The last few years have upended the way customers select to interact with brands. Digital engagement has led to higher customer expectations, rising demands on marketers for personalized interactions across all channels, and an increased likelihood that customers will jump ship if brands don’t deliver. Generative AI (genAI) is THE buzzword of the last two years, being discussed everywhere, and the customer analytics ecosystem is no exception.
Image 1: Consumer Demands Are Higher Than Ever
Part 1: 2024 Trends & Research Insights
Now, allow me to invite readers to check out this introductory video extracted from a recent on-demand webinar summarizing the 2024 observed trends in the customer analytics ecosystem.
Part 2: User Types, Creating Synergy, Removing Friction & Recipes
Think about the magnitude of requests that come in from customer experience and marketing teams to their supporting analysts. The wish list includes actionable scoring for topics like:
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 2: Requirements Meeting Between Data Science & Marketing
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 analytics.
In the Part 2 video below, we cover an array of topics across:
Part 3: Recipes 101 & Acceleration
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 3: Recipes 101 & Acceleration
Topics that will be covered in the Part 3 video include:
Part 4: Projects 101 & Welcoming Everyone Else To The Analytics Party
Now, the big question that has been posed to analytical technology companies year-after-year is whether data-driven insights can bring positive momentum to mission-critical KPIs. This brings us to another important topic because I have a message for my data science and analyst brothers/sisters. There is more to activation than just scoring your model!
Image 4: Projects 101 & Empowering CX & Marketing Teams
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 let's complete this by discussing the democratization of CX & marketing team enablement via customer journey orchestration and prescriptive activation.
Topics that will be addressed in the final Part 4 video will include:
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.
Image 5: SAS for Responsible Customer Engagement
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 customer analytics technologies ecosystem, check out fresh (and unbiased) research here.
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