The Power of Deep Listening for Consumer Insights

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Is your business really responding to consumer needs and wants, as the customer sees them? Are you truly customer-centric, or is your customer experience program more of a “just make sure sales stay strong” thing? Are your insights programs unbiased, or is your research narrowly focused on sustaining business-as-usual with limited capacity to support innovation and ensure resilience?

We need more than journey maps, customer-satisfaction scores, and media metrics to stay ahead of change and identify opportunity. That’s why I’m a proponent of deep listening, a communication technique that starts without preconceptions, designed to discern the authentic voice of the consumer.

We record and study contact-centre and automated bot conversations. We survey our customers and employees and conduct focus-group and community discussions. We read reviews and monitor social and online media. But — and there’s always a but — when we frame our research and draft our questions, we tend to consider only our own business needs. That’s understandable, however I guarantee that brand concerns such as “How do I sell more and more widely?” aren’t top-of-mind for Jennifer Consumer. We also tend to over-focus on interactions and transactions, that is, selling and providing service. We neglect relationships and the feelings that underlie them. Loyalty is rooted in perceptions and emotions and not just product features and prices.

You have to put aside your assumptions — you have to ensure that your research and listening are comprehensive and unbiased — to get at truly foundational insights into consumer needs and values, motivations, and activation. This is where deep listening comes into play.

Deep Listening

Deep listening is founded on genuine interest. You center your interlocutor, not yourself, and you allow that person to direct the conversation. Sure, you can ask questions intended to clarify and prompt further discussion — let’s call this variation active listening — but the keys are to identify your community and hear their voices with an open and empathetic mind and to attend to both facts and feelings.

You can go narrow or you can go broad. I’m a proponent of broad, of looking beyond your customer base to prospects and influencers on the one hand and employees, partners, and market channels (e.g., retailers that sell your products) on the other. Your research extends beyond employee experience (EX) and also beyond customer experience (CX) in that we’re interested in non-customers’ views — if you’re in healthcare or government, substitute “patient” or “constituent” for “customer” — and it goes beyond brand experience in that we interested in the totality of the voice of the market.

Human analyses are fine, if by individuals trained in emotional intelligence techniques. However humans don’t scale. If you’re operating in multiple languages or mining social and online media and other high-volume sources, you’ll want to apply appropriate data and analytical technologies. These include conversational and emotion AI technologies — including natural language processing, voice analytics, biometrics, and behavioral modeling — that help you automate interactions, data collection, and analyses. They help you go deep. (These are topics that speakers and panelists will discuss at the up-coming CX Emotion conference, taking place July 22 online.)

And how do you design your surveys and data collection and analyses to produce deep insights?

Principles and Implementation

We can adapt mindfulness-movement principles to our organizations and our business networks, elements such as self-awareness, authenticity (which I’ll translate as transparency), and being present. While mindfulness concepts such as stilling and openheartedness aren’t associate with business, nonetheless we can map these principles to our organizational work. Try this: You have to be genuine, to engage in deep listening as a learning experience rather than as a selling opportunity, and you have to be empathetic, that is, understanding of your interlocutor’s life circumstances.

Culture and context count. They provide explanatory power, but also they’re the leading source of bias, which may result from lack of diversity, skewed data collection, poor choice of machine learning and analytical algorithms, data mis-labeling for model training, misinterpretation of findings, and misapplication of insights. That’s a lot to look out for! It points to careful validation and process measurement and evaluations.

I’ll offer a summary of other considerations in the design of a deep-listening initiative:

  1. Define your business context, based on your mission, market, goals, and operating conditions. Context provides purpose for your research.
  2. Design your universe to encompass the wide set of customers, prospects, and influencers, and employees, partners, and market channels — or instead keep the scope more modest — but in all cases, watch for prejudice and bias.
  3. Don’t sell. Listen.
  4. Use technologies such as conversational AI (bots) to scale and systematize the engagement and listening processes.
  5. In conducting surveys, ask open ended questions. Apply text analytics, behavioral analysis, and emotion AI, again to systematize your evaluations.
  6. Don’t rely only on keywords, boolean expressions, and rules to pull data from social and online media. These methods exemplify “feature engineering”; they’re limited to confirming or denying what you already know to look for. Don’t be afraid to carefully apply machine learning and an exploratory data-science approach.
  7. When possible, build models that draw on multiple signals or correlate across multiple data sources, to improve accuracy and provide explainability. How does what people say correlate with what they do?

I’ve skipped inconvenient realities such as budget and time and the need for executive buy-in. If you’re good at what you do, you know how to get the resources to do the job you need to do. That job is to ensure resilience and sustain innovation in difficult times, and to out-perform when good times return. Deep listening will help you by providing comprehensive and forward-looking analyses. Down with bias and preconceptions, and up with research and insights founded on empathy, authenticity, and consumer-centric take on the voice of the market!

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