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SAS for Digital Customer Analytic Measurement & Reporting

Started ‎10-07-2022 by
Modified ‎10-08-2022 by
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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, we have 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 your 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) applications. However, many brands seem to be hitting an adoption ceiling.


Image 1: Measurement and reportingImage 1: Measurement and reporting


At SAS, our perspective on this trend is the lack of customization (or personalization) that derails 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 exploit the potential of analytic measurement, interactive visualization and distributed reporting, numerous dimensions 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 a unique recipe of audience 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 you find 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.


The commoditization of many core visualization capabilities is well underway, whether we like or not. But the game is far from over. SAS recognizes the critical importance of serving multiple enterprise personas through augmentation (embedded AI and machine learning to assist in report authoring and consumption.) 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.


Image 2: Technology user personasImage 2: Technology user personas


Furthermore, what can 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-code environments to identify key drivers or influencers on a metric, find anomalies, or project a forecast/trend supported with natural language generation (NLG) to ensure accurate interpretation of insights.
  • What about data scientists and data engineers? It should not be overlooked that SAS Viya allows them 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. After all, when a modeling project reaches completion, it is common to author and distribute reports that summarize the value models deliver to audiences with lower thresholds of analytical literacy.


Let's pivot and focus on how SAS supports digital customer analytic measurement and reporting. SAS Customer Intelligence 360 offers a single JavaScript tag to collect website 1st/Zero-party interaction and element level data, pixel tracking, mobile SDKs, server-to-server APIs, and tracking for native/hybrid apps. Once any of these data streams are absorbed, the SAS Customer Intelligence 360 unified data model (UDM) contextualizes this information into OOTB/customizable structured tables for analysis enabling do-it-for-me (DIFM) software user features & do-it-yourself (DIY) analyst acceleration.


The subject areas 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 get to the technology demos and bring this to life.


Technology demo: Augmented dashboards, reporting & measurement


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.



We look forward to what the future brings in our development process – as we enable marketing technology users to access all of the most recent SAS analytical developments. Learn more about how SAS can be applied for customer analytics, journey personalization and integrated marketing here.


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‎10-08-2022 09:54 AM
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