Over the last few years, brands have continued to explore and stress-test new mechanisms in deriving actionable analytical insight. An emerging trend that continues to garner attention orbits around trust which is critical when using analytics effectively. In this article, we will focus on the intersection of data and analytical literacy with how information is used to build models, how we accurately make model interpretations, and how we can elevate the confidence in an individual's ability to use algorithmic scoring to make intelligent decisions.
Our promise at SAS to help you overcome business problems is to gain deep customer knowledge that extends to action through seamlessly enhancing and extending customer data activation.
Before jumping into the technology demo below, let’s ease in by addressing an important point. Digital transformation is now a top issue for most executives. It’s what unlocks the real-time insights and agility needed to deliver value to customers in the moments that matter. In reality, most real-time data is underused or not used at all. But few data processing systems are truly real time. Most systems capture real-time data in log files, then group events and batch process them to yield patterns that drive something close to real-time rules-based decision making. In other cases, insights from this data come weeks or months later – and in aggregate. Making the most of real-time customer interaction and streaming digital data requires real-time processing tools.
Decisioning is best used to drive real-time actions in three contexts.
With that said, Let me propose a thought…
Is all AI built the same? No. Yet it seems every software vendor is pitching it. Let me be clear. It isn't enough to capture and store 1st/2nd/3rd/Zero-party customer data. Further, feeding this information into automated or custom prediction and segmentation models helps serve the marketer's mandate with accessible analytically-derived scores, but still falls short on potential.
Do you want to know what wedges separation for competitive advantage? Decisioning engines enhanced with model governance, AI bias detection and supervised/unsupervised/reinforcement/deep learning insights triggered by real-time customer behavioral events contextualized by forward-thinking CDP capabilities. Think CDPs with a DataOps and ModelOps enablement mentality. If you take the time to lean in and look closely, it becomes obvious that all AI, ML & analytical solutions are not built the same.
The content shared in the technology demo below for the application area of customer next best experience represents an exciting opportunity to showcase technology and approaches across SAS Customer intelligence 360 and SAS Viya for different profiles of users (marketers, analysts and data scientists) in the context of customer analytics. While we attempt to maximize diversity, it should be noted a reasonable, non-exhaustive number of examples will 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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