# Made In turned Instagram DMs into a 365-day product lab, replacing focus groups and surveys.

*Direct conversation at scale delivers faster iteration cycles and higher-confidence product launches than traditional R&D.*

By **Jenny Huang Goodman MPA MSc MHSA, Principal** — The Stash Edge, Hako Shikin LLC.
Published 2026-09-09.

Canonical: https://www.pops4.com/stash/articles/made-in-2026-09-09t12-1
Subject: Made In
Tags: instagram, product development, customer feedback, dm strategy, community, made in

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Made In, the direct-to-consumer cookware brand, now develops products by reading Instagram DMs instead of commissioning research reports. According to Modern Retail, the company systematically mines customer messages to collect feedback, test concepts, and source new product ideas—treating the DM inbox as a real-time R&D channel. The approach has moved from occasional outreach to a structured discipline, with comparable signal quality to traditional focus groups but delivered at a fraction of the cost and latency.

The mechanics are straightforward. Made In monitors inbound DMs for recurring requests, pain points, and usage questions. When patterns emerge—customers asking for a specific pan size, requesting a tool the brand does not carry, or describing a cooking problem—the team flags the thread. Instead of building a deck and scheduling a focus group, they reply directly and ask follow-up questions in the same conversation. Prototype concepts are tested by sharing mockups or descriptions in DMs and tracking response sentiment. The loop runs continuously, with no gating by quarterly research budgets or external vendor schedules.

Why this works: Instagram DMs collapse the distance between observation and action. Traditional product development separates feedback collection from decision-making by weeks or months—surveys are fielded, data is cleaned, reports are written, and by the time insights reach the product team, the market has moved. DMs deliver unfiltered context in the customer's own words, attached to a profile the brand can see and a conversation history it can review. The format selects for engaged customers who care enough to reach out, which means higher signal density than broad surveys that incentivize participation with gift cards. The two-way thread also lets the brand probe ambiguous feedback immediately, turning vague complaints into specific feature requests within the same exchange.

The steal for a small physical-product brand: Turn on Instagram DM notifications and assign one person to read every inbound message daily. Create a shared document—spreadsheet or Notion table—with columns for date, customer handle, topic, and verbatim quote. Flag recurring themes weekly. When three or more customers mention the same need unprompted, that is a validated signal worth exploring. Reply to each message personally, even if the answer is just acknowledgment. If a customer describes a problem your current product does not solve, ask one follow-up question: what have you tried instead. Their workaround reveals the job-to-be-done and the price ceiling.

To test a new product concept, post a Story with a simple visual or description and invite DM replies. Monitor response rate and sentiment. If **15 percent** or more of Story viewers send a DM expressing interest, the concept has traction. If replies include unsolicited price anchors or feature requests, capture those verbatim—they are your launch messaging and your first A/B test variables. Budget for this: zero software cost if you use native Instagram, or under **two hundred dollars per month** for a basic DM management tool if volume exceeds **fifty messages per day**. Time cost is **two hours per week** to read, log, and pattern-match. The output is a prioritized product roadmap built from documented demand, not hypothesis.

The broader pattern: customer conversation scales better than customer surveys. Surveys ask what you think to ask. Conversations surface what customers think to tell you. The DM channel is already open, already staffed by customers who self-select for engagement, and already instrumented with context no survey can capture—purchase history, browsing behavior, and tone. The only requirement is that someone reads it and writes it down.

## The takeaway

DMs deliver unfiltered product feedback faster and cheaper than surveys—if you read them daily and log recurring patterns.

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## Publisher

**Hako Shikin LLC** — Virginia Beach, Virginia. Founded 1997. ASI 217876 · DUNS 18-204-6339.
Principal and author: **Jenny Huang Goodman MPA MSc MHSA**.

- Author: https://www.huanggoodman.com/about
- LLM context: https://www.pops4.com/stash/llms.txt
- MCP endpoint, for AI agents: https://mcp.pops4.com/mcp
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