Customer Feedback as a Moat

Varun Sharma
Co-founder, CEO
June 1, 2023

In business, a moat is an advantage that goes beyond survival - it establishes a company in such a way that competitors can’t easily copy the company’s success.

Many companies have moats, but not every moat is the same. Facebook has a network moat - everyone’s friends and family are already there, and as a result, it’s very hard for a newcomer to replicate that user base. Amazon Web Services has a switching cost moat - companies looking to migrate out of AWS often have to pay tens of millions of dollars. Microsoft has a corned resource moat - arguably, they have the best enterprise distribution engine.

The list goes on, but the point is clear. Companies with staying power have moats. With AI and machine learning driving ever-shortening feedback loops, we’re starting to see a new moat emerge: collection and analysis of customer feedback.

Market and Execution Risk

It feels self-evident that customer feedback is important. Y Combinator’s mantra is “build something people want” and Amazon prides itself as a customer-centric company.

In building a company or launching a product, you face two risks - market risk and execution risk. Market risk is when people don’t need or want the product. Execution risk is when competitors can execute better and take market share.

Airbnb skewed toward market risk. No one knew if there would be a market for renting a spare room in someone’s house. On the other end, Zoom was more execution risk. When they started, video conferencing was already a well-understood space with multiple alternatives.

Customer feedback tackles both risks.

In a new market, customer feedback is often the only way that you can gain signal on their product. Airbnb had their breakthrough moment when they realized that poor photography was a barrier to bookings. To solve this, Joe Gebbia, cofounder of Airbnb, went to NYC and started taking photos for the hosts.

We would go in, we take photos. And then we’d show them on the back of the camera and say ‘hey, what do you think?’ and they’re like ‘oh my God, my apartment looks so good! do you want to stay for some tea of coffee?’ And, so, I would sit down on the couch, and our earliest customers, much like yours, where zealots. These were the people willing to take a risk, try a new weird crazy product, be kind of outcast amongst their friends, which meant that they had a lot of knowledge. They had a lot of insights into this kind of activity because they were already doing it in other web sites.

- Joe Gebbia, cofounder of Airbnb

Fast-forward a decade and today, Airbnb is an integral part of millions of travel plans.

In execution risk, discovering and solving customer pain points is your best competitive advantage. Zoom’s founding vision came from customer pain points in using WebEx - poor video/audio quality and a choppy mobile experience. As Zoom took off, customer feedback showcased that customers wanted more than just a good conferencing solution, they also cared about privacy and security.

Anyway, I think to build a better solution, you’ve got to spend more time with your customers. Ultimately, it was innovation. Innovation is really about you want to be the first company to understand customer pain points. And, also, take actions quickly, and to be the first [vendor] to build a solution. If you keep doing that, sooner or later, you are going to win. So, that’s our approach. That’s the reason other competitors lost.

- Eric Yuan, CEO of Zoom

When you leverage user feedback to gain insights and make improvements, your products fit users better. Your new features are more on target. And your priorities are closer to what your customers need.

These small advantages accelerate progress and, over time, they accumulate and create an obstacle against other competitors in the market. They become a competitive moat.

Customer feedback is hard

Even though it feels obvious that companies should incorporate customer feedback into how they design their product, how and how well they do it is subject to tremendous variance.

The distance between vision and implementation is a function of getting the correct context out of conversations with customers. That context isn’t always obvious and often requires significant investments of time, energy, and money to discover.

The largest difficulty with customer feedback isn’t actually obtaining it - customers will suggest improvements, ask for help, send in complaints, and praise new features by themselves. But customer feedback is noisy, and it’s difficult to find a useful signal in that noise.

Part of this problem is that feedback comes in dozens of different forms. A couple of years ago, companies sent out annual satisfaction surveys, keeping feedback in a neat and tidy box. Today, any SaaS company has FullStory sessions, support tickets, emails, dedicated Slack channels, Tweets, and sales call data. These are all distinct types of data that don’t play well together; they carry different tones, come from different kinds of customers, and arrive in different formats.

On the positive side, you now have an almost endless stream of customer data to mine for insight. On the negative side, it takes exponentially more energy to make sense of all the information.

When a company is small, a founder can look at all the incoming feedback themselves and get a good mental image of customer priorities. But that doesn’t scale - eventually, responsibilities are delegated, and no member has direct access to more than a small fraction of customer feedback. When a company’s largest prospect delivers feedback that they won’t commit before they get a niche feature they need, it’s hard for the company as a whole to judge whether it’s worth the engineering time to build it.

Customer data also means different things to different parts of the company. The sales team might place additional emphasis on customer requests for new features, the support team on ensuring existing features don’t break, and the product team on balancing tradeoffs across different priorities from leadership and customers.

All this adds up to a time and energy investment that most companies aren’t able to make. It’s not that companies don’t understand the value of feedback. It’s that most companies struggle to sort through the noise and extract the context they need to fully leverage customer feedback. Yet, by walking away from customer feedback, they leave valuable insight and product direction on the table.

Feedback and AI - the moment is now

In an ideal world, the feedback you receive should be fed into your company’s roadmap, messaging, growth plans, and culture. Part of what made Amazon so successful was their laser focus on customer experience; they sought out a wide variety of types of feedback and then relentlessly focused on improving the customer experience to drive the rest of their company flywheel.

The landscape and scope of customer experience has evolved in the three decades since Amazon was founded. Today, we’re at a point where analyzing the sheer bulk of customer feedback is no longer a job humans can handle unassisted.

The solution is AI. More specifically, large language models (LLMs). Before, companies were getting overwhelmed with the volume and diversity of incoming feedback. Now, LLMs can synthesize distinct pieces of feedback and find trends. They can help you identify new and growing problems that were previously lost in the noise. Most importantly, they can package all of this in a way that humans can read and understand at a glance, so anyone in your company can access the context they need.

That’s why we started Enterpret. We set out with the mission of building analytics for customer feedback - bringing Amazon’s flywheel to startups and Fortune 500 companies alike.

To do this, we build a custom ML model for each of our customers that ingests data directly from various data silos such as Zendesk tickets, Twitter tweets, and even Slack/Discord messages. The model automatically categorizes feedback into various themes and reasons. Product and engineering teams can then query on the model to understand sentiment, find insights, and ultimately drive product outcomes.

There’s a growing distinction between companies that can properly leverage customer feedback and ones that can’t. More feedback, properly analyzed by large language models, has led to actionable insights, better products, and ultimately more revenue.

We’ve worked with Notion to empower “a holistic view from our social media coverage, to our support tickets, to every single interaction that we're plugging into it.” That then led to additional insights for the product team, including answers to product questions such as “Hey, what are the top requests related to search? When do users report issues with real time collaboration?”

For more information on our partnership with Notion, see our case study on How Notion is supercharging its product feedback loop using Enterpret.

In a world where users are becoming more well-informed and enterprises are grappling with the disruption of new technology, it becomes increasingly important for you to be aligned with the customer. Doing that means a distinct advantage in distilling market trends, locking in customers, and achieving higher margins. In other words, collection and analysis of customer feedback has become a moat.

Customer Feedback and AI FAQ

Question: What specific examples does the article provide to illustrate different types of moats, such as network moats, switching cost moats, and cornered resource moats?

Answer: The article provides examples of different types of moats to illustrate how companies establish and maintain competitive advantages. For instance, Facebook's network moat is highlighted, emphasizing the challenge for newcomers to replicate its extensive user base of friends and family. Amazon Web Services' switching cost moat is explained through the significant financial investment required for companies to migrate away from its platform. Additionally, Microsoft's cornered resource moat is mentioned, emphasizing its superior enterprise distribution engine.

Question: Can you elaborate on the challenges companies face when it comes to incorporating customer feedback into their product development processes?

Answer: One of the main challenges companies face in incorporating customer feedback into their product development processes is the overwhelming volume and diversity of feedback sources. Feedback comes in various forms, such as support tickets, emails, social media posts, and sales call data, making it difficult to extract meaningful insights. Moreover, the noise-to-signal ratio poses a significant obstacle, requiring substantial time, energy, and resources to sift through and identify actionable feedback.

Question: How does AI, particularly large language models (LLMs), address the issue of handling large volumes and diverse sources of customer feedback?

Anwer: The article discusses how AI, particularly large language models (LLMs), addresses the challenge of handling large volumes and diverse sources of customer feedback. LLMs can synthesize disparate pieces of feedback, identify trends, and package insights in a comprehensible manner for human interpretation. By leveraging AI, companies can automate the process of analyzing customer feedback, enabling them to extract valuable insights efficiently and effectively.

Question: What are the benefits that companies can expect to gain by effectively leveraging AI-driven analysis of customer feedback, as mentioned in the article?

Answer: Effectively leveraging AI-driven analysis of customer feedback offers several benefits, as outlined in the article. These benefits include gaining actionable insights, improving product development processes, and ultimately driving revenue growth. By understanding customer needs and preferences more comprehensively, companies can refine their products, enhance user experiences, and maintain a competitive edge in the market. The article also mentions specific examples of companies, such as Notion, that have utilized AI-powered analysis of customer feedback to enhance their product offerings and achieve success.

Question: How does the article define a "moat" in the context of business, and why is it considered essential for a company's long-term success?

Answer: In the context of business, a "moat" is described as an advantage that extends beyond mere survival, establishing a company in a manner that makes it difficult for competitors to replicate its success. It serves as a protective barrier around a company's market position, making it challenging for rivals to penetrate and compete effectively. The article outlines various types of moats, including network moats (e.g., Facebook), switching cost moats (e.g., Amazon Web Services), and cornered resource moats (e.g., Microsoft), each offering unique competitive advantages.

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We are laser-focused on giving customers more than they expect through a hospitality-first, individualized approach to drive retention and loyalty. Enterpret has allowed us to stitch together a full picture of the customer, including feedback and reviews from multiple data points. We now can super-serve our loyal customers in a way that we have never been able to before.
Anna Esrov
Vice President of Customer Experience & Loyalty
Enterpret allowed us to listen to specific issues and come closer to our Members - prioritizing feedback which needed immediate attention, when it came to monitoring reception of new releases: Enterpret picked up insights for new updates and became the eyes of whether new systems and functionality were working well or not.
Louise Sellars
Analyst, Customer Insights
Enterpret is one of the most powerful tools in our toolkit. It's very Member-friendly. We've been able to share how other teams can modify and self-serve in Enterpret. It's bridged a gap to getting access to Member feedback, and I see all our teams finding ways to use Enterpret to answer Member-related questions.
Dina Mohammad-Laity
VP of Data
The big win-win is our VoC program enabled us to leverage our engineering resources to ship significantly awesome and valuable features while minimizing bug fixes and" keep the lights on" work. Magnifying and focusing on the 20% that causes the impact is like finding the needle in a haystack, especially when you have issues coming from all over the place
Abishek Viswanathan
Since launching our Voice of Customer program six months ago, our team has dropped our human inquiry rate by over 40%, improved customer satisfaction, and enabled our team to allocate resources to building features that increase LTV and revenue.
Abishek Viswanathan
Enterpret's Gong Integration is a game changer on so many levels. The automated labeling of feedback saves dozens of hours per week. This is essential in creating a customer feedback database for analytics.
Michael Bartimer
Revenue Operations Lead
Enterpret has made it so much easier to understand our customer feedback. Every month I put together a Voice of Customer report on feedback trends. Before Enterpret it would take me two weeks - with Enterpret I can get it done in 3 days.
Maya Bakir
Product Operations, Notion
The Enterpret platform is like the hero team of data analysts you always wanted - the ability to consolidate customer feedback from diverse touch points and identify both ongoing and emerging trends to ensure we focus on and build the right things has been amazing. We love the tools and support to help us train the results to our unique business and users and the Enterpret team is outstanding in every way.
Larisa Sheckler
COO, Samsung Food
Enterpret makes it easy to understand and prioritize the most important feedback themes. Having data organized in one place, make it easy to dig into the associated feedback to deeply understand the voice of customer so we can delight users, solve issues, and deliver on the most important requests.
Lauren Cunningham
Head of Support and Ops
With Enterpret powering Voice of Customer we're democratizing feedback and making it accessible for everyone across product, customer success, marketing, and leadership to provide evidence and add credibility to their strategies and roadmaps.
Michael Nguyen
Head of Research Ops and Insights, Figma
Boll & Branch takes pride in being a data driven company and Enterpret is helping us unlock an entirely new source of data. Enterpret quantifies our qualitative data while still keeping customer voice just a click away, adding valuable context and helping us get a more complete view of our customers.
Matheson Kuo
Senior Product Analyst, Boll & Branch
Enterpret has transformed our ability to use feedback to prioritize customers and drive product innovation. By using Enterpret to centralize our data, it saves us time, eliminates manual tagging, and boosts accuracy. We now gain near real-time insights, measure product success, and easily merge feedback categories. Enterpret's generative AI technology has streamlined our processes, improved decision-making, and elevated customer satisfaction
Nathan Yoon
Business Operations,
Enterpret helps us have a holistic view from our social media coverage, to our support tickets, to every single interaction that we're plugging into it. Beyond just keywords, we can actually understand: what are the broader sentiments? What are our users saying?
Emma Auscher
Global VP of Customer Experience, Notion
The advantage of Enterpret is that we’re not relying entirely on human categorization. Enterpret is like a second brain that is looking out for themes and trends that I might not be thinking about.
Misty Smith
Head of Product Operations, Notion
As a PM, I want to prioritize work that benefits as many of our customers as possible. It can be too easy to prioritize based on the loudest customer or the flavor of the moment. Because Enterpret is able to compress information across all of our qualitative feedback sources, I can make decisions that are more likely to result in positive outcomes for the customer and our business.
Duncan Stewart
Product Manager
We use Enterpret for our VoC & Root Cause Elimination Program - Solving the issues of aggregating disparate sources of feedback (often tens of thousands per month) and distilling it into specific reasons, with trends, so we can see if our product fixes are reducing reasons.
Nathan Yoon
Business Operations,