PRFAQ: Echo, the Product Research AI

North Star Vision

Product and research teams often have to source customer feedback to validate product concepts or seek out new unmet customer needs in markets looking for a better solution. The typical process usually starts with qualitative interviews:

• 1:1 interviews with a researcher yield large amounts of rich insights but are slow and can only be done on a small scale (less than 100).

• With market research tools like UserTesting and dscout, teams can source qualitative feedback at scale, but then research and product teams become the bottleneck, spending countless hours sifting through recordings looking for usable feedback and insights.

After qualitative research is over, the team then faces another challenge - we've found these customer pain points, but how do we prioritize which one to focus on? The product team can't base their roadmap on the opinions of 10-15 interviewees, so now a quantitative study is needed to estimate the size of the opportunities so that the team can focus on the largest ones.

• Survey tools like Qualtrics and SurveyMonkey are abundant, but while survey results are valuable in sizing they lack the context of why someone chose any specific answer (or may have even preferred an answer that wasn't built into the survey)

• All surveys come with a bias problem when they're being designed - the bias of what product and research teams took away from qualitative studies and chose to include in a survey question or answer choice. Normal cognitive biases can lead teams into groupthink, rejecting anything that doesn't already confirm existing beliefs about the market and the value of the solution the team is likely already leaning towards (not helped by the pressure of top-down initiatives).

By now, the team has likely spent months on research and are being pressured to launch their MVP. They may have skipped the qualitative or the quantitative research step entirely to save time, which can either cause them to focus on a problem that doesn't actually exist, or a problem that's not large enough to justify investment.

Echo, the Product Research AI provides the benefits of both qualitative and quantitative research studies, in a fraction of the time and effort. 

1. The product team goes through the normal process of setting up a qualitative research study (screener surveys, interview guides for 1:1 interviews, stimulus and questions for prototype testing)

2. All sessions are recorded and transcribed

3. Echo runs these qualitative studies at a large scale, in the hundreds up to thousands (with human researchers in 1:1 interviews)

4. It then analyzes voice to text transcriptions to provide summaries, pattern matches similar feedback, and visualizes the data to provide teams with high level insights of what's most critical

5. It links everything back to its original source - the customer feedback and recordings that fed its summaries and data visualizations. The product teams can double click into any summary or data visualization to find the related recordings and feedback, to develop a deeper understanding of the context for decision making.

Product teams save time by only running one comprehensive study, and only deep diving into the pain points that matter most to their customers. Valuable customer feedback does not have to be skipped over in favor of speed to market, and teams can launch more useful and successful products.

Press Release

Stop launching bad products.

Img Credit: Dilbert by Scott Adams

A new startup EchoInsights AI has launched EchoAI, aimed at helping product teams build better products with customer feedback at scale.

Product and research teams often rely on customer feedback to validate product ideas or identify unmet market needs. Traditionally, qualitative interviews with researchers uncover a wealth of insights but are time-consuming and limited in scope. Tools like UserTesting and dscout enable large-scale feedback, but teams get bogged down analyzing recordings for actionable insights.

Post-qualitative analysis, teams grapple with prioritizing customer pain points for the product roadmap. A handful of interviews aren't sufficient for strategic decisions, prompting the need for quantitative studies to gauge the scale of opportunities. While survey tools like Qualtrics and SurveyMonkey quantify data, they often miss the context behind choices, not to mention the inherent bias in survey design influenced by pre-existing team beliefs and top-down directives.

This extensive research process can cause product teams to potentially skip vital steps to expedite launch, which risks product teams focusing on negligible problems or non-issues.

Echo, the Product Research AI from EchoInsights AI, streamlines this by combining the depth of qualitative studies with the breadth of quantitative analysis. Teams set up studies as usual, but the AI scales the research to hundreds or thousands of sessions, analyzing voice to text transcriptions to identify patterns and visualize critical insights. It retains breadcrumbs back to original feedback, enabling teams to explore context deeply.

By running a single, comprehensive study, product teams save time, avoid overlooking valuable feedback, and launch more impactful products, ensuring they address the most significant customer pain points.

"I love doing interviews with my customers, but I always hate the part afterwards - knowing that no matter how insightful I found these interviews to be, I can't just ask leadership to take a risk on just the opinions of a few people," says Sr Product Manager Jane Smith at XYZ, a Fortune 100 company. "Since these interviews can take a while to run, compile, summarize, and share out, I always had a challenge getting the go ahead to run yet another study to do the quantitative analysis and vet out which opportunities to focus on. This AI research tool does EVERYTHING in one go - I don't have to ask for another study, I just have to run one and find out what the numbers are telling me to focus more on."

Lead Researcher Eric Daniels works closely with product teams and finds current research tools lacking in their ability to answer all his stakeholders' questions. He's been using Echo for product discovery and has already seen improvements. "I never have to stare at a pie chart or bar graph from a survey and wonder, 'Did these people understand the question they were answering? What else are we missing that we forgot to include in the survey?' Echo's most powerful asset is its ability to link all the way back to the customer's feedback, timestamped exactly to the point in the recording it's basing its finding on, so you can listen in and even hear the tone of voice your customers are using. It gets at the ground truth of what matters most to your customers, and this truly empowers product teams to develop the critical insights and empathy they need to excel in product discovery and definition."

"I've always wondered why there were so many bad products," says EchoInsights AI's CEO, John Maven. "When I started working at a large company, I started forming a hypothesis as to why it happens - there's often too much pressure on product teams to launch something quickly, but usually that means it's not coming from a place of market insight.

"Executives and CEOs are too busy running the company - they're not as close to the ground or to their customers as product teams are, but they're also pressured to deliver growth each quarter to their shareholders. So you can get these high pressured, top down initiatives that ultimately end in failure - product teams are too rushed to do proper research and testing, and as humans we also tend to have biases that lead us to interpret any feedback we do get in favor of what we're already planning to do anyway. There's a lot of pressure to launch so that's just short circuiting our abilities to be creative and thoughtful, and it isn't helping with our biases either. 

"With Echo, you can get analysis that is less biased, and you can run the kind of research your product teams know they should be doing at a much faster clip. You don't have to sift through hours and hours of recordings of qualitative interviews to only find the 10-30% of feedback that was actually useful or related to the biggest pain point in the market - the AI runs the analysis at scale and quantifies similar feedback, so you can just focus on the biggest problems and opportunities in the market. Its linking capabilities can then take your teams all the way back to the raw data and help you uncover the human need and context, so you can develop the product sense that drives truly great product teams."

Product Features

- Voice-to-Text Transcription: Captures every detail of user feedback during prototype testing.

- AI-Driven Summaries: Creates structured summaries of feedback, ensuring no insight is overlooked.

- Interactive Feedback Review: Users can confirm and refine summaries, ensuring their thoughts are accurately represented.

- Quantitative Analysis: Aggregates feedback into visual data representations, streamlining the analysis process.

- Emotion and Nuance Recognition: Identifies and highlights emotional responses and subtle nuances in feedback.

Benefits:

- Accelerate product development with precise and comprehensive user feedback.

- Save on research costs by reducing reliance on external firms and streamlining the feedback process.

- Increase market share and user engagement by developing products that truly meet user needs and preferences.

- Enhance accuracy in interpreting feedback, accommodating for accents, language nuances, and emotional responses.

Achieve Business Goals

- Accelerate the creation of superior products by leveraging detailed and accurate user feedback, driving revenue growth and market share.

- Significantly reduce the time and expense associated with traditional product testing methods.

- Enhance customer engagement and satisfaction by incorporating their detailed feedback into product development.

User Friendly

- Echo is designed to remove friction from the prototyping research experience, making it easy for test participants to share their feedback in a seamless way that feels natural

- It caters to a diverse user base by accurately interpreting a range of accents and phrases, and by recognizing the emotional tone of feedback.

How it Works

- The AI operates in real-time during user studies, providing immediate summaries for users to review for quality.

- It incorporates advanced disambiguation logic to clarify user intentions and preemptively addresses potential misunderstandings, ensuring the feedback is as clear and useful as possible.

- It then compiles data post-study for comprehensive research summaries and quantitative analysis.

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