Building Products that Matter to the Customer

Company A built an extremely sophisticated, technically flawless platform and launched it with much fanfare. The president sent congratulatory notes, and the teams went out for happy hour. Two months later the customer experience metrics either stayed stationery or slipped further down. Customer support costs skyrocketed. The product became bloated with useless features. The result: Sky rocketing acquisition costs, increasing customer churn, employee burnout and so on.

This is obviously a hypothetical scenario – there is no such thing as a technically flawless platform! But it is a case study that, unfortunately for the customer, happens far too frequently in the real world.

What did Company A miss? They forgot about the customer. No customer ever said, “I hope they used fancy AI and cool automation technology to build their products”. Every customer does have the expectation that the products you build will reduce cognitive load, respect their time, and solve their problem quickly and easily.

Building truly customer-centric products requires moving past “what do users want” to asking “why do the users need it”. Here is a quick breakdown of some best practices as we unpack this philosophy:

• Don’t start with technology. Don’t begin by making important decisions like the kind of agentic framework to use, the target architecture, or whether to buy or build. These should come much later, after you have understood the problem. Instead, start by walking a few miles in the customer’s shoes.
  1. Look at where the customer is failing across the entire journey from the time they enter the funnel, see how often and where they are dropping off.
  2. Identify new features/products, then create a value-effort grid of everything you have identified that will make it easy for you to understand what type of product you should be building and what features you need to prioritize.

• Metrics are important, but the right metrics are everything. Move from output to outcome metrics.
  1. Measure what the customer sees and feels. Example of a common red flag: Company A rolled out a feature that has 98% adoption but returns negative customer satisfaction. The metrics dashboard celebrated the adoption and never looked past that.
  2. Layer quantitative telemetry on top of qualitative data from user feedback surveys, focus groups etc, to get the full picture. Don’t be alarmed if the two do not always tell the same story. What customers think they want and how they actually behave is the “Say Do” gap. You can only close that gap by looking at both types of data together.

• Plan and design for iteration and feedback. Waiting six months for a feature to improve is not an option:
  1. Design modular architectures that allow you to isolate and work on small chunks of features.
  2. Deploy experimentation platforms. Use synthetic and historic data to automate customer experience evaluation dashboards so that you can test against baseline CX metrics even before it reaches the customer.
  3. Insert customers into your feedback loop in real time. Perhaps you have identified a critical point in the journey where customers are abandoning and you simply don’t know why. Automate a micro-survey at this point in the journey, something light and quick like “looks like you are leaving, anything we can do better”?
  4. If you are using Agentic frameworks, enable real-time agent evaluations that supply dynamic feedback and automatically update your product roadmap every few minutes.

Building products that are relevant to the customer is the most reliable way to drive growth, increase revenue and improve satisfaction. A solution that solves a customer problem has much higher premium than the technology behind it. When the focus is on the customer, the product is a true market differentiator. Anyone can build products, but those who are customer–centric build lasting products that continue to evolve and meet the customer where they want to be met.

Shri has built her career at the intersection of customer experience, digital transformation, product strategy, and enterprise technology, helping organizations modernize how they serve customers, scale digital businesses, and prepare for the next era of AI-enabled engagement.

Across 20+ years in telecommunications, healthcare, financial services, insurance, and technology-driven environments, she has led transformation through ambiguity, complexity, and rapid change. Her work often begins where mandates are unclear, legacy systems limit progress, data ownership is fragmented, and teams need a stronger operating model to move from vision to measurable execution.