Customer listening paths: Explicit, implicit and algorithmic

Customer listening has evolved significantly over time. And now, we are entering another era of considerable change in how we listen to our customers.

Until the 1980s, customer listening was one-way, using focus groups, interviews, and paper or phone surveys. These explicit customer listening methods have stood the test of time—offering structured, reliable insights—especially for niche audiences. But they can be slow, costly and hard to scale. So, by the 1990s and 2000s, digital tools like online surveys, CRM systems and review platforms began to enable real-time, scalable feedback. These channels help brands respond quickly and transparently, especially mid-transaction or when markets shift quickly. However, these methods also bring challenges: information overload, low customer engagement, and the risk of customer frustration. They also require extra care to ensure compliance with privacy rules.

Listening between the lines: The rise of passive and social listening

In the 2010s, organizations embraced passive listening through behavioural analytics, social sentiment tracking and clickstream data—focusing on what customers did, not just what they said. Tools like Google Analytics, Hotjar, Brandwatch and Meltwater helped brands monitor behaviour, sentiment and engagement across channels. Note that information on social media profiles generally requires consent for marketing use, even when publicly posted. Implicit signals such as time on page, interaction rates and brand sentiment shifts, offered scale and more honest insights into customer preferences. They also brought challenges: interpreting intent, filtering noise, and ensuring marginalized voices weren’t overlooked.

From passive to algorithmic listening

The brand-customer dialogue has rapidly shifted, from explicit to implicit, and now to algorithmic. Powered by digital transformation and AI, listening is becoming more ambient, continuous and automated.

For example, consider how a company might use AI to analyze the text from thousands of customer service chatbot interactions. The algorithm can identify recurring product complaints, gauge overall customer sentiment, and detect emerging service issues in real-time. This allows the brand to listen and respond at scale, without relying solely on post-interaction surveys. It is crucial to remember that even this text-based data is personal information, and its collection and use for analysis requires clear disclosure and consent. 

We are starting to see a further shift from implicit, or less conscious, listening paths to algorithmic listening paths. Companies are beginning to offer tools to power customer service and personalization, for example, Google offering Vertex AI and Gemini. AI-driven agents are evolving from support tools into consumer engagement hubs—becoming the pre-eminent listening path for brands to understand how their consumers feel and what they want. As consumers continue to engage, the hubs create deep and unique customer profiles, providing an unparalleled opportunity for brands to establish listening paths and cater to millions of different consumer voices, as long as we're transparent about how these rich profiles are created and have the customer's permission to use them.

The role of AI in customer representation

Imagine a near-future scenario where a personal AI assistant knows your preferences, values and buying habits. “Bots” do the heavy lifting of representing customer interests, absorbing customer feedback, managing expectations, and curating relevant and personalized customer experiences. When you need new headphones, it doesn't ask you to search; it pre-filters thousands of options, reads every review and makes a shortlist. Your personal AI assistant already knows your budget and listening habits. It filters the noise, curates a shortlist, and maybe even orders them, interacting with brands and offers directly.

This shift fundamentally changes how brands “listen.” They're no longer just tuning into a customer; they’re interpreting data from an AI agent that acts on the customer’s behalf. This marks a profound change: listening becomes predictive, not just reactive, and brand strategy must evolve accordingly.

Managing brand presence in the AI listening era

Any consumer-facing AI input will become the pre-eminent listening path for brands to understand how their consumers feel and what they want. Moreover, each AI system “listens” and surfaces insights from vastly different digital sources. AI search firms have done a lot of research in this area. For example, AI search firm Profound has noted:

• ChatGPT relies heavily on Wikipedia (47.9 per cent) and Reddit (11.3 per cent) for its responses.
• Google AI Overviews lean on Reddit (21 per cent), YouTube (18.8 per cent), Quora (14.3 per cent), and LinkedIn (13 per cent).
• Perplexity cites Reddit most (46.7 per cent), then YouTube (13.9 per cent).

These patterns show that AI's "ears" are shaped by source bias—what it hears depends on where it listens. For marketers, that means understanding not just what AI is surfacing, but what environments it's drawing from. A strong presence in subreddits, as well as on short-form and long-form video platforms, community Q&A platforms, and even Wikipedia, can directly influence how a brand is represented in AI-driven conversations.

AI and data efficiency

Before diving into AI, businesses must clearly define their goals: Is the aim cost savings? More timely customer feedback? Simpler access to listening data across multiple teams? These goals shape how AI is implemented, ensuring it serves specific business needs rather than being implemented for the sake of technology.

Since AI generates vast amounts of data, to maximize the value, it's essential to focus on the right data, not all of it. The goal is to use it effectively to filter and prioritize data and focus on insights that matter most. For instance, AI can identify key touchpoints that indicate customer pain, enabling the swift resolution of critical issues.

While AI is a helpful tool, the human element is crucial. We must ensure AI customer listening insights align with strategic objectives and customer personas, making data actionable and relevant. Strong data governance is necessary to prevent overload, providing the right teams with access to the right information at the right time.

When implemented correctly, AI will enable companies to stay ahead of customer needs, drive engagement, save time and money and enhance overall performance.

Key takeaways

Below are a few takeaways to keep in mind:

1. Adopt AI-driven listening for personalization at scale

AI and algorithmic listening paths allow brands to interpret passive data (like behaviour) to create real-time, hyper-personalized experiences. This shift moves marketers from reactive to predictive engagement, enabling deeper customer insights with less direct input.

2. Maintain a balanced, inclusive listening approach

While AI offers scale, relying solely on it risks missing marginalized voices or misinterpreting intent. Combining explicit (e.g., surveys) and implicit (e.g., browsing patterns) data with algorithmic listening ensures a fuller picture, builds consumer trust, and improves decision-making.

3. Customer listening will include customer representation

By ingesting feedback and behaviours, AI customer engagement will allow the bots to do much of the heavy lifting of representing customers' interests. Additionally, strategic engagement with online sources of AI customer listening data presents a marketing opportunity for brands.  

4. Liken effective AI use to effective use of data

The effectiveness of AI depends on alignment with business objectives and establishing clear goals for the data. While AI can automate data prioritization, human oversight is necessary to ensure focus on the correct data and insights. This allows AI to support faster, more informed decision-making, leading to improved customer experiences and stronger business outcomes.

Methods:

Note: While this article focuses on strategic opportunities, we recommend consulting with your legal team when implementing technologies to ensure compliance. Privacy laws, particularly Quebec's Law 25, have specific requirements around consent and transparency for activities like tracking and profiling.

Authors:
Diana Brink-Gourlay, Vice President, Customer, Aviva Canada Inc.
Ursula Green, CXO, Senior CX Marketing Consultant
Scott Miles, Chief Client Officer, Active International

References: 
Outside In; Harley Manning, Kerry Bodine
Chief Customer Officer; Jeanne Bliss
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