2024 Trends & Viewpoints For The Future Of Customer Analytics

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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

The idea of designing an LLM-enabled user interface within a software product to help with user democratization is a reasonable hypothesis. However, practitioners reading this article will likely agree that genAI does nothing (or very little) to ensure the robustness of the underlying analyses. Instead of being instantly romanced by genAI's potential (which remains to be seen as use cases mature), readers should look for differentiated analytical techniques to innovate customer treatment strategies.

As a result of these recent trends, customer analytics 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 theirs 1st party 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. Many of us have heard that ~80% of the world’s data is unstructured, and the opportunities to harvest analytical innovation is front and center today. As customers and prospects increasingly interact with brands conversationally, all the resulting unstructured (natural language) data can be stored, contextualized, and deterministically blended with structured CRM data for richer insight potential.
• 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 teammate 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.

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:

• Acquisition
• Upsell
• Retention
• Segmentation
• Next-best-action (or experience)
• Recommendations
• Lifetime value
• Pricing personalization
• Attribution

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:

• Domain expertise
• Applied use cases
• Acceleration
• Simplification of analytically injecting data-driven intelligence into CX and marketing workflows

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:

• Recipe templates applied to customer & marketing-centric use cases
• Accessing data where it resides to avoid duplication & redundancy
• Flipping the 80-20 rule upside down
• Democratizing analytical base table (ABT) engineering
• Managing data quality, privacy, imbalances & irrelevancy
• Moving beyond the standard/typical CDP value propositions

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:

• Use case-driven recipes
• Moving beyond customer propensity scoring/likelihoods to optimizing marketing strategy profitability
• Assessment, interpretability & explainability
• Federation of customer analytical scoring
• Responsible AI & governance for CX & marketing

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.