How to use Generative AI to build better products with customer feedback

Tiffany Go
Head of Product Marketing
March 13, 2024

This is a recap from our conversation with Shyvee Shi on the topic of how to use Generative AI to build better products with customer feedback.

Shyvee: What initial spark or problem led you to start Enterpret, and how did your background prepare you for this venture?

Varun: The idea for Enterpret was inspired by my experience building out the customer success team at Amplitude, where I led hundreds of deployments of product analytics for small consumer apps to large Fortune 500 companies.

I witnessed the evolution of product analytics as a category and, more importantly, observed a critical gap in understanding the 'why' behind product analytics. Teams can track retention and their conversion funnel metrics to the decimal, but if you ask the simple question, “What are the top three customer pain points?” Crickets. Regardless of size or industry, most teams lack a clear understanding of customer pain points or actionable insights.

Enterpret began to take shape in 2020 after I had worked on NLP projects at Scale.ai. My brother Arnav and I started exchanging ideas about how to solve the gap in product analytics using the rapid advancements in AI. Arnav’s background is in computational linguistics and NLP research, and he led the Engineering team at Uber, so he was very much at the forefront of research and development in the space. Together, we’ve embarked on this venture to decode customer feedback into actionable insights for product development using AI.

Shyvee: Can you share some real-life case studies from companies that have used Enterpret to empower their product organizations?

Varun: We’re lucky to work with some of the most customer-centric companies in the world, like Canva, Notion, Loom, and Apollo.io. Here are a couple of ways we’re helping these teams:

Notion Improves Product Load Times: Notion uses Enterpret to run its Voice of Customer initiatives to help prioritize complaints. Enterpret helped to identify the top issue around slow product load times. After they shipped these enhancements, Enterpret helped verify a 50% improvement in load time and a 65% reduction in related complaints, illustrating the impact of combining quantitative and qualitative feedback for product optimization. These improvements enhanced user satisfaction and validated the effectiveness of the changes through direct customer feedback.

Apollo.io connects feedback revenue impact to drive their products forward Apollo.io uses Enterpret to consolidate all feedback and customer interactions to align the organization around the Voice of Customer. These VoC insights help Apollo.io to identify the Pareto, which is 20% of the issues that cause 80% of the damage or 80% of the inbound contacts. It has empowered the team to leverage their engineering resources to ship valuable features while minimizing bug fixes and" keep the lights on" work. The team has seen a 40% reduction in support issues around themes they’ve chosen to address.

Shyvee: Can you share your biggest challenges while building Enterpret and how you overcame them?

Varun: The biggest challenge was altering the perception of customer feedback from a procedural task to a critical, trackable dataset. We tackled this by demonstrating the tangible benefits of actionable insights derived from feedback analysis, emphasizing the importance of integrating customer perspectives into the analytics framework.

Another challenge was ensuring the adaptability of our AI models to the fast-evolving nature of customer feedback and product development. Continuous innovation and a flexible approach to model training are helping us stay ahead.

Shyvee: What are some unique considerations working with generative AI?

Varun: Working with generative AI requires meticulous attention to the format and quality of customer interaction data. We've developed processes to clean and standardize data, removing irrelevant patterns and ensuring consistency. This foundation allows us to leverage AI to generate granular and actionable insights. Additionally, we focus on natural language interactions, enabling users to query our database effortlessly, thus making feedback analysis an integral part of the product development cycle.

Shyvee: How does Enterpret position itself to stand out? What are some “moats” Enterpret is building for long-term sustainability?

Varun: Enterpret distinguishes itself by providing granular, actionable AI models tailored to each customer's unique needs. Every Enterpret customer has a custom taxonomy built to reflect their customers, product, and business. Our unique AI models can adapt to each customer's specific feedback landscape.

Unlike generic solutions, our "custom-fit tailored suit" approach ensures that insights are highly relevant and actionable, making us a preferred choice for product and customer experience organizations seeking to base decisions on deep, actionable customer insights.

We are also deeply committed to making the feedback analysis process seamless. By continuously investing in AI and ensuring our models remain at the cutting edge, we create a competitive advantage that is difficult for new entrants to replicate quickly.

Most importantly, when it comes to learning and acting on customer feedback, it boils down to, "What is the revenue impact of improving this thing, and is this the highest leverage investment we can make?” By keeping a live mapping of all of your customer feedback across all sources to the system of record for user/account/revenue data, we enable product teams to answer questions that matter. Enterpret is connecting feedback to revenue impact to inform critical decision-making. See it in action.

Shyvee: How do you envision the future of product development changing in the next 3 years, and what role do you hope to see Enterpret play?

Varun: As digital transformation accelerates with the emergence of generative AI, I see product development becoming increasingly customer-centric. Feedback analysis will continue to play a more pivotal role in shaping products.

Enterpret aims to be at the forefront, enabling companies to understand and act on customer feedback at scale, thus ensuring products meet and exceed customer expectations.

Shyvee: As you reflect on the journey so far what has been the most pivotal or significant learnings during your journey building Enterpret?

Varun: I have always believed in building a team of people united in their shared conviction that a problem is worth solving.

The second is to stay humble. I look for people with a humble mindset and a strong desire to learn and improve.

Reflecting on the journey, these two principles of great people and humility have guided Enterpret. This experience has only reinforced my belief that with the right team and a humble growth mindset, challenges can be transformed into opportunities for innovation and growth.

Check out Shyvee's book:Reimagined: Building Products with Generative AI”. You'll learn more about frameworks to think through which use cases should use generative AI and how companies can build moats using AI. The book features over 150 real-world examples, 30 case studies, and 20+ frameworks, “Reimagined” offers an extensive guide for integrating generative AI into product strategy and careers. Grab your copy on Amazon: https://a.co/d/btmnJfu.

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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
CPO, Apollo.io
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
CPO, Apollo.io
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, Apollo.io
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, Apollo.io