2025 Innovation Themes For Martech Use Cases Intersecting With Data Science & AI
Image 1: Applying AI To Marketing
As a result of these recent trends, marketing research and customer analysis as a discipline should continue to explore the following considerations to unlock incremental innovation:
Treat all of your owned data assets as a priority. It is frequently mentioned in the martech industry that brands must maximize the potential of their "owned" customer data sources. However, this also means we should prioritize activating structured, semi-structured and unstructured data sources. Brands cannot deprioritize semi-/unstructured data because of a perception that these flavors of information are difficult to use, as the opportunities to harvest these ingredients for analytical innovation is front and center today.
Recipes for data and analytical models. Retail data offers different analysis experiences from mining financial banking data. Similarly, a churn model in the discretionary fashion industry, where most customer behaviors indicating disengagement is silent, will likely differ from a churn strategy for a subscription-oriented streaming service, where churn is explicit (or observable). Recipes provide comprehensive use case-specific solutions to reduce adoption friction and increase the likelihood of success in leveraging customer insights within a marketer's workflow.
Optimize customer-level treatments. Machine learning models don’t make decisions — but they do identify actionable signals in noisy customer behavioral data. It’s up to our CX and/or marketing teammates to take prescriptive insights and execute data-driven targeting/personalization. But the decision isn’t always clear. Should a brand optimize on likelihoods, engagement, propensities, or profitability? The path forward will vary by a brand's unique business model, as well as the industry vertical it operates in.
Transforming Marketing Organizations Into Analytical Factories
Numerous well-known martech vendors have attempted to aspire to this vision over the years. However, the theme across the big martech vendors has frequently gone like this. As public companies, we strive for growth year after year. There are three segments we target our software and technology towards: marketers, analysts and data scientists.
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 (a private company with different motives), 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.
Think about the magnitude of requests that come in from customer experience and marketing teams to their supporting data science and analyst teams. The wish list includes actionable scoring for topics like acquisition, upsell, retention, segmentation, next-best-action (or experience), recommendations, lifetime value, pricing personalization, attribution and net lift.
The list 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. Let's take a moment and imagine we are in a requirements gathering meeting between two teams - data science and marketing.
Image 3: Requirements Meeting Between Marketing & Data Science
The marketing and CX teams responsible for the interactions between a brand and everyday consumers speak one language. The data science and analyst group likely speaks 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 in 2025.
Image 4: Data Science Jargon Creates Friction For Those Not Trained In The Discipline
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. 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.
Image 5: The Language & Use Cases Of Martech
Our vision at SAS is to serve as the market leader in advanced audience creation & targeting, independent of channel, for enterprise brands leveraging complex, disparate data sources and wishing to consistently deliver superior understanding within customer journeys. In other words, we want to empower brands to practice responsible marketing. However, none of this aspirational messaging matters unless SAS delivers comprehensive, end-to-end use case-specific solutions to common martech challenges.
An emerging trend to combat the ongoing analysis inefficiencies cited above involve Do-It-For-Me (DIFM) prebuilt recipes representing a specific ML/AI algorithm or model ensemble, processing logic, and configuration to auto-build and execute a trained solution that comprehensively solves (or improves efforts against) specific business problems. The analytical models and data engineering pipelines are ingredients of a broader recipe that get trained on data and parameter configurations to optimize a solution's ability to contribute significant value when pivoting to customer inference and marketing strategies.
Image 6: Increase Synergy & Accelerate Analytics
There is a fair amount to unpack and explain. SAS practitioners who program, code, visualize or model data recognize that the "beginning of time on Earth" is defined as midnight on January 1, 1960. The methodology of data science workflows across high-code and low/no-code windows has largely been a "best-practice approach" with specificity across the analytical lifecycle taught in academia and software vendor education/training programs. Now, its 2025, and SAS is introducing a new approach for enterprises to unlock value from beautiful, wonderful data.
The concept of recipes and required ingredients 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.
Image 7: SAS for Marketing AIWe know what you're thinking. Is this another fully or semi-automated analytics offering that promises the lovely benefits of AI but sacrifices transparency, control and customization? The answer is a "pound the fist on the table" moment, and an enthusiastic NO!
Remember, we are SAS, and for the last 45+ years, our heritage is rooted in being the founder and future of analytics. It all began when curious minds set out to answer some big questions. Is there a better way to analyze data? How can we turn data into intelligence? Who might benefit from our technology?
Years ago, lines of code were the key to something extraordinary. Now, SAS has customers around the world. We analyze billions of rows of data every second that change the way we work and live. Ultimately, we believe curiosity is at the heart of human progress. As the years pass by, SAS continues to hear in the field that marketers (not all, but many) struggle to do analytics at scale.
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. Benefits will include self-service analytics run by marketing teams with minimal external support, the ability to deploy anywhere to run analytics against your data (wherever it lives), and streamline data preparation (or engineering) with accelerators and automation.
The Role of the Data Person and Configuring Actionable Recipes
The efforts made by data engineers everyday focused on maximizing the potential and accuracy of a brand's data assets for analytics and marketing is a critical function. Let's begin explaining how efforts by SAS in the domain of Marketing AI will enable a data-savvy individual in crafting recipes for their marketing counter-parts to leverage.
How do use cases and requirement meetings begin? Well, with some type of objective that the brand aligns to. Below readers can view a screenshot of how the software will begin assisting the user through prompted screens to guide a workflow of configuring a recipe focused on customer churn (or retention).
Image 8: Define Your ObjectiveRecipes can be given custom names, codes, descriptions, business context and specificity on customer events of interest. Ranging across a variety of business models and industries, SAS recognizes our users need the ability to adapt and apply customization to gain incremental value through the recipe's specificity.
Moving on, 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 marketing and customer analytics ecosystem that practitioners frequently will admit to when pressed for honest feedback. A massive proportion of customer & marketing analysts in 2025 continue to skew towards the wrong end of this workflow spectrum:
"I spend more than 80% of my time accessing/preparing data, and less than 20% actually performing analysis."
Speed bumps like this usually emerge when customer experience teams require data to unlock and fuel advanced marketing insights.
For example, have you ever tried to extract event (or raw HIT) data from your preferred marketing cloud vendor? It tends to be challenging for data engineers based on numerous reasons (data volume, structure, storage, etc.). Our viewpoint going forward is to remove high-code requirements or manually connecting to APIs (Application Programming Interfaces) to transform the user experience within the software by providing simple point-and-click prompted steps to complete data absorption into SAS across a variety of martech vendors.
Users can select data originating from different martech vendors (assuming brands have contractional relationships with these organizations that allow access). This will include features to access, query, filter, profile and more when allocating data sources to a recipe configuration. Accelerating user workflows through the known challenges (mentioned earlier) residing in the phase of DataOps has been a key research area of interest at SAS.
Table joins are one of many examples in the DataOps phase for users to begin the process of acceleration through improving data quality, reducing time-to-value, and fostering collaboration between data engineers, data scientists, and business/marketing stakeholders. Image 12 below highlights the completed steps of joining multiple tables using variants (inner, left, right, etc.) of join definitions.
Preparing analytic base tables (or ABTs) is the process of organizing data into a flat table schema that's traditionally used by analysts for building analytical models and scoring (predicting/inference) the future behavior of a customer. A single record in this table represents the subject of the prediction (such as a customer or anonymous visitor) and stores all data (variables, features or predictors) describing this subject.
An example of this workflow includes aggregating transactional records associated with one customer to "flatten" the table and meet the expectations of the downstream algorithms that will produce the prescriptive scoring for marketing. Readers take note - users will will have features available across the spectrum of what is expected for best practices within data science and engineering.
If this concept is new to any readers, here is a marketing-centric example of how an ABT is important in the context of attribution analysis. Although there are many sub-topics related to the acceleration of data preparation, the intent of this article is to introduce readers on leveraging a guided interface that assists in the areas of automated aggregations, data quality handling, and feature engineering.
The next topic of interest is defining the business objective of the recipe, or in data science jargon, providing the brand's custom definition of the target (or dependent) variable.
The target variable is the data signal that you're modeling or predicting. It's also known as the dependent variable, response variable, or y variable. The target variable is important because it defines the type of problem you're solving (regression or classification) and determines how to evaluate a model's performance. Here is a contextual example:
Regression Use Cases: Predicting house prices (target: house price), predicting stock prices (target: stock price).
Classification Use Cases: Predicting whether a customer will churn (target: churn - yes/no), predicting whether an email is spam (target: spam - yes/no).
The target variable should be well-defined, measurable, and relevant to the problem you're trying to solve. An example can be framed in a credit risk model, where the target variable might be whether a borrower will default on a loan (1 = default, 0 = no default). A value proposition to bring to your attention is how SAS simplifies and automates workflow steps by removing the user's responsibility to simply know which algorithms should be used in a recipe based on the target variable's type (numeric or categorical).
The formation of a model-ready ABT with a customizable target objective leads us to the topic of project variants. Before a brand's data person can share pre-configured recipes with their marketing counterparts, SAS is bringing forth another value proposition in automating the readiness of addressing difficult customer experience scenarios. Take for example, churn. The definition of a churn event of interest can carry different time lines on when to flag (or censor) the customer's exiting behavior. Project variants, as a user feature, will automate the formation of sibling ABTs to enable marketers choice on which time-series scenario is more relevant to their use case.
After the user is satisfied, the next step is to kick off the job for preparing the table variants. The concept of local analytical modeling agents as a service needs to be introduced to highlight what is unique about this area of innovation. Local analytical modeling agents as a service refer to AI-powered systems that perform data analysis tasks locally on a user's computer or network, rather than relying on a cloud-based service. These agents can be accessed as a service, meaning they can be deployed and used without the need for extensive coding or infrastructure setup.
The agent runs on the user's device or local network, minimizing reliance on external services, thus providing local execution and enhancing data privacy by keeping data within the brand's control. Local agents are a major part of the reason why customization of user features to suit specific analytical needs can be tailored to use cases and recipes. Other benefits to call out include:
- No movement of data. Brands simply need to provide SAS access to their cloud structure and connection to owned data.
- Extreme Agility. SAS manages the software's design environment and pushes the necessary logic to run in a brand's data environment.
- Service. Analytical Agents are delivered by SAS as part of our solution for Marketing AI.
It's vital to indicate that users have transparency to review and assess jobs that have been kicked off. In the image above, we can observe an example where data connections have occurred, data extraction steps, and every analytic base table variant that is generated (or failed) with user access to information within detailed logs.
For the benefit of recipe configurations, users can confirm when a job has completed successfully across table variants, and can step forward to customizing column names and designating scoring eligibility. These user features simplify the proposition of delivering customizable prescriptive information to marketing team counterparts to action on.
SAS emphasizes incorporating fairness and oversight at every stage of the analytics journey, from development to deployment. This ensures that Marketing AI models are not only accurate but also fair and equitable in their predictions and decisions. Users will have tools for assessing fairness and bias in recipes, identifying potential variances in model performance for different groups. These assessments help uncover biases that might be embedded in the data or the model itself.
Detecting bias is only half the solution, and SAS will enable users to auto-mitigate bias during model training, helping organizations create more equitable and fair predictions. The software, as needed, will adjust algorithms, rebalance datasets, or use other techniques to reduce the impact of bias. SAS recognizes the importance of explainability in building trust and confidence in AI models. Explainable AI helps users understand how to recommend strategic decisions and identify potential biases or areas for improvement.
The Role of the Business/Marketing Person and Leveraging Projects
Projects is the transition from a configured recipe to unleashing the opportunity for marketing teams to train, activate, and manage models for a given use case. 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 live demo video snippets with screenshots to bring the user's experience to life for readers.
In this short introduction, the business/marketing user enters the Projects section of the interface where configured recipes await them. In a production deployment at a brand's site, the user's welcome screen would likely look similar to the Image 22 below.
Analytics and data science do NOT need to be constrained, and should be applied to what MATTERS to the brand. At the end of the introductory project user video above, we were presented with choices to optimize the project.
As marketers, what do you care more about in the context of churn or retention?
- Balancing the effort to save existing customers at risk of leaving while avoiding unnecessary retention actions.
- Optimizing your strategy to ensure retention efforts 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 targeting customers who are truly at-risk.
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 terms which improve the state of the business.
Let's strive forward into another software demo snippet which will showcase how a business/marketing user can take a configured project recipe and start training the solution.
The important takeaway for readers is the user is benefitting from the configuration of the recipe prior to accessing the project, allowing them to quickly train an automated 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 data-driven insights never get overlooked again due to the velocity of martech requirements today.
As training completes, the project experience receives a number of auto-benefits for the user.
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 begin by demonstrating what the training results screen offers users.
After viewing, let's discuss why this matters to business and marketing users.
- Natural language generated explanations and co-pilot helpers 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.
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. Furthermore, the demo video cast a spotlight on the simplicity of scheduling retraining of the use case solution to ensure marketers are activating on the latest (and freshest) customer scoring.
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.
We have reached the final phase of the project user's workflow, which relates to monitoring specific metrics prior to activation.
The reasons SAS built this functionality into a Marketing AI software solution's user work flow 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 solution needs to be revisited/refreshed, as well as when fairness becomes an issue and should be considered for mitigation (manually by a user or auto-addressed by the system itself).
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. Here is a brief walkthrough of what users can expect.
We have spent a great deal of time discussing the notion of activation. To be transparent, we will now introduce readers to how an analytically scored Audience eligible for activation can be seamlessly absorbed into SAS Customer Intelligence 360 Journeys. In the context of SAS, an Audience is the starting point for a journey orchestration use case (churn, acquisition, next best action, etc.).
In other words, it's time time for the big finish!
Although this last demonstration example was be handled comprehensively by SAS martech capabilities, selecting an external vendor destination is always an option for users as well.
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.
