The new blueprint for customer experience: Always on, always listening

Engaging today’s customers is like trying to hit a moving target. Needs and expectations shift by the minute. Generic staging through a predictable funnel no longer captivates audiences numbed by choice. To earn loyalty amid endless options, brands must deliver value through relevance and anticipation in the moment.

This article reveals how to implement the technologies and strategies necessary to understand, align and act on individual customer contexts for situational engagement.

CX today requires looking beyond the funnel

For years, we’ve used linear models to map the customer journey, assuming a predictable path from discovery to loyalty. However, rising consumer expectations and rapid technological changes have disrupted the orderly framework.

Modern customers’ needs and expectations now shift situationally moment-to-moment, not in predictable linear phases conforming to neat traditional funnels. Think of the massive popularity of the modern subscription product model — we can try out the full features of a product for a term before even committing to make a purchase. Churn is easier than ever, so we must focus on loyalty and retention even ahead of conversion. 

On top of that, as customers, we willingly demand instantaneous personalization in real-time, not generic staged engagements that fail to adapt based on individual context. The explosion of the direct-to-consumer model has been fueled directly by a brand’s ability to deliver personal product offerings to its customers. 

This new paradigm demands brands meet customers with relevance in the moment while continuously optimizing those engagements through contextual insights and signals. AI and automation make this achievable.

The new CX imperatives for brands (Content + Signals):

• Always on: Deliver situational relevance powered by understanding each customer as close to a segment-of-one as possible.
• Always listening: Rapidly interpret signals to predict the next best actions rather than reacting post-behavior.

This approach to hyper-personalized content velocity and predictive intelligence presents the next era of intelligent customer experiences. And it is powered by the connectivity of data, insights and triggered actions.

Enabling always-on relevance

Situational content alignment refers to the ability to instantly tailor messaging, offers and creative to each individual customer based on understanding their current real-time context. 

At a high level, this can be achieved through four sequential areas of focus:

• Collect. Collect and integrate diverse data types to build comprehensive customer profiles.
• Map. Create dynamic personalized content models mapped to micro-segments.
• Create. Generate tailored content assets to align with each visitor.
• Analyze. Optimize content through real-time performance analytics.

Let’s explore these in detail, understanding their objectives, the technology platforms that empower them and how AI can be leveraged within each to enhance the solution.

Collect

Personalization requires a deep level of understanding of your customers and their behaviors. To understand our customers, focus on collecting and integrating diverse data types to build comprehensive customer profiles, encompassing their behaviors, preferences and interactions across various touchpoints. 

This is where customer data platforms (CDPs) are essential in their ability to offer a centralized repository to aggregate data from a myriad of sources (e.g., CRM, web/email/mobile analytics, Social media management, ecommerce, POS, IoT, customer service and chat, survey tools, etc.). 

The CDP is the dumping ground for all customer touchpoints and arguably the most essential tool required to deliver a brand’s ability to embrace customer-centricity. 

By using AI/ML algorithms, CDPs can significantly enhance the automation of data cleansing, refine data quality and implement preventative measures to ensure clean and accurate data. 

This saves time and manual effort in aggregating data from the external capturing systems and reduces human error and speeds up the data preparation process.

Dig deeper: AI-powered features to look for in customer data platforms

Map

AI-powered CDPs process immense datasets to surface insights that can refine customer profiles into smaller and smaller segments (“micro-segments”), surpassing what could be achieved through manual interpretation.

AI can detect nuances of customer behavior across profiles and then automate the segmentation process with granularity and extreme depth, predicting the most effective content for each micro-segment and moment. 

With micro-segments identified, the decision on what content to deliver to that individual customer profile is made using tools such as a dynamic content optimization (DCO) platform. We can assign specific content strategies that resonate with each micro-segment at various stages of their journey. 

Applying dynamic content models to micro-segments sets the rules for our personalized content strategy, enabling unprecedented levels of customization in responding to individual customers.

Assemble

DCO creates personalized content by using real-time data (visitor activity) and specific user information (profile match from the CDP). It follows content strategy rules defined in the DCO and combines them with pre-designed, flexible, and modular creative templates. This approach ensures the delivery of highly relevant and engaging content to individual customers throughout different touchpoints and stages of their journey.

DCO leverages machine learning algorithms to decide which creative elements (ad copy, images, CTA) to adjust based on the firm understanding of the visitor’s identity compared against profiles in the CDP, all within milliseconds.

This advanced communication and decisioning is only possible through connected systems driven by machine learning, which can interpret advanced consumer signals, then deliver contextually relevant, hyper-personalized content.

Assess

Measuring in-market performance is essential for improving how content is delivered. It helps enhance decision-making, monitor results, and make continuous adjustments to optimize engagement.

This is where machine learning shines in its ability to self-assess and improve based on a feedback loop. Real-time interaction management (RTIM) systems and integrated analytics tools equipped with AI algorithms assess how customers engage with content. 

This real-time analysis enables immediate adjustments to content strategies based on what is most engaging to customers, ensuring that marketing efforts are always aligned with customer expectations and preferences.

The journey toward always-on enablement is comprehensive, requiring a thoughtful integration of technology and data across customer identification, content creation, delivery and analysis. Still, if orchestrated strategically, you can unlock the potential to deliver truly personalized, dynamic customer experiences at scale.

Listening with predictive intelligence

Historically, brands reacted to customer behaviors only in hindsight once an outcome had already occurred, missing opportunities in the moment. AI changes this by quickly interpreting signals to understand emerging needs immediately.

For example, an ecommerce company can now analyze every step millions of shoppers take across multiple channels — from browsing categories to social shares and then predict the likelihood and timing of a single individual customer’s propensity to make a purchase. 

Or a streaming service can determine subscribers at risk of canceling even before they threaten to quit. This is achieved through four primary areas of focus:

• Collect. Aggregate and unify data from diverse sources to create a comprehensive view of customer interactions and behaviors.
• Analyze. Identify patterns and insights within the collected data, understanding customer preferences and behaviors.
• Predict. Forecast future customer actions, determining what they might need or do next based on historical data.
• Trigger next-best-action. Act on predictive insights to engage customers with the right message at the right time.

Collect

As discussed earlier, CDPs are the central repository for collecting data from various sources. They provide the foundational data necessary for AI algorithms to analyze. Without this comprehensive data collection, AI wouldn’t have the information needed to identify patterns or make accurate predictions.

Analyze

After collecting data, AI and machine learning algorithms analyze it swiftly to identify subtle patterns in customer actions. This process is often much faster than humans alone could detect trends across millions of data points, enabling the identification of patterns that might not be immediately obvious.

This step is where the “connecting the dots” happens. AI algorithms sift through the collected data to understand customer behaviors and preferences, which is critical for making accurate predictions about future actions. 

Predict

Predictive analytics tools use the insights generated by AI algorithms to score the probability of future customer actions. These tools can predict which products a customer will likely buy next, when they might make a purchase, or if they’re at risk of churning. This predictive capability is key to moving from a reactive to a proactive approach to customer engagement.

Trigger (next-best-action)

Marketing automation and journey optimizer platforms use predictions to automate personalized marketing actions. Based on the results of the predictive analytics, these platforms can trigger targeted emails, personalized product recommendations or customized offers to customers at the optimal time.

This is the execution phase, where insights and predictions are translated into real actions. Marketing automation platforms ensure that the recommendations are delivered to the customer through the right channel and at the right moment, completing the cycle of predictive engagement.

Delivering always-on, always-listening customer experiences

Over time, AI continually gets smarter by learning from outcomes to refine predictive models and signals. This means you can pivot experiencing design from reactive to proactive using AI to understand emerging desires during micro-moments of engagement.

An always-listening strategy represents a holistic approach to customer engagement, predicated on listening, predicting, engaging and optimizing with unparalleled precision.

By leveraging advanced technologies and AI, you can transform reactive data capture into a dynamic dialogue with customers, anticipating their needs and exceeding their expectations. This proactive engagement model elevates the customer experience and fosters loyalty and long-term value crucial for modern market advantage. 

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