How product teams validate assumptions and hypotheses in Enterpret

Vivek Kaushal
Product
January 31, 2024

Hey there, I’m Vivek, and I’m a Product Manager at Enterpret!

At Enterpret we use Enterpret to build Enterpret. Our Product Team uses Enterpret in many ways, from helping us measure the success of launches to how we make product decisions.

One of the most important product use cases for Enterpret is the ability to validate our assumptions and gauge the success of our bets. Being able to join quantitative and qualitative feedback quickly helps us to automate our feedback loops so we can efficiently move our product forward.

In this post, I’ll share a short case study of how we iterated quickly after our recent Reason Creation Launch by validating our hypotheses in Enterpret.

Why we launched Creating Reasons in Enterpret

We launched Creating Reasons to solve a challenge our customers had when working with our Taxonomy. While having an LLM powered Taxonomy that automatically tags feedback saves time, it does not always address the challenges of:

  • Customers talk about feedback in different ways than your internal teams
  • Predicting the level of granularity your business wants to understand specific and important pieces of feedback
  • Customers talk about the same problem in multiple different ways

In listening to our own customer feedback 😃 we realized that our users wanted the ability to define Reasons on top of the existing Taxonomy. Enter, Create Reasons. This functionality gives our users more agency and control to quantify and track areas of customer feedback in Enterpret.

Digging into the Create Reason Launch Results

There are three steps in Creating a Reason:

  1. Users describe the Reason they wish to create — we use this description to curate definitions and find matching feedback
  2. Users can then review curated definitions, add/remove definitions
  3. Users can confirm the definition and create Reason (success)

Here’s the funnel in Amplitude:

After launch we noticed a sharp drop-off in transitioning from step 1 to 2. With a 23% drop-off rate.

Digging into the Analytics and some Assumptions

We did not know exactly know why users are dropping off. After looking at the product feedbackl data our team had some working theories:

Blank-slate Problem — it’s difficult to start describing a Reason you want to create. This is intimidating to users, and they’re struggling to describe the Reason effectively.

  • Problem to Solve: What would be more helpful instead?
  • Assumption: We show examples of descriptions to fight the blank-slate problem. Are the examples not helpful? why? what can be better?

Just Checking Out — It’s a new feature, with a prominent product announcement. Users might very well be just checking the feature out, without an intention to actually create a new Reason.

Validating our Assumptions in Enterpret Using Feedback to Gain Clarity

Using the Enterpret <> Amplitude Integration we are able to sync over the cohort of 23% users.

In Enterpret we can zoom into the feedback from this cohort of users by creating a Search for:

  • Feedback on Reasons
  • There’s also the option where we can filter by user_email in {email addresses who dropped off}

By zooming into the launch feedback our team is able to validate if our hypothesis of a blank-slate problem is true. We can also understand “why” it’s a problem, and start to answer questions of

  • Why did providing Create Reason Examples not help?
  • What could have been better?

Prioritizing Using Impact Analysis

Once we have our validated assumptions we take the next step of deciding whether the work gets prioritized by using Synced Users and Accounts.

Everything is a tradeoff. Synced Users and Accounts allows us to analyze the impact of addressing a particular issue on different customers and revenue.

I hope you found this case study on how product teams can validate assumptions and hypotheses in Enterpret helpful! If you have any questions feel free to reach out to me vivek@enterpret.com or find me on X @vi_kaushal!

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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