Marketing & Data Science Viewpoints - Google Analytics 4 & SAS
If one doesn’t take a closer look into how digital analytic technologies differentiate from one another, it is easy to assume they do similar things. For example, both Google Analytics and SAS Customer Intelligence 360 can create 1st party digital data assets for a brand to utilize. But is that it? No, and if one investigates this topic, it is evident that brands have incremental opportunities to generate business value and insight from their digital properties.
It wasn’t long ago where brands operated across the customer lifecycle of acquisition, cross-sell, and retention using assumptions based on years of practitioner experience. Then up and coming analytic talent prior to the data science explosion began challenging those conventions with hard quantitative data. The shift to today is the monetary expenditures associated with capturing and storing 1st party customer behavioral data has decreased significantly opening a new realm of innovative possibilities.
Let’s start by sharing a generalized definition for "digital analytics" platforms:
In short, they represent specialized analytic applications used to understand and improve digital channel user experience. Their focus ranges across customer acquisition and behavior, as well as optimizing marketing campaigns. The emphasis is on digital channels and techniques yet use cases can evolve to connect with offline data. They are end-to-end platforms performing functions from data collection through analysis and visualization.
After approximately 15 years, Google officially modernized their well known digital analytic software offering from Google Universal Analytics (UA) to Google Analytics 4 (GA4). This occurred in July of 2023, the previous UA version no longer functions and brands had to perform a new implementation if they desired to leverage GA4 going forward. Given how many years brands depended on the UA version of the software, numerous readers will agree with various areas of concern associated with this change:
- Risk of historical data loss
- Lack of feature parity between GA4 and UA
- Learning to use a new product
- Resources required for re-implementation
Building upon the importance of this historic event, Adobe released their annual holiday digital shopping forecast. The Q4 - 2023 edition of this report summarizes the analysis of 1 trillion visits to retail sites and over 100 million SKUs in the United States. For context, that's one industry within a singular geography. Two key findings to be aware of:
- Despite a challenging macro-economic climate, consumers will drive up positive spend growth and generate over $221.8 billion (4.8% YoY) for online retail this season. ~5% YoY growth may seem small, but keep in mind this is incremental growth after the Covid pandemic when everyone was at home and utilizing digital devices at a much higher rate. Suddenly the ~5% metric is impressive.
- Mobile device will overtake desktop this holiday season, accounting for 51.2%of online spend for the holiday season and amounting to a record$113 billion.
Now, extrapolate on market opportunities outside of the retail industry driven by progressive growth. The importance of digital customer experience and the analysis that supports B2C interactions is massive. Hopefully we have your attention.
Think about why people visit a website or open an app. A few examples could be:
- Existing customers looking for help
- Segments of people with unique behaviors and interests
- Not everyone comes to you to buy something…maybe they are interested in an organization’s philanthropic efforts
Digital customer analytics helps brands understand why people come to your website or app. What do they do? Do they leave happy? Did the brand make money during these interactions? The main purpose of digital customer analytics in any industry is to help brands understand their progress toward particular business objectives. Considerations include:
- Deciding which objectives to focus on
- Identifying the metrics that drive the chosen objective at each stage of the customer journey
- Defining a target metric to work towards
Now, for readers who are not familiar with Google Analytics 4, it is a digital analytics service that tracks and reports website and mobile app traffic, currently as a solution inside the Google Marketing Platform.
The most common use cases for Google Analytics 4 revolve around websites and mobile apps. GA4 collects and stores user interactions with your website or app as events (page views, button clicks, user actions, etc.). Before we begin contrasting GA4 with SAS Customer Intelligence 360, it is relevant to note the evolving state of digital customer analytics associated with the frequently used term "digital intelligence". This category is represented by specific sub-categories making up a logical hierarchy:
- Data management technology
- Tag management systems
- Customer data platforms (CDPs)
- Data management platforms (DMPs)
- Digital analytics technology
- Location intelligence
- Application analytics & performance management
- Web analytics
- Interaction (CX) analytics
- Product intelligence
- Predictive modeling
- Social analytics
- Internet-of-Things (IoT)
- Experience optimization technology
- Behavioral targeting
- Recommendation systems
- Testing and experiments
- Digital touchpoints
- Web
- Mobile app
- Paid media
- Social, etc.
- Customer and business context
- Personalization
- Relevance
- Conforms to a brand's unique business model and customer measurement objectives
To learn more about digital intelligence as a theme, it is recommended to review the latest research on the subject available here. Moving on, rapid digital transformation trends have resulted in a hybrid consumer engagement model that is changing how brands shape, manage and deliver customer experiences (CX). Throw in the forthcoming loss of third-party cookie tracking and marketers must double down on the information they do have: owned first-party data.
Brands can address B2C challenges by deriving real intelligence from first-party data as opposed to misleading insights from inferred sources (third-party data) while acting with the speed and agility necessary to meet consumer expectations. With that said, let’s explore the commonalities between GA4 and SAS before addressing how they are different.
Google Analytics 4 & SAS Customer Intelligence 360: Commonalities
Both Google and SAS create 1st party digital customer data assets for your brand to leverage by offering JavaScript tracking code & SDKs as the technologies that natively collect data from websites and apps while providing brands the capability to address which user interactions to focus on, as well as what to defocus.
Each time the tracking mechanisms are triggered by a consumer’s behavior, such as a page load on a website or a screen view in a mobile app, GA4 and SAS record that activity, and then package the data. Once created, the data is transmitted either to the Google Analytics collection servers within the Google Cloud Platform (GCP) or SAS collection servers on Amazon Web Services (AWS) tenants. Examples of the type of data GA4 and SAS capture from a digital property include dimensions and metrics associated with the Page URL, Screen Views, Browser Info, Language Type, Device, Operating System, Traffic Sources and more.
In the context of sites, to add the JavaScript tag to a website's code, users have a few options:
- Use a tag management solution
- Manually add the tag to a website's code
- Provide the tag to a website builder service
Heading into 2024, most brands desire a user-friendly solution for tag management. One example would be Google Tag Manager. It allows organizations to efficiently add and update all of their website tags in a web interface to better understand conversions, site analytics, and more. This makes tag updates much simpler for anyone who’s not familiar with coding in JavaScript. Most importantly, it works with both Google and non-Google tags, including SAS Customer Intelligence 360.
Alternatively, users can manually add either the Google or SAS data collection tag to a website by copying and pasting it in the code of every page immediately after the <head> element. No matter how a brand approaches website set up, once added, this establishes the connection with the digital property and data collection begins!
Before we move on, let's address mobile app data collection. Both Google and SAS use software development kits (or SDKs) to add support for event collection and managed content to native mobile applications. Marketers, business analysts, and designers can use collected events to understand how the mobile app is performing, customer insights and targeting users for content distribution. Whether using Google's Firebase SDK or the SAS Customer Intelligence 360 SDK, if your brand’s app is on both iOS and Android, users need to create a customer data stream for each platform. App developers must implement the SDK within the brand's app before analysts can measure or model customer/prospect activity.
Once configured for an app, a number of customer behavioral events like opens, in-app purchases & screen views will become available for analysis. In addition, users have the option to collect custom events that are relevant to their company's business. While the method of data collection differs slightly between web and app, analysts can research all this data together in separate or consolidated views to understand how customers navigate across different touchpoints and properties.
As the tracking executes, both GA4 and SAS carry one more similarity which is creating anonymous unique identifiers to distinguish between new and returning digital visitors. There are different ways an identifier can be created, and industry best practices continue to utilize persistent 1st-party cookies.
Google Analytics 4 & SAS Customer Intelligence 360: Differences
Now that we have outlined the similarities, this is the moment when the topic of data contextualization emerges to showcase the differences between digital customer analytic technologies. After digital behavioral data is collected, both Google and SAS transform the raw information using the settings (or configurations) controlled from their respective administrative user accounts. During data contextualization, the technologies take steps to transform the semi-structured raw event and interaction data from collection using these rules-based settings and configurations. These settings underscore the importance of a solution’s implementation, as they help align your 1st party digital data more closely to your brand’s measurement plan and business objectives.
It should be noted how data is transformed has direct implications on the type of analysis, as well as the sophistication of the use cases, that can be performed by an analyst team. In the case of Google, after it has finished processing what it has collected, let's dive in and provide visibility into how this works.
With each customer interaction to a brand’s website or app, an event and associated parameters containing information about the interaction are sent to GA4. An event could be when a user first opens an app, watches a video, or views a page on a website. Events are sent with additional data called event parameters for interpretative context. For example, when someone watches a video on a website, an event is fired when the user clicks play. Event parameters, like the name of the video and how long the video was watched, add context. Along with events and event parameters, user data, like geographic location and the device being used, are also sent. These are called user properties.
To visually represent this data in reports, GA4 uses dimensions and metrics. A dimension is an attribute of your data. It describes your data, and it's usually text as opposed to numbers. A metric is a quantitative measurement, such as an average, a ratio, and a percentage. It's always a number as opposed to text. All GA4 reports are based on different combinations of “dimensions” and “metrics”. When viewing data in the analysis interface, users can think of this environment as a layer on top of the data that allows analysts to organize, segment, and filter. When opening a GA4 report, a query is sent to the aggregate tables stored in the Google Cloud Platform (GCP) that are populated with prepared data to enable the analyst with descriptive insights.
One of the strengths of GA4 is represented by prebuilt descriptive analytic & measurement views available in the "reports" section of the user interface (which can be viewed in Image 8 above on the left-side menu of the screenshot). Let's dive into the GA4 Audiences reporting view to see an example of this. The intention of this pre-made report is to identify a brand’s engaged and profitable audiences.
Google defines the term "Audiences" as a group of users from a site and/or app who have generated similar behavioral data or who share demographic or other descriptive data (e.g., same age group, same gender, were acquired by the same campaign). A notable benefit to users of GA4 is the ability to share audiences with Google Ads, so marketing across touchpoints like search or display to specific groups of users is feasible.
For example, analysts might want to create an audience who have made a purchase of any kind (purchase event_count > 0). However, analysts might find this audience too large for the practical purposes of their brand's ad campaigns. In this case, analysts can identify smaller groups of users who have more specific behavior and demographics in common, like:
- Users from California who have purchased 1-5 items
- Users from San Francisco, California who have purchased 1-5 items in the last 7 days
- Users from San Francisco who purchased 1-5 items in the last 7 days and who spent more than $100
GA4 administrators can work with their analyst teammates and define audiences broadly or narrowly. When a unique audience needs to be defined and made available in GA4 reporting views, this is performed in the admin section of the software.
Another feature to point out here is the concept of predictive audiences in GA4. A predictive audience is an audience with at least one condition based on a predictive metric. For example, analysts and/or admins could build an audience for ‘likely 7-day purchasers’ that includes prospects who are likely to make a purchase in the next 7 days. The availability of predictive audiences depends on the underlying predictive metrics being eligible for use by meeting specific prerequisites. As of January 2024, GA4 offers three predictive metrics for usage:
- Purchase probability: The probability that a user who was active in the last 28 days will log a specific conversion event within the next 7 days.
- Churn probability: The probability that a user who was active on your app or site within the last 7 days will not be active within the next 7 days.
- Predicted revenue: The revenue expected from all purchase conversions within the next 28 days from a user who was active in the last 28 days.
Google describes that GA4 automatically enriches a brand’s data by bringing machine-learning to bear on the dataset to predict the future behavior of customers. This concept of do-it-for-me (DIFM) functionality is an evolving trend in the broader martech ecosystem of solutions, and is designed to simplify the usage of advanced analytics. Keep in mind, there are both advantages and disadvantages to friendly features like this that will be discussed in more detail shortly.
Once defined, predictive audiences are available for analysts to view or edit within the admin screen.
Image 9 above shows predictive audiences displayed in the same Audience reporting views as standard audiences. However, pre-configured reports can sometimes be frustrating for analysts who want more customization within their research. Building upon this premise, GA4 offers analysts the ability to build custom explorations. When analysts want to explore data in more detail, they can:
- Perform ad hoc queries
- Configure and switch between techniques
- Sort, refactor, and drill down into the data
- Use filters and segments
- Create segments and audiences
- Export the exploration data for use in other tools
The screenshot below was taken in January 2024. At the time, these were the exploration techniques supported:
The ingredients of an exploration in GA4 are made up of three parts.
- Canvas: The large area on the right displays data using the selected technique.
- Variables: The panel on the left gives analysts access to the dimensions, metrics, segments & timeframe to use in the exploration.
- Tab Settings: Analysts can use the options in the Tab Settings panel to configure the exploration.
The types of exploratory visualization and graphing objects supported are:
- Table
- Donut chart
- Line chart
- Scatterplot
- Bar chart
- Geo map
- Funnel
- Sankey Diagram
- Venn Diagram
- Heatmap
For example, here is a screenshot of the GA4 funnel exploration.
Analysts can visualize the steps visitors take to complete a task and see how well they are succeeding or failing at each step. Funnel exploration enables analysts to address questions like:
- How do prospects become shoppers and then become buyers?
- How do one-time buyers become repeat buyers?
Moving on in our quick GA4 tour, the last section to cover relates to Advertising analysis. The reporting views in this section aim to help analysts better understand the ROI of media spend across channels, make informed decisions about budget allocation, and evaluate attribution models.
For readers who are not familiar, attribution is the act of assigning credit for conversions to different ads, clicks, and factors along a consumer's path to completing a conversion. An attribution model can be a business rule, a set of rules, or a data-driven algorithm that determines how credit for conversions is assigned to touchpoints on conversion paths. These GA4 reports allow analysts to explore different attribution measurement approaches and determine which one might work best for their business.
As of January 2024, there are currently four reporting views in the GA4 Advertising section:
- Advertising snapshot: Overview of business metrics, while allowing analysts to dig deeper into the areas they want to explore.
- Performance: Observe which channels and campaigns received conversion credit.
- Model comparison: Contrast how different attribution models impact the valuation of marketing channels.
- Conversion paths: See customer paths to conversion, and review how different attribution models (last touch vs. data-driven) distribute credit on those paths.
Analysts can use these reports to answer questions like:
- What roles did referrals, searches, and ads play in conversions?
- How much time passed between a customer's initial interest and their purchase?
- What are the most common paths customers take leading up to conversions?
Now that we have provided a detailed introduction of GA4, let's pivot to SAS. After digital consumer interactions are processed and contextualized by SAS Customer Intelligence 360, the first key difference to highlight relates to the solution's data model. While GA4 enables:
- Pre-made & templated descriptive reporting views
- Data exploration, segmentation & filtering
- Advertising analysis
- Audiences & Google Ads integration
- Evolving set of automated insight features
In contrast, SAS provides an actionable data model that serves both overlapping and uniquely different requirements.
A number of value propositions SAS is bringing forth include:
- Analyst access to structured data tables at varying levels of aggregation and detail
- Integration with on-prem or cloud-based CRM
- Pre-made, templated & custom reporting
- Do-It-For-Me (DIFM) automated insights
- Do-It-Yourself (DIY) data visualization and visual no-code modeling
- DIFM or DIY predictive modeling, machine learning & model interpretability
- Pre-made ABTs (analytic base tables) for data science acceleration
- Support for data-at-rest and data-in-motion use cases
- Triggered real-time decisioning activated by customer behavioral event detection
- Campaign management, personalization & targeting for owned digital properties, ad media platforms & multi-touchpoint journeys
As the proportion of online to offline customer interactions with a brand continues to elevate, both digital experiences and the 1st/Zero-party data that analytic solutions collect about them will mature in ways that will challenge and benefit insight-led companies. Over the years, SAS has observed the web/digital analytic ecosystem of software solutions and targeted users bury themselves in templated measurement reports primarily descriptive in nature. As digital data related to a brand's customers continues to incrementally increase in importance, enterprise use cases for sharing insights broadly within their walls frequently attempt to absorb digital data signals into their business intelligence (BI) or data visualization software applications. However, many brands seem to be hitting an adoption ceiling.
At SAS, our perspective on this trend is the lack of customization (or personalization) derails analysts on receiving the benefits of a digital measurement solution's attempt to be easy-to-use. Augmented data visualization and discovery is appealing because it does not subscribe to a one-size-fits-all user experience. To fully extract the potential of analytic measurement, interactive visualization, predictive modeling, algorithmic segmentation and machine learning, numerous viewpoints should be considered:
- Insights - Who will explore, identify & produce? How will they be explained? Most importantly, don't overlook how they will be consumed. Every brand will have unique profiles (or segments) of team members who will receive insights, and their personal levels of data/analytical literacy matters.
- Analysis - Web/digital analytic standalone solutions tend to skew more towards canned/guided delivery of insight. There's nothing wrong with that if your brand finds this beneficial. But when brands link digital and non-digital data sources together, the flexibility to explore, share recipes of approach & using technology that augments/accelerates the analysis workflow in arriving to unbiased, impactful insight conclusions is critical.
SAS recognizes the critical importance of serving multiple enterprise personas through augmentation (embedded AI and machine learning to assist as analytical helpers.) This spectrum ranges from business users who want out-of-the-box benefits to savvy analysts 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 within an organization.
Furthermore, what can Marketing AI do to keep improving insight-driven practices? It's natural within brands to see different flavors of analysts and martech team members express a desire to go beyond reporting, querying, data visualizations, and descriptive/diagnostic analytics. Augmentation introduces emerging capabilities worth noting:
- Enabling users in no/low or high-code environments to identify key drivers or influencers on a metric, find anomalies, or project a forecast/trend supported with natural language generated (NLG) explanations to ensure accurate analyst interpretation of insights.
- What about data scientists and data engineers? It should not be overlooked that SAS Viya allows users to author and train new ML models in the same platform where models are going to be deployed, but it also eliminates the need to integrate multiple platforms, improving model transparency/governance, and reducing the likelihood of errors that may result from platform-to-platform data and metadata handoffs.
As we mentioned earlier in the article, SAS can support website and mobile app 1st/Zero-party data collection, pixel tracking, mobile SDKs, and server-to-server APIs. Once any of these data streams are absorbed, the SAS Customer Intelligence 360 unified data model (UDM) contextualizes this information into out-of-the-box (OOTB) and customizable structured tables for analysis enabling DIFM martech user features & DIY analyst acceleration.
The subject areas of the data model include session/event-based data for visits, media, pages, products, forms, search, goals, conversions, customers, web/mobile/email/direct marketing and contact/response. SAS provides over 100 prebuilt & customizable reports (including templates, metrics & KPIs) for campaigns, journeys, content, events, ecommerce and more. Users can create recipes & share using native SAS visualization or through integration w/ MS Power BI & open-source (Python, D3) to take advantage of customizable features for color, KPIs, sizing, responsive design, cross-device/streaming analytics, user annotations/alerting, extensibility to iOS/Android mobile apps & SDKs for custom/3rd party app/websites.
But enough chatter...let's bring this to life. The forthcoming sections will provide readers the opportunity to learn how SAS can be used for a variety of customer and marketing use cases. This will provide clarity into how SAS and GA4 overlap and differentiate.
SAS Technology Demo 1: DIFM & DIY Dashboards, Reporting, Measurement & Distribution
To begin, we will briefly introduce standard OOTB reports to users of SAS Customer Intelligence 360. We will then transition to how 1st/Zero-party data captured & contextualized by SAS Customer Intelligence 360 can be made available to SAS Visual Analytics on SAS Viya. This will significantly expand the amount of customization that can be applied to dashboards and reports, as well as diversify the data sources that can be represented in these measurement assets. We will drill into the augmentation features that provide value propositions such as natural language generated insights, explanations, outlier detection, correlated measures, auto-segmentation & DIFM propensity-scoring. We will wrap up this demonstration in how these measurement reporting assets can be shared.
Within the martech industry, there are several factors that contribute to the challenges surrounding brand decision-making. Obviously, customers and markets are more competitive and demanding. When you step back and reflect on this, it's a linear trend upward year-after-year when it comes to consumer expectations. This means, to satisfy that demand, it's well recognized that brands need to respond quicker, but it's often overlooked that accuracy holds an equal weight. Personalization, targeting, segmentation, relevance and other fun martech buzzwords all rely on this.
Data continues to flood every organization, both in size and in speed. Sometimes more data is better, but the challenge can be that critical decision-making information gets lost. Skilled analytical talent with application experience in the various domains of modern marketing is the key to move a brand from reactive to proactive. Thus, varying flavors of technology and automation are critically important to augment customer analysts in accelerating their delivery's time-to-value.
Machine learning is a branch of AI that automates the building of systems that learn iteratively from data, identify patterns, and predict future results. And it does that with minimal human intervention. Machine learning shares many approaches with other related fields, but it focuses on predictive accuracy. Building representative machine learning models that generalize well on future data requires careful consideration of both the data at hand and assumptions about the various available training algorithms.
SAS Technology Demo 2: Acceleration of DIY Customer Propensity Analysis
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, this demo video will address AutoML and hyperparameter autotuning with data captured and contextualized from SAS Customer Intelligence 360.
As an analyst, when you can communicate the value of your modeling efforts in monetary terms, every executive paying attention is going to lean in and focus. Passing these insights to influence our marketing teammates will directly impact their segmentation strategies and touchpoint tactics. As a life-long student of business and marketing analytics for the last two decades, the concept of applying a profit matrix is one of the most industry-practical topics I have ever learned.
Keep in mind, supervised learning algorithms are trained using labeled examples (conversion vs. non-conversion), such as an input where the desired output is known. The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors. It then modifies the model accordingly. Supervised learning is commonly used in applications where historical data predict likely future events. For example, supervised learning can anticipate when an insurance customer is likely to file a claim, or when a retail customer has a higher likelihood to be interested in an upsell recommendation.
SAS supports two types of supervised learning problems through natively-supported algorithms such as gradient boosting, forests, neural networks, support vector machines, Bayesian networks and more.
- Classification – When the data are being used to predict a categorical target, supervised learning is called classification. This is the case when assigning a label or indicator (for example, labeling an image a dog or a cat). When there are only two labels, this is called binary classification. When there are more than two categories, the problems are called nominal classification.
- Regression – When the data are being used to predict interval targets, the problems are referred to as regression.
The reason supervised learning as a category contains a variety of algorithms is based on the notion that no model is uniformly the best, particularly when considering the deployment over time, when data changes. Analysts select a model primarily based on fit statistics and assessment graphics of performance.
Supervised classification does not usually end with an estimate of the posterior probability. For example, in binary classification problems, the ultimate use of a predictive model is to allocate cases (customers) to classes (target / don't target). This is accomplished by appropriately choosing a posterior probability cutoff. The cutoff or threshold represents the probability that the prediction is true.
SAS Technology Demo 3: Supervised Learning, Prediction & Maximizing Profit for Customer Targeting
Determining an appropriate cutoff is problem specific, and there are many ways of accomplishing this (Bayes' Rule, Central Cutoff, KS Cutoff, etc.). We will focus on one solution referred to as the Profit Matrix, which is a formal approach to determining the optimal cutoff using statistical decision theory. The decision-theoretic approach starts by assigning profit margins to true positives and loss margins to false positives. The optimal decision rule maximizes the total expected profit.
The next demo video will address the usage of profit matrices using digital interaction data and other 1st party customer assets together to benefit marketing-centric use cases.
As marketers and advertisers utilize AI and ML to elevate their brands, understanding the potential for detecting and mitigating bias within predictive analytic procedures is crucial. Today, AI plays a progressive role in advancing the marketing and advertising mandate. Data is generated constantly in the digital ecosystem, associated with real-time customer engagement behavior, web/mobile interactions and incremental streams of desired revenue year-after-year. This gives brands the chance to apply a variety of analytical techniques to draw "ah-ha" insights that can be used in various forms of customer interaction tactics.
At a fundamental level, AI/ML helps brands improve their viewpoints on customers and subsequently, running their business. When using data science for these intentions, brands are placing immense trust that their modeling IP is providing useful insights that subsequently influence marketing treatments. Keep in mind, not every single human being on Planet Earth has the same level of data/analytical literacy, and some may consider applications of AI/ML as opportunistic innovation, free from the mistakes commonly attributed in human-driven decisions.
I hate to spoil the fun, but we need to be more prudent as practitioners. AI/ML can contain bias. Instead of ushering in a utopian era of fair decisions, AI/ML have the potential to exacerbate the impact of biases. As innovations help with everything from the identification of attractive prospects to predicting who should receive a marketing stimuli, it is important to understand every modeling application has the potential to affect separate segments of a customer population differently. When applied in martech, biased AI/ML can negate efforts to learn, understand and anticipate consumer behavior. Brands should improve their understanding of how AI bias impacts them, how to detect it and ultimately, mitigate.
SAS Technology Demo 4: AI/ML Bias Detection and Mitigation for Responsible Marketing
One of the top predictions for 2024 centers on responsible marketing. There are numerous ways in which bias can slip into customer data. Although marketers themselves may not build analytical models, it's hard to find a use case these days that doesn't benefit from propensities or probabilities. There are many perspectives to take into account when describing bias in data science. Bias can happen during data collection, data processing, sampling, model building, and so on. However, when AI/ML is applied to data that is inaccurate, it has the potential to magnify the errors and cause unintended bias in campaigns, personalization or testing. If that wasn't enough, bias can lead to irrelevant results from severely impacted KPIs, such as failing to reach the correct audience or serving up the wrong offers to a particular demographic. Ultimately, this means wasted money and resources, failure to reach relevant customers, and potential harming the reputation of a brand.
While most brands readily promote fairness in AI/ML as a principle, putting the processes in place to execute it consistently is an ongoing obstacle. There are multiple dimensions for evaluating the fairness of AI/ML, and determining the correct approach depends on the use case. In short, we (at SAS) see two parallels at the moment:
- Data scientists and analysts need to continue their focus on translating the output of AI/ML in business language and storytelling to reduce stakeholder intimidation. Remember, if your models are not put into action, what was the point of your effort?
- Marketing and CX decision makers may not be passionate about statistics, but nearly every use case can elevate through the usage of propensities and probabilities. While AI/ML is advertised as next-level precision, it is not 100% perfect. Therefore, the translation of propensities and probabilities into business context must be interrogated, transparent and understood.
Effective risk management is increasingly being brought to the frontline rather than functioning in the back office. When using advanced analytics, it’s becoming increasingly important to understand and measure fairness risk to avoid exploiting vulnerable customers. The next demo will feature a combination of online and offline customer data as inputs to the use case.
As 2024 takes hold, it is anticipated use cases for customer analytics and digital intelligence continue to evolve with a bottomless hunger for variety and volume of data to supercharge use cases. Check in with any of the analytical magicians within your brand, and observe their struggle to acquire the relevant input data (and enough of it) to train modeling recipes.
Sounds strange, right? The frequently used term of yester-year known as "BIG data" used to roll off the tongue as often as we hear the term "AI" today. The challenge isn't that there is enough information in general. But the intention of any analyst is to identify meaningful data signals to address business objectives. What's the point of having access to oceans of big data if it's just noise? Garbage in, garbage out. For example, I would like to build a classification model using supervised learning to improve our understanding of behaviors and drivers of conversion behavior for my B2C (business-to-consumer) brand.
- 99.5% of the customer journeys resulted in non-conversion for the past 90 days.
- 0.5% resulted in a conversion.
If my analysis does a great job of accurately predicting non-conversions (high true negative rate), but does a terrible job of classifying conversions (low true positive rate), what marketing leader is going to get excited about that? Excluding special exceptions, not many. The problem here amplifies a well known problem in analytical modeling related to a rare-event of interest.
Taking it one step further, as generative AI and deep learning increase in practical application of business use cases, many observe the space as a foundational component of modern AI algorithms. But we also know to effectively train models using these algorithms, one needs a tremendous amount of data. And while it seems like we’re practically swimming in data day in and day out, we don’t necessarily always have enough of the RIGHT data for every process or behavior we’re trying to model. In other words, it's NOT the lack of choice of machine learning algorithms, but the scarcity of high-quality data.
Enter synthetic data (one of numerous generative AI approaches) which replicates, mirrors, or extracts look-alike information that allow analysts to model use cases that would otherwise be impractical. A few examples of challenges include:
- Data quality concerns
- Privacy
- Lack of relevant data
But I work in the martech industry, how does this apply to me?
Real data is expensive to collect and properly annotate, especially when it is in large scale. This is both a monetary and time drain for high-utility team members who support customer journey management processes. Real data can also be messy, requiring time to clean and/or extract useful features. It can be imbalanced, which makes it harder to train good models in support of journey-based analytics. It can be sensitive to share or store due to privacy concerns.
Now, marketers frequently run campaigns, tasks and activities. They target audience segments. The desire is to deliver personalization that is helpful, relevant and value enhancing within tailored customer experiences across channels. Two examples of analytically-driven marketing to consider:
- Propensity scores. They are intended to identify high-likelihood audiences who will convert on your macro- and micro-conversion goals. However, imbalanced data that is used to train classification models will produce higher margins of error, or less valuable propensity scores that lead to irrelevant personalization and lower conversion rates.
- Look-alike audience insights. You know you love it when an analyst describes to you the behaviors, demographics, and transactional patterns of high-value customers. The actionable outcome is to hunt for look-alikes within acquisition & upsell/cross-sell marketing. Imbalanced data is like a viral infection, and reduces the opportunistic potential of the insight-driven strategies being leveraged to influence the usage of marketing budgets.
SAS Technology Demo 5: Synthetic Data Generation (Generative AI) for Martech
Synthetic data can be artificially manufactured by special-purpose machine learning models in a way that captures the data distributions and patterns, while also helping to maintain privacy without exposing real information. For example, a Generative Adversarial Network algorithm, or GAN, can learn the patterns and relationships in existing data in order to generate new observations that are indistinguishable from real data. You’ve probably seen this used for what are known as deep fakes (creating very realistic images of people that don’t even exist). But we can also use this same technology for tabular data, which is most common for training predictive models with machine learning algorithms. The following demo will showcase the use of the SAS native tabularGAN action set to generate synthetic data.
It's readily recognized customers are digitally savvy, discerning and motivated to get the best deal. This has made it increasingly difficult for brands to develop pricing strategies that optimize net revenue.
Customer-centric pricing can be a game-changer. Whether an online or brick-and-mortar shop, pricing personalization aims to use data and analytical insight to influence what is (or isn't) offered to each prospective buyer. Is the price a part of the shopping experience? Absolutely. Price is one of the major factors that forge a consumer's buying decision and loyalty.
The direct interaction between brands and customers (think web, mobile app, email, etc.) enable the opportunity to implement personalized pricing more effectively. Brands collect 1st party data on the consumer's engagement with their product offerings, and can use this information to develop offer strategies. Over time, our consultative engagements here at SAS with various brands have shown common challenges:
- Storage and effective use of high quality, un-sampled 1st party data on user behavior, purchase history, etc.
- Optimizing net revenue with pricing tactics
Pricing personalization can be a useful tool to help brands reach different objectives, depending on their business model, market conditions, and customer segments. For instance, brands can increase sales volume by targeting price-sensitive customers with lower prices while maintaining higher margins from less price-sensitive consumers. Sounds logical, rationale and intelligent, right? While pricing is often described as a science by practitioners, it is a key factor to drive conversions. And we want to do this while optimizing profit, which points to a secondary challenge in regard to which customers should receive a marketing intervention (or stimuli), and which ones shouldn't.
Setting the right price for a good or service is an old problem in economic theory. Keep in mind, there are a vast amount of pricing strategies in existence that depend on the objective sought. One brand may seek to maximize profitability on each unit sold or on the overall market share, while another company needs to access a new market or to protect an existing one. Moreover, different scenarios can coexist in the same company for different goods or customer segments. Although strategies like premium and penetration pricing have existed for many years, let's focus on the use of algorithms to address this challenge.
Algorithmic personalized pricing is a process of setting optimal offers using the power of AI/ML to maximize revenue, increase profit or address other business goals set by brands. Algorithmic personalized pricing can easily become one of the most powerful means of gaining a competitive advantage for a brand.
SAS Technology Demo 6: Pricing Personalization, Net Revenue Optimization & Marketing Interventions
One challenge brands will always face relates to the heterogeneous characteristics of their customers. Such variability can affect how customers choose to (or not to) convert from a targeted marketing intervention (such as a price increase or decrease). Understanding customer response behavior to targeted offerings is crucial for informing individualized pricing decisions. PROC DEEPPRICE from SAS offers a flexible framework for specifying and estimating customer responses to marketing treatments.
Let’s look at a demo of how an online media brand can offer targeted discounts through personalized pricing to optimize revenue.
Helping customers of your brand's owned digital properties find items of interest is useful in almost any situation. Now, recommendation analysis leverages customer interaction data to uncover hidden patterns in order to identify related products, services or content to surface for targeting and personalization. This analysis easily extends to other types of use cases to build more customer relevance, especially with recent innovations of new AI/ML techniques.
In SAS Customer Intelligence 360, users can create tasks (Web, Mobile, Email, etc.) that display different creatives based either on a product being viewed or a customer’s behavior. There are two methods for delivering recommendations to users. User-centric recommendations take a user’s behavior into account. Product-centric recommendations are based solely on a product.
A sampling of native recommendation analysis algorithms available in SAS include regularized & non-negative matrix factorization, k-nearest neighbor, bayesian personalized ranking, factorization machines, data translation w/ optimal step-size, slope one, market basket, link analysis, and the list goes on. Additionally, SAS supports usage of open source (Python/R) recommender packages. Given the high volume of algorithms to select from as an analyst, SAS enables champion-challenger recommender modeling prior to deployment.
Let's articulate two scenarios for designing and deploying a recommendation system:
Offline Training & Online Scoring
Offline training is performed on a snapshot of data at rest to produce scoring code. That scoring code can then be deployed into an online scoring system on forthcoming (new) streaming data produced by customers and prospects interacting with your brand. Keep in mind, the scoring on new data is based on a modeling solution that was trained offline (or in batch). Thus, the solution would need to be retrained at signs of performance decay. For a detailed demo video on this scenario (within the context of financial services), please check out this article. For SAS user documentation on developing offline recommenders, go here.
Online Training & Scoring
The demonstration video below (in the context of the retail industry) is based on a true online training and scoring solution for recommendation systems. The incremental value proposition here is represented by a recommender modeling solution that trained once on a snapshot of data-at-rest in SAS (to mitigate issues like cold start problems), optimize using three algorithmic approaches leveraging factorization machines (FMs), bayesian personalized ranking (BPR) & data translation w/ optimal step-size (DTOS), deploy, and online training and scoring handles the re-training of the champion model and associated scoring on a 1:1 basis. For SAS user documentation on developing online recommenders, go here.
SAS Technology Demo 7: Real-Time Customer Recommendation Systems For Data-In-Motion
Using a SAS for Retail website, let's walkthrough a technology demonstration where SAS Customer Intelligence 360 is leveraged for an AIoT use case in the context of recommenders. We will cover:
- The capture, contextualization and input streaming of digital interactions between a prospective customer and the brand's digital property
- The event monitoring and triggering of a champion recommender model
- Training and scoring on data-in-motion
- The output of the recommender scoring will drive immediate actioning, targeting and personalization back into the customer's digital experience
Brands today are complex ecosystems of decisions that must be executed with increasing levels of automation - due to their competitors digitally transforming and influencing customer expectations. In response, there is a need to change how decisions are made. Organizations have the opportunity to increase their capability to perform augmented decision making - where a human takes analytically driven insight to make a decision (such as within a call center, website or mobile app). Automation within decision making is when an algorithm (or algorithms) blended with business rules make the decisions without human intervention (such as next best offers/actions/experiences). With each passing year, the acceleration of the scale, speed and complexity of customer 1:1 decisions is increasing.
A customer journey in its purest form represents a series of brand-orchestrated connected experiences addressing an individual's desires and needs — whether that be completing a desired task or traversing the end-to-end journey from prospect to customer to loyal advocate. When you reflect on this, the customer experience is the totality of cognitive, affective, sensory, and behavioral consumer responses during all stages of the consumption process including pre-purchase, consumption, and post-purchase stages.
Customer decisioning is best used to drive real-time actions in three contexts.
- To drive the ideal next journey-based interaction that a customer or prospect should have with your brand.
- As part of a cross-channel marketing initiative that unifies an experience across customer-facing channels.
- To enable personalization that delivers customized messages based on an individual's profile and observed behaviors while respecting experiential privacy.
SAS blends DataOps, ModelOps, DecisionOps & marketing orchestration to support offer treatment prioritization requirements for a variety of journey-based use cases. To enable data-driven decisions at scale, the analytics life cycle must be highly operational, automated and streamlined. By connecting all aspects of the analytics life cycle – brands can turn critical questions into trusted decisions.
SAS Technology Demo 8: Real-Time Customer Offer Treatment Prioritization
The demo video below will feature a fictional financial services company comprised of multiple business units (savings, lending, wealth management, etc.) and operating in numerous geographies. The primary objective will be to showcase a mutual value exchange across the full customer journey by optimizing orchestration capabilities and using customer-directed engagement models to guide personalization strategies.
Secondarily, SAS recognizes brands must adapt to a mix of cross-functional and cross-brand goals. The use of machine learning and prescriptive analytics will be shown in support of how marketing teams can generate and prioritize single and cross-brand journeys. Data monitoring, ML and AI help surface alerts & address needed optimizations that govern which inbound and outbound interactions a customer should receive in a given time period. The intention is for brands to prove the value of marketing in a volatile business environment, connecting strategies to marketing and customer outcomes.
It's no secret who the two biggest players in digital ad media are. Google and Meta (Facebook) quickly come to mind. For those who aren't familiar, Google Ads and Meta (Facebook) Ads are online advertising platforms enabling marketers (and brands) to find customers.
When we consider Google Ads, brands have the opportunity to connect with current and prospective customers across Search, Display, Shopping, Video and App. Pivoting to Meta Ads, marketers can reach new/existing customers as well as their networked communities on Facebook, Instagram, Messenger and WhatsApp.
With this stated, reflect for a moment on the business opportunity to:
- Maximize qualified leads and conversions
- Increase online sales
- Drive in-store foot traffic (or send more users to your website)
- Show your brand to more people to increase awareness, reach and engagement
- Market your app to new users (or increase app installs)
Now consider how many human beings on Planet Earth use Google and Meta (Facebook). It's not thousands or millions. It's billions. Hopefully we have your attention, and let's proceed to connect the dots to SAS. Brands can use connectors to retrieve or transfer data between SAS Customer Intelligence 360 and on-premises or cloud-based applications. Our design intent with these OOTB connectors is to offer brands time-to-value acceleration in activating commonly used data integration flows.
For example, if users of SAS Customer Intelligence 360 want to retrieve GA4 data residing on the Google Cloud Platform (GCP) for segmentation and audience targeting purposes on their owned digital properties, they can use the recently enhanced Audience Management capabilities. If the targeting criteria is met, SAS can then be used for testing, targeting and/or personalization benefitting from the availability of GA4 data. This is an important gap for SAS to fill for our customers because Google sunset (or retired) their Optimize software product in September of 2023.
The fun doesn't stop with the ability to retrieve external cloud-based data for segmentation and targeting. Users of SAS Customer Intelligence 360 can utilize their brand's 1st-party data assets (including the contextualized digital interaction data SAS makes available) to form and transfer audiences/segments to media platforms like Google & Meta (Facebook) Ads. Given the amount of 3rd party ad targeting data in these media platforms, the opportunity to use 1st party data will benefit ad campaign performance metrics.
SAS Technology Demo 9: Customer Closed Loop Campaign Management On Google & Meta Ads
To bring this to life, provided below is an introductory demo video on how SAS guerrilla-like data science powers can be applied to this use case. It will exemplify how closing the loop on campaign management is much more than just getting clicks. SAS takes the baton from Google and Meta within a customer journey (websites, apps & other brand-owned properties) using analytical muscle (testing, propensity-driven retargeting, algorithmic recommenders, etc.) to improve conversion metrics and monetary-driven objectives.
SAS is frequently requested by our customers/prospects and challenged by the analyst community to showcase how we help marketers design and manage journeys. This tends to involve:
- Audience segmentation.
- Creation, management and planning of the timing/sequencing of a diverse set of channels/touchpoints.
- Accommodating both new and in-progress campaigns.
Objectively, SAS strives to enable marketers to provide a mutual value exchange on digital channels across the full customer journey by optimizing orchestration capabilities and using customer-directed engagement models to guide personalization strategies. The intent is to provide increasing customer lifetime value (LTV), progressive returns on engagement, more personalized interactions, and sophisticated orchestration across the customer's end-to-end journey.
SAS Technology Demo 10: Customer Journey Building and Design
Let's get to the last demo video below. Here is a quick preview:
- SAS will be used to demonstrate customer journey design capabilities, showcasing support for multiple customer-directed engagement strategies.
- The exemplified journey incorporates a variety of channels/touchpoints, including email, web, mobile, social and external CRM systems.
- Key features which will be showcased include libraries of trigger-based journeys, node controls, content and offers, testing/experimentation, and journey versioning.
Google Analytics 4 & SAS Customer Intelligence 360: Together Is Better
Although this article contains a tremendous amount of information, we will draw a few conclusions in our research contrasting GA4 and SAS. Let's take this in steps. Every idea, hypothesis and project born within a company begins and ends with a question and decision.
Borrowing from software development practices, DataOps provides an agile approach to data access, quality, preparation, and governance. It enables greater reliability, adaptability, speed and collaboration in your efforts to operationalize data and analytic workflows. Both GA4 and SAS acutely understand and enable a tremendous amount of features and capabilities in regard to instrumentation and implementation of data collection within digital properties. However, after these initial steps, the questions orbiting within a brand's business model will drive what is necessary from a data management perspective.
- GA4 analysts primarily focus on reporting, measurement and revealing descriptive and diagnostic insights. The topics of data quality, non-normal distributions, missingness and feature engineering tend to be outside the boundaries of typical GA4 analysts use cases. If such data problems are identified, GA4 administrators adjust implementation settings to amend.
- SAS analysts embrace these data management topics and use technology solutions to improve their ability to identify predictive and prescriptive insights. Solving data problems can equate to a diversity of data engineering solutions, typically handled by the individual themselves as opposed to depending on administrators. Keep in mind, SAS provides a data connector into the Google Cloud Platform for scenarios where brands prefer their analysts to use GA data for reporting/measurement, but leverage SAS data engineering capabilities using this information for use cases that involve customization, sensitive data or advanced analytical approaches not currently supported in GA4.
Conclusion: Although brands historically felt one digital analytics solution sufficed, as we progress through 2024, it is now a frequent observation to see brands investing into multiple solutions to improve their state of data readiness.
Moving on, analysts and data scientists use a combination of techniques to understand the data, visualize and build predictive models. They use statistics, machine learning, deep learning, natural language processing, computer vision, forecasting, optimization and other techniques to answer real-world questions. There are distinctly different flavors of analytical enablement between GA4 and SAS.
- GA4's strengths lie in its focus on pre-made reports applied to common marketing questions. Once the solution is implemented, digital data flows into a variety of templated analysis views summarized earlier in this article. With a skew towards marketers and no-code analysts, it's easy to appreciate the simplicity of the user experience. Alternatively, it is challenging to accommodate more sophisticated business questions that require custom-authored solutions. Some will debate that GA4's improvements from Universal Analytics (UA) added a "light" version of a modern data visualization platform that addresses these exploratory concerns. To a certain degree, this is true yet lacks many of the graphing benefits a user of a Power BI, Tableau or SAS Visual Analytics is accustomed to. New features like predictive audiences and anomaly detection are steps in the right direction, but the lack of transparency or customizable experimentation limits the massive potential from an advanced analytical perspective.
- SAS provides a robust number of analysis and modeling capabilities, from no-code visualization to low-code model pipelining and high-code programming windows using the proprietary SAS language. Trends of DIFM auto-insights through DIY-derived insights heavily influence the current state and future of SAS software. With that said, SAS never intended to build a solution exactly like GA4, rather it provides complementary solutions when a brand is ready to mature their analytical usage and actionability of digital customer data.
Conclusion: Anticipating an unprecedented year of Marketing AI hype, the opportunity of deriving incremental insight from 1st party digital customer data will be in focus. As consumers continue to spend more time interacting with brands digitally, the innovation within data science is begging every company to evaluate their technology stacks, analyst team skills and ROI on customer targeting initiatives. Our viewpoint is simple when comparing GA4 and SAS, and if a brand's investment budget allows, together is better. Whether through integration use cases, or leveraging the solutions individually to tackle specific use cases where one is better suited than the other, the value proposition is simple. To grow incrementally, brands need more business opportunities to take advantage of.
This naturally pivots to the subject of ModelOps, which focuses on getting AI models through validation, testing and deployment phases as quickly as possible, while ensuring quality results. It also focuses on ongoing monitoring, retraining and governance of models to ensure peak performance and that decisions are transparent. For readers who have spent more time in GA4 vs. SAS, this topic of ModelOps may (or may not) be familiar unless you have explored other modules within the Google Cloud Platform.
If topics like TensorFlow Extended, Vertex AI, BigQuery and Cloud Build don't come to mind, it's because Google requires it's users to navigate outside of GA4 to address a brand's objectives around ModelOps. From our perspective at SAS, Google offers powerful software to support ModelOps, but requires users to have high-code skills in Python, SQL, etc. to make use of their offerings. If this suits you or your team, then great. But if you want the benefits of ModelOps without the requirements of being high-code users, SAS provides an alternative solution to support no-, low- and high-code users in one platform.
Model management is not a one-time activity, but an essential business process. Models must be well developed and validated to demonstrate that they are working as expected. Outcome analysis is necessary to:
- Ensure that the scores derived from applying the model to new data are accurate.
- Verify that model performance over time remains satisfactory.
- Other aspects include cataloging and tracking this growing inventory of analytical assets, while providing support for the governance of these models using version control through repeatable and traceable workflows.
Practical considerations for data science emerge when an analysis worthy of addressing your marketing team’s business problem pivots the work stream to taking action via model deployment.
Conclusion: SAS enables brands to use first-party data to make better decisions using predictive analytics and machine learning in conjunction with business rules across a hub of channel touch points. As your brand's journey into analytical marketing use cases progresses, usage of modeling intellectual property cannot be under-exploited. It’s competitive differentiation awaiting to be deployed. If the ModelOps offerings on the Google Cloud Platform prove challenging to use because of the high-code user requirements, this provides another opportunity to leverage both GA4 and SAS together to address the upward trend of Marketing AI use cases.
All of this culminates into a brand's guiding light framed around scalable customer decisioning. Rooted in a variety of analytical approaches that can be leveraged within a wide set of marketing use cases, it doesn't matter if one or multiple technology solutions serve as the bridge to the finish line. There is innovation being served from the software industry to appreciate, explore and experiment with. We (at SAS) simply want to help our customers through partnership and adoption, and whether it involves working with an implementation of GA4 to derive incremental value for specific use cases, or using SAS standalone to resolve challenges in orchestrating meaningful customer experiences, one theme is clear.
2024 is showing a strong propensity for how marketing divisions will fall in love with analytics, machine learning and AI for an array of new customer use cases.
Two examples of well known companies describing their experiences and transformation from direct to integrated marketing/analytical-driven brands can be viewed at the links below:
Example 1: The Nature Conservancy [LINK]
Example 2: World Wildlife Fund [LINK]
Our vision at SAS is to serve as the market leader in advanced audience creation & targeting, independent of channel, for enterprise customers leveraging complex, disparate data sources and wishing to consistently deliver superior understanding into their customer journeys. In other words, we want to empower brands to practice responsible marketing.
Learn more about how SAS can be applied for customer analytics, journey personalization and integrated marketing here.
