How AI closes the gap in building customer-centric products

Putting customers at the center of product development should be a given; after all, they will be using the product, so why wouldn’t they be front of mind? There is also a direct line between prioritizing customer feedback and bottom-line metrics. Companies who listen to their customers experience 1.6 times more growth.

Yet knowing that customer-centricity is a route to success and being customer-centric are two different matters.

The barriers to customer centricity: Data overload and team silos

What’s stopping customer-first development from becoming a reality? There are two fundamental reasons: an overwhelming volume of feedback to parse; and product functions that operate in silos.

Depending on the company and its industry, feedback might come from social media, support tickets, sales calls, customer interviews, face-to-face meetings, email, or chatbots.

The format might vary from structured data with strict rules and logic to informal, off-the-cuff remarks. The employees receiving the feedback might work in support, sales, or marketing; they might be employees with extensive training or contract workers brought in to help with a single project.

We know there is gold in all those insights. If a product team can capture, analyze, and action it, organizations can engage with their customers at a level most struggle to reach.

And that’s the other part of the equation that makes true customer-first development difficult: the silos between functions. Organizations could be gathering valuable insights already, but is it getting to the right people at the right time?

Many product organizations have invested significantly in user experience research to help understand which features could delight their customers and supercharge revenue growth. Yet, there’s a disconnect between the data collected and what product teams actually use when building their roadmap. The result is products that do what companies think customers want rather than what they truly need.

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AI is purpose-built for processing large volumes of data

When used appropriately, artificial intelligence could offer a solution to both of these issues.

Tackling the first part is pretty straightforward: AI lives for large volumes of data. Processing vast amounts of customer interviews, social media comments, support tickets, surveys, and emails becomes much easier using AI. Models can be trained to find and classify themes into feedback that help teams identify opportunities for improvement. For instance, in this way, what might appear to be isolated anecdotes  from customer interviews can be cross-referenced with support tickets to highlight issues impacting customers at scale. Or it could help by focusing on specific data ranges, such as after an update or fix, to help measure the impact of the change and see if it has had the desired effect. .

Used in this way, AI can partner with functions tasked with sorting through data and finding valuable insights. But it must be a partner, not a replacement. Unthinkingly relying on AI to tell you what customers want without interrogation or human involvement can have a negative impact.

For instance, models that aren’t monitored and prompted within closely defined guardrails run the risk of hallucinating, where the AI makes up the answers. If unchecked, this could distort insights and wrongly influence product development.

Keeping humans in the loop is vital to continually reviewing and quality-testing outputs. The AI provides signposts to key themes across your many channels of customer data to dive deeper into, whether you’re a researcher, designer, product manager, or leader.

Breaking down silos with AI

How can AI help solve the problem of functional silos? By giving everyone access to customer data, so they can pull out the insights they need without having to conduct their own research.

Embedding customer centricity throughout the organisation requires democratizing customer research. This involves giving people a way of finding valuable insights quickly in ways that fit their workflows. Not everyone needs or should be a UX researcher, but they should have access to the feedback that matters to their jobs.

For example, a CEO will not have time to sift through lengthy customer satisfaction surveys or hundreds of support tickets. However, a two-minute video of a customer struggling with a feature, perhaps captured as part of a live troubleshooting session, could be something they can consume and respond to.

It’s visual, it’s real, and they can literally see what the issue is. Previously, finding that clip might have been too much for an overworked and under-resourced product team; using AI to draw out the key themes, now they can home in on specific problems and find the feedback (and the format), that suits the team and their intended internal audience.

Silos remain when different functions can’t communicate. This is especially true at large organizations dealing with massive amounts of customer data. Different workflows, targets, and objectives all contribute to making it harder to communicate. Therefore, having AI that can rapidly uncover relevant information and help teams to  present feedback  to multiple audiences can help overcome those barriers. In doing so, this can bring everyone closer to the customer across the product development lifecycle.

The AI-powered customer-centricity opportunity

Once, not having enough customer feedback might have been an issue; now, it’s having too much and not knowing what to prioritize.

From analyzing vast amounts of standard and non-standard data to surfacing insights and allowing non-specialists to get closer to the research process, AI can help overcome some of the obstacles companies face to being customer-centric. When deployed appropriately and with the right support and oversight from human colleagues, AI can help close the gaps many companies have and help them build more customer-centric products.

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