubling were the response themes related to a lack of clear vision on how to use the data, poor understanding of the ROI of data, and customer information not seen as a strategic asset. SAS recognizes that data-driven marketing begins with the quality of the ingredients. It is when poor quality ingredients embed themselves in the marketing workflow that erodes leadership's understanding and/or trust of how valuable data can be.
The perception gap between consumers and a brand’s ability to meet execution expectations is increasing. Our clients frequently mention concerns about analytically-focused vendor partners with rigid data requirements in leveraging their services, or lack of customization support for a unique brand's business model. This is contributing to the inability in meeting consumer expectations, and widening the perception gap of what brands can truly be in their valued customer relationships.
The Econsultancy research keyed in on this gap between customer expectations and brand execution. Here are the top two insights (extracted from Image 13) we want readers to focus on:
- There is a 49% differential in consumers expectations and brand execution capabilities in the usage of "AI across imagery, content and/or recommendations."
- 37% differential in consumers expectations and brand execution capabilities in the "ability to anticipate consumer needs and actionability".
These are significant perceptual variances between what consumers expect, and the lack of readiness to activate intelligently as the brand. Flexibility, as opposed to inflexibility, is the only viewpoint SAS maintains to support our clients.
The software development shortcuts from external martech vendors to rush out inflated analytical solutions that don't live up to the advertised hype is exactly why SAS has strategically observed and crafted an alternative blue print plan in creating a domain-specific Marketing AI solution to solve common use cases - end to end.
Just look at how the Econsultancy research zoomed in on vendor inflexibility (Image 14):
In which of the following ways does your brand routinely personalize digital content for customers?
- 61% do NOT "use data and algorithms to personalize...".
- 58% do NOT "make recommendations based on previous purchase or browsing behavior".
- 53% do NOT use "data and analytics to predict customer needs by segment and/or persona".
Given we are living through a massive AI moment in the world, with a bright future ahead, SAS views these trends as easy to fix and improve. It's simply a matter of selecting the right vendor to enable your brand.
SAS 360 Marketing AI will address these challenges by focusing on four key themes:
- Use case driven: Provide a proactively guided approach to solving marketing-centric use cases.
- Self-service analytics: Enable business users and marketers to run analytics with minimal external support.
- Deploy anywhere: Run AI and analytical workloads against your data, wherever it lives, without requiring copying and synchronizing outside of your owned data environment.
- Streamlined projects: Accelerate and automate analytical projects through interactive workflow steps between marketers and analysts/data scientists.
The methodology of AI workflows has largely been a "best-practice approach" with specificity across the analytical lifecycle taught in academia and software vendor education/training programs. With that said, SAS is introducing a new approach for enterprises & brands to unlock value from beautiful, wonderful data.
The concept of recipes and required ingredients, which lives at the center of SAS 360 Marketing AI's design principles, can be outlined as:
Data – What data do I need?
Preparation – How does it need to be transformed?
Use-case specific – Applicable ML/AI algorithm(s).
Scoring - Segments, recommendations, propensities, etc.
Activation – Using the scoring in journeys and channels.
From a software user's perspective, our motivation at SAS is to create an experience that unites what is special and unique about data scientist and marketer talents. To achieve this, use case-driven solutions that proactively and prescriptively guide these two types of anticipated users is the intended vision.
Data person - Users who have previous experience managing, engineering or analyzing data assets.
Business/marketing person - Leverage recipes approved by "data person" to run no-code analytical projects at the velocity necessary to support customer treatment strategies and campaign activation cycles.
Our intention is to support every step of the marketing/customer analytics journey in an applied manner through functionality that will help with use case driven solutions that guide users through their challenges.
Introductory Demo Video
The use cases for marketers in every industry are expanding every day. We look forward to what the future brings in our development process – as we enable technology users to access all of the most recent SAS analytical developments.
As mentioned at the top of this article, readers should anticipate Part 2 (Data person - acute focus) and Part 3 (Business/marketing person - acute focus) of this series releasing soon. Until then, readers can continue to learn more about how SAS can be applied for customer analytics, journey personalization and integrated marketing here.
