Introduction to SAS 360 Marketing AI (Part 2: Project User Experience)
AI and/or analytical marketing is the process of using capabilities like data collection, data-driven analysis, natural language processing (NLP) and machine learning (ML) to deliver customer insights and automate critical marketing decisions. Today, AI technologies are being used more widely to generate content, increase team efficiency, improve customer experiences and deliver more accurate results.
With the increasing utility of Generative AI (i.e. GenAI), marketing teams use the technology to instantly create hyper-personalized marketing assets, generate insights from customer data and iterate tactical improvements on existing strategies. Given the vast amounts of multi-touchpoint data processed by brands, and the value of leveraging that data, AI adoption is increasingly critical for those that want to remain competitive.
With that said, I want to be crystal clear. There is much more to the AI discipline than GenAI. As everyday people prompt away hunting for clever new solutions to their use cases, or as brands assess, experiment and/or deploy agentic strategies to improve their business model, the level of autonomy of AI should and will vary by task, risk level and business context. It is critical that brands are empowered to adopt and leverage AI for marketing-centric use cases, while minimizing friction and disruption.
And one more thing...don't forget a Composite AI strategy that augments all the recent excitement of GenAI, agents & agentic orchestration in this new world. The analytics landscape has evolved significantly during the past decade. Many organizations have progressed from basic statistical modeling to machine learning, and some have added deep learning to their toolkits as well. In this context, the emergence of GenAI — with its ability to create humanlike text, generate images, and write code — introduces new possibilities and questions.
While GenAI promises to revolutionize everything from customer service to product development, its optimal role alongside predictive AI tools (that is, machine learning, deep learning, etc.) remains a work in progress. That often leaves leaders asking what the right approach is for addressing a particular problem. The choice between predictive analytics, machine learning, deep learning, and GenAI should not be viewed as an either-or proposition but as a set of capabilities that can be mixed, matched and tailored based on the specifics of the problem at hand. Looking ahead, the boundaries between these methods will likely continue to blur as new capabilities emerge.
How can a brand leader decide which AI solution to use for a given problem?
Let’s assume that the problem has been clearly defined, relevant inputs have been identified, and the desired output has been specified. A logical starting point is the nature of the problem: Is it a prediction problem or a generation problem?
Generation problems are easy to identify. If the desired output is unstructured — such as text, images, videos, or music — it is a generation problem.
Prediction problems come in two varieties: classification and regression. In classification problems, given an input, the user needs to make a choice from a set of predefined outputs. For example, given data about a customer, a marketer may want to predict whether the customer is at high, medium, or low risk for a retention/churn use case. The key here is that the output categories — high, medium, and low risk — are predefined by a human or unsupervised learning (think clustering), not generated on the fly.
In regression problems, you want to predict a number (or a few numbers). Given data about a customer and engagement details, a marketer may want to predict what their risk level for churn will be six months from now. Or, given past sales data for a product, an organization may want to predict its sales units for the next 24 hours. Note that the distinction between classification and regression can be somewhat fuzzy. For example, regression problems can often be framed as classification problems. With the nature of the problem identified, we can turn to which tool or solution to use.
Let’s start with an easy use case. If you have a generation problem to solve, there’s only one game in town: GenAI. Depending on the sort of output you want to generate, you may need to use multimodal LLMs from services offered by OpenAI, Anthropic or Google Gemini. If you have a prediction problem, however, matters become more complicated.
The most straightforward scenario is when the input data is all tabular. In this situation, you should favor traditional machine learning. While deep learning can also solve these problems, it brings a host of other burdens that may not be worth the effort: It may require more effort to “tune” the model to the problem, the model may not lend itself to managerial interpretability due to its black-box nature, and so on. In contrast, machine learning models are much quicker to build and tune and require less “babysitting,” and interpretable methods are available.
By choosing machine learning over deep learning, you are not necessarily settling for lower accuracy in exchange for ease of development. Certain widely used machine learning methods (like Gradient Boosting) are not only easier to work with than deep learning but also can be more accurate, at times, for tabular data prediction problems.
SAS 360 Marketing AI - Why now?
A few months ago, we shared a preview of SAS 360 Marketing AI. The article introduced SAS development efforts to release a solution-oriented software application offering prescriptive recipe-oriented experiences to address trending use cases for B2C (and B2B) brands. For readers unfamiliar with the term "recipe"...
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.
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 other words, SAS is introducing AI and advanced analytic capabilities FOR marketing use cases acutely. For a moment, reflect on the idea of a software application that is:
- Designed for the domain space and themes of martech.
- Focuses on use cases (i.e. recipes) while minimizing adoption friction related to statistical jargon frequently misunderstood by anyone outside of the data science profession.
- Uses the best of both worlds - GenAI blended with best-practice machine learning, predictive & segmentation capabilities in a no-code rapid-scoring mechanism that seamlessly integrates with the broader SAS CI360 solution, or external 3rd party martech tools.
As we continue to partner, guide and help find incremental value with our customer partners, SAS realizes the time in NOW to release a solution that bridges all of the power and innovation of prediction, machine learning, and GenAI together. Let's transition and outline how SAS 360 Marketing AI will benefit and be used by marketers (as well as other similar personas).
SAS 360 Marketing AI: Project User Experience
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 reimagined capabilities and features with the following 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.
The remainder of this article will focus on the project user experience depicted on Image 5 above towards the right-side of the screenshot. 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.
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.
SAS 360 Marketing AI will enable our partnered customers 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.
From a marketer's perspective, our motivation at SAS is to create an experience that unites the precision of data science with creative marketing that resonates. 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.
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.
Projects are the transition from a configured recipe (which we will release a detailed article on in the near future) to unleashing the opportunity for marketing teams to train and activate on data-driven scores. The intent is for SAS to reduce the complexity and automate as much of this process as possible. For this section, we will utilize both screenshots and a demo video at the end to bring the project user's experience to life for readers.
The first screen marketers we will observe is an auto-generated dashboard which includes an overview of projects and scores. It summarizes projects that are active, trained, in-design or failed. In addition, users have co-pilot support.
An exciting aspect of the co-pilot's design in SAS 360 Marketing AI to highlight is the dual value proposition it provides for asking questions, as well as agent support for creation. When users select to enable GenAI, the following benefits blossom:
- Users can leverage the Copilot to get answers to questions while in the software application. The copilot uses the application’s documentation to source answers.
- Users can create scheduled journeys (the activation layer once AI scores are generated). If this captivates your interest, check out a short, interactive demo here to learn more.
- For readers who are familiar with the broader SAS Customer Intelligence 360 solution, users can find information about tasks, audiences and journeys. For example, you can ask the agent to tell you the average runtime of a bulk task or which audiences are active.
Users can select a recipe to derive a new project, or access an existing project. What I want readers to take note of here is the language being used in the software's design. This is the first time in the history of SAS that we're building a domain-specific AI solution for martech and customer experience. It's never been done before, but we’ve seen year after year the friction building up between the data science and marketing communities. The marketing community is very data- and insight-hungry when it comes to activation use cases.
We hear you. This is our intent. To build software that speaks a language that both marketers and data science understand, while building team-oriented synergy to elevate customer go-to-market strategies.
After recipe selection for a new project, users can add personalized information to conform to the brand’s strategic preferences. Users will have control on customizing project names and descriptions. As for generating customer analytic scores, marketers can select the "look-back" window of the data used that contains the correct definition, population, and time frame for their use case. Lastly, users can be proactive alerted regarding various project statuses before proceeding to the next workflow step.
The next step leads us to an exciting and unique feature in the software.
Analytics and data science do NOT need to be constrained, and should be applied to what MATTERS to the brand. As marketers, what do you care more about in the context of acquisition, segmentation or retention?
- Balancing the effort to save existing customers at risk of leaving while avoiding unnecessary retention actions.
- Optimizing your strategy to ensure retention acquisition generate the highest net profit.
- Ensuring the predictions to target/not target are correct as often as possible.
- Drive the highest total revenue.
- Maximizing precision for engaging segments for personalization strategies.
Guess what? SAS offers choice to select the strategy that aligns with your brand's preferences. This is a wonderful enhancement that transforms a user's thinking to shift from probabilities and likelihoods to maximizing net profit or revenue.
Regardless of industry type, SAS believes CMOs and CFOs likely care more about monetary returns which improve the state of the business. Moving on, project users can perform analytical scoring by use case on-demand, or in a scheduled manner.
The important takeaway for readers is the project user is benefitting from the configuration of the use case recipe prior to accessing the project, allowing them to quickly train a scoring solution without technical friction or distraction. Optimizing models for real business impact—like revenue, retention, and profitability - has never been easier or faster.
Speeding up analytical workflows with ready-to-use (yet customizable) templates for common marketing challenges is the objective to ensure insights never get overlooked again due to the rapid velocity of martech requirements that exist today.
As training completes, the project experience receives a number of auto-benefits. When a user receives an indication that project training has completed, the software's left menu pane confirms a series of automated outputs are now available for review. Let's discuss why this matters.
- Natural language generated explanations and co-pilot helps assist throughout the user's experience to help accurately understand the output (to reduce any possible intimidation issues that prevent business adoption).
- Interactive audience cutoff selection for inclusion/exclusion prior to marketing activation.
- Modeling results summary and key performance measures to increase the confidence of the user before campaign activation occurs.
- Data science technical terms like accuracy, precision, recall and specificity are explained in common business language to help users understand, appreciate and make better outcome decisions.
- Assessment plots and butterfly charts to gain transparency on which data signals (or predictors) matter (both positive and negative correlations) in accordance with a brand's defined recipe objective.
- Confirmation if the trained solution will generalize as expected, alleviating concerns associated with under- and over-fitting models.
- Fairness and ethical AI checkpoints to alert users if bias and/or other concerns exist.
Let's discuss an acute example. Frequently, an area of friction between data science and marketing that we observe with our customer partners relates to the topic of model over- and under-fitting. The challenge is rooted in technical jargon, and requires practitioner experience in analytical modeling to appreciate. We saw this as an opportunity to simplify!
After reviewing the screenshot above, readers will observe naturally-generated language produced by the software attempting to explain why this matters, and why it is important a "passed" decision was disclosed. Now, we know our data science brothers and sisters will ask about transparency when dealing with these matters. So, let's go ahead and show an example (screenshot below) where the software allows users, if they desire, to fundamentally understand why a singular customer gets a model score assigned to them.
Not only does the software generate auto-visualizations, but AI-helpers add incremental value to ensure user interpretation is accurate. There is a theme we hope readers are detecting in the area of simplifying the data science workflow. Now, let's pivot and review a walkthrough of the eligibility, scheduling and output user interface screens.
Once again, let's revisit the question of why this matters. Interactive eligibility provides two key benefits involving customization of the inclusion/exclusion filtering of customers who will (or will not) be part of an activation-oriented audience, as well as the assistance of the software to proactively prescribe selection recommendations.
Finally, the output screen examples shown provide users the ability to select from a variety of destinations within SAS Customer Intelligence 360 and external martech vendors, such as Amazon Redshift/S3, Microsoft Azure SQL, Google BigQuery, Oracle, Salesforce, Adobe and Snowflake.
Let's walk through the steps of setting up an Audience (SAS Customer Intelligence 360 term), and why this matters to project users. After creating analytical scores in the context of your use case, what is the next step?
Analytical scores without a vision for activation and go-to-market strategies are essentially constrained from providing optimal value to your brand. Those scores need to be paired with other customer profile attributes. The question users must answer is:
Where do my analytically-scored customer profiles need to be available? In SAS or externally in a 3rd party solution?
We will proceed to select SAS 360 Audience to showcase how users can add personalization attributes and append with the analytic scores the software is associating with each customer.
Finally, the last step of setting up an Audience for activation allows users to specify additional information related to context, touchpoint communication requirements, properties, and more.
Once users complete these steps, Audiences become the first ingredient in setting up campaign targeting and journey orchestration within SAS. In the screenshot below, readers can see the Audience named Customer Tier - Gold as the first node of this customer journey use case. Whether it is an acquisition, upsell or retention use case, any type of recipe-oriented use case can be activated seamlessly within SAS Customer Intelligence 360.
Now, there's one more thing to touch on, and it's extremely vital from our perspective that the themes of AI governance, transparency and explainability blossom for users within SAS 360 Marketing AI. Project(s) can be activated with the support of monitoring vital metrics to ensure the analytical solution is fresh (not stale), and predicting with optimal accuracy.
The reasons SAS built this functionality into a Marketing AI solution's user workflow includes:
- The software will guide and prompt the user with recommendations (while still allowing interactive customization).
- Metric monitoring supports automation on when to alert users of decaying health of the trained solution and provides data-driven evidence when the scores needs to be revisited/refreshed, as well as when fairness becomes an issue and should be considered for mitigation.
Marketing teams will hopefully agree that using predictions and segments that are out-of-date and making unexpected mistakes on treatment recommendations will negatively impact KPIs. Scoring insights and dashboards will auto-populate on behalf of users when activation takes place, and labeled data refreshes to support scheduled (or alerted) training updates for the project solution.
Again, we at SAS challenged ourselves on reimagining how to simplify how the user sets up this automated monitoring. For those unfamiliar with this family of metrics, we have defined them below.
- Performance metrics
- Accuracy: In essence, it represents how often our predictions are right. In other words, it monitors how often we correctly predict both event levels of a classification use case, such as why customers choose to buy vs. not buy.
- F1 Score: Monitors the balance between accurately identifying customers who purchase (or churn) while avoiding unnecessary marketing tactical actions. This helps brands limit wasting resources while keeping marketing strategies effective.
- Precision: Monitor how often the model correctly identifies your desired event and/or objective. For example, in a churn use case, this metric tracks how many customers that we predicted to take on this behavior, actually churn. The intention is to assist brands in saving time and resources by ensuring that your retention actions are targeted at actual churn risks.
- Recall: Ensures your brand captures as many acquisitions/upsells/churners as possible, even if some were not going to follow that desired behavior. This is sometimes described as "catching as many fish as possible", while raising our tolerance for a higher threshold of errors.
- Overfit: Compares the model's performance on new data with its performance on data that it learned from. This ensures that the model performs consistently across new and trained data so that predictions remain reliable.
- Fairness metrics
- Performance bias: Measures bias in performance among different groups (or segments). A higher value indicates stronger bias. If a fairness attribute has multiple groups (like small, medium, large or bronze, silver, gold), we measure bias as the largest difference in performance between all groups.
- Predictive Bias: Represents how much greater the model's probability to predict the event is for one group over another on average. A higher value indicates stronger bias between groups. In cases where a fairness attribute has multiple groups, we measure bias as the largest difference in average predictions between all groups.
Monitoring fairness prevents biased targeting of specific groups. This helps ensure that your marketing efforts are trustworthy and effective for everyone. Let's gently walk through the user's workflow.
That's the beauty. The user is proactively given recommendations of what to monitor for, driven on your brand's data. Take note, AI helpers continue to provide assistance and explain why these metrics matter. If a user disagrees with the suggested thresholds, they are free to adjust to their preference.
Once the user is happy with the settings, they can activate this project in a single-click. Once activated, automated dashboards and scoring insights come to life to provide trending updates.
For example, if a user observed a negative trend for model score accuracy, this would be a sign (or reason) as to why the model may need to be retrained and refresh the customer scores.
From a AI fairness perspective, many readers realize AI (and the broader analytics discipline) never attempts to be correct/right 100% of the time. Humans (or customers) do not behave rationally all the time, and algorithms can make errors. As we become more comfortable with AI use cases maturing in everyday life, we at SAS recognize the importance of keeping our software's users aware when AI may be doing something unintended. This is where predictive & performance bias enter the lives of marketers. If we care about topics like brand health and/or integrity, then AI fairness also matters at this intersection.
The Middle Is Where Good Gets Great
The hardest part of marketing isn’t getting started. It isn’t crossing the finish line, either. It’s the messy middle – that critical space between ideas and outcomes. The place where data meets decisions, where strategies either connect or collapse. The middle is where audiences come together. Where channels align. Where stories stop being plans … and start becoming experiences. But the truth is, the middle doesn’t always get the attention it deserves.
It’s where good intentions get stuck. Where disconnected tools slow the momentum. Where silos, spreadsheets and scattered signals make the work harder than it has to be. The middle is not where things should fall apart. It’s where everything should
come together.
That’s the focus of SAS Customer Intelligence 360 – built to power the middle. Designed to connect data, decisions and delivery. Engineered for marketers who are ready to move from messy to magic. Because the middle matters. And when the middle works, marketing works. SAS Customer Intelligence 360 – where the middle makes the difference.
Learn more about how SAS can be applied for customer analytics, journey personalization and integrated marketing here. For those who want to dive deeper into the current state of the marketing/customer analytics technology ecosystem, check out fresh (and unbiased) research here.
