How SilentSignals used ValueFlow's AI-led interviews to collect and analyze consumer insights

Challenge

Traditional consumer research methods — from one-on-one interviews to focus groups and surveys — often miss the subtle cues that reveal how customers truly feel. Hesitations, emotional shifts, or what customers don't say can be just as important as what they express.

For her consumer insights projects at SilentSignals, Nicole Ludwiak needed a solution that could uncover these "silent signals" — the unspoken emotional and behavioral indicators behind customer responses — to deliver more accurate predictions of future consumer behavior for her clients.

Traditional Approach

Before using ValueFlow, Nicole relied on conventional research methods:

• Manual interviews: time-consuming and limited in scale.
• Surveys: efficient, but incapable of detecting vocal nuance or emotional undertones.
• Focus groups: valuable but costly and prone to groupthink bias.
• Manual behavioral analysis: disconnected and labor-intensive.

While these tools gathered explicit responses, they often failed to detect the silence between words — moments like pauses, self-corrections, or subtle tone changes that can uncover true customer sentiment.

Solution with ValueFlow

Nicole turned to ValueFlow, an AI-driven interview platform that automates conversation collection and analysis.

The process is fairly straightforward. First, users determine whether they prefer a fully unique AI agent or can use a template that the company provides. Once the agent is named and a description about the interview context is added, consumer insight specialists can either provide the questions they want the agent to ask, or let the agent determine what type of questions should be asked depending on the topic of the interview. Once that is set up, users simply configure the parameters they want it to analyze, and they are done.

Via Link or QR Code the interviews are shared with the participants, which can then conduct with the configured interview agent.

After each finalized interview, the consumer insights specialists finds voice recordings, transcripts and the custom AI analysis of the customer interview in the ValueFlow dashboard.

Setup of Interview Agents

For her consumer insights projects, Nicole quickly set up three ValueFlow interview agents:

• Feature Request Interview Agent
• Power User Interview Agent
• Customer Support Interview Agent

The setup process — including defining the questions and interview topics — was smooth and took less than 15 minutes per agent. Each agent performed reliably from the start, delivering high-quality interviews and valuable first-round insights.

After analyzing the initial interviews, however, Nicole realized that her analysis framework was too broad — the insights remained somewhat superficial. Determined to go deeper, she began exploring how to surface the unspoken signals hidden in the interviews: subtle emotional cues, implicit needs, and the underlying motivations behind customer feedback.

In one of her projects focusing on customer support interactions, she refined her interview agent's parameters to detect genuine emotional signals in customer responses. By narrowing and specifying her prompts — moving beyond the general goal of "capturing emotional context" — she achieved significantly richer and more actionable behavioral insights.

AI Analysis of Interviews

Through her work on multiple consumer insights projects, Nicole found that while broader parameters provided useful baseline analysis, granular parameter configuration delivered the depth and specificity required for actionable behavioral insights. This refined approach became her standard methodology for uncovering the silent signals that drive consumer decision-making.

Building on this learning, Nicole developed a refined approach using 11 highly specific, atomic analysis parameters. Each parameter was carefully crafted to detect a particular type of behavioral signal, enabling her to identify subtle patterns that would otherwise remain hidden.

The analysis items in ValueFlow work on a prompt basis: users write a prompt that instructs the AI model what to look for, and the AI then applies this prompt to every available interview. When the consumer insight specialists defines the analysis items, is up to him / her — analysis items can be defined before interviews are conducted or retroactively applied to existing interviews, depending on when you know what specific insights you want to analyze.

Examples of Nicole's granular parameters include:

• interrupted_thought_analysis: Identify moments where users interrupt themselves mid-sentence or backtrack. Look for phrases like 'never mind,' 'actually forget it,' 'you know what,' 'doesn't matter,' 'anyway,' or trailing off with '...' These often reveal what users REALLY think but are too polite to say. Note what comes immediately before and after these interruptions.
• emotional_shift_detection: Track changes in tone, pace, or energy level throughout the conversation that might indicate shifting emotional states or discomfort with certain topics.
• hesitation_pattern_analysis: Identify prolonged pauses, filler words, or repeated phrases that suggest uncertainty or internal conflict about responses.
• correction_behavior_tracking: Detect self-corrections and revisions that reveal initial responses being modified, often pointing toward more accurate underlying thoughts.

Nicole applied this granular methodology across all 11 parameters, enabling her to uncover the subtle, unspoken insights of consumers to make more informed strategic decisions.

She discovered that ValueFlow's AI is most powerful when given precise, detailed instructions tailored to the specific behavioral patterns she wanted to identify. By investing time upfront to configure granular parameters, she was able to extract highly specific, actionable insights from interviews. The granular approach consistently delivered the nuanced analysis she needed to understand what consumers truly think, feel, and hesitate to say.

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Benefits of Using ValueFlow

ValueFlow proved to be a highly effective platform for Nicole's consumer insights projects. Each interview automatically generated both a voice recording and a complete transcript, providing a rich dataset for later analysis. Once configured, Nicole could have scaled her outputs effortlessly — whether 10, 100, or 1,000 participants were involved, the interviews could run simultaneously without additional manual effort. For larger scale analysis projects, this means a significant leap in efficiency compared to traditional, interviewer-led studies.

Nicole highlighted that the transcription accuracy is phenomenal. The system comprehended every word during the interviews. When configured with granular analysis parameters, the platform effectively identified both explicit responses and subtle, implicit signals that traditional methods would often miss. The pattern recognition was thorough and actionable, delivering detailed behavioral analysis that directly informed client strategies.

“ValueFlow shows tremendous promise for scaling consumer interviews. The platform's ability to capture natural conversations at scale and analyze parameters provides a great foundation for those willing to dive into uncovering the hidden voice of the customer with the fine-grained analysis capabilities.”

Nicole Ludwiak

Consumer Insights Analyst

Finally, the speed of analysis was a game-changer for Nicole's workflow. With just the press of a button, she received comprehensive insights in minutes rather than days. Perhaps most valuable was the ability to reanalyze completed interviews with new parameters, allowing her to extract additional insights from existing conversations without needing to conduct new interviews. This capability enabled her to deliver more comprehensive, iterative analysis while working within tight project timelines.

Future Plans for ValueFlow

Nicole continues to use ValueFlow for her consumer insights projects. She finds it particularly valuable for discovery calls with clients, enabling her to quickly understand their true challenges and pain points beyond surface-level statements.

One of her most powerful use cases involves using ValueFlow to conduct interviews that reveal how clients actually see their customers — not just how they describe their customer perceptions. This allows her to identify gaps between stated understanding and actual insights, providing more accurate strategic recommendations.

Nicole also plans to extend ValueFlow's capabilities to her own workflow optimization, creating an agent that helps her identify her own 'silent signals' — tracking thoughts and patterns throughout her week to better organize insights for discussions and content creation.