# Made In turns Instagram DMs into product development channel, ships cookware based on 300+ customer conversations per week *The cookware brand mines direct messages for feature requests and prototypes, then validates demand before manufacturing.* 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-2026-09-09t09-4 Subject: Made In Tags: instagram, product development, customer feedback, dm strategy, community listening --- Made In built a cookware line by listening to Instagram DMs at scale. According to Modern Retail, the brand collects product feedback and new item ideas from hundreds of weekly direct message conversations, then uses that input to decide what to manufacture next. The company is not alone—Plainspeak and Springrose run the same play, treating the DM channel as a continuous focus group that costs nothing and runs in real time. The mechanics are straightforward. Made In's team monitors incoming DMs for recurring feature requests, material preferences, and complaints about existing products. When a specific ask appears repeatedly—say, a request for a carbon steel wok or a smaller saucepan—the brand logs it, estimates volume based on message frequency, and decides whether to prototype. The DM channel also surfaces objections: price resistance, confusion about care instructions, or requests for bundles. That intelligence feeds directly into copy, FAQ updates, and product roadmaps. This works because the DM is the highest-signal channel a physical product brand has access to. Email is passive. Comments are performative. A customer who opens a DM to ask a question or make a suggestion has already cleared the friction threshold. They care enough to type. That self-selection creates a dataset far more useful than a survey, and it arrives unsolicited. The brand does not have to chase it. The second mechanism is speed. A DM-based feedback loop compresses the product development cycle. Instead of commissioning research or waiting for quarterly reviews, Made In can spot a pattern in two weeks and decide to prototype in three. The brand is not designing in a vacuum. It is designing in conversation, which reduces the risk of shipping something nobody wants. According to Modern Retail, this approach has allowed the company to validate demand before committing to tooling or inventory, a critical advantage in a category where minimum order quantities run into the thousands. A small physical product brand can run this play with one person and no software. Start by enabling DMs on Instagram and setting expectations in the bio: "DM us your questions, requests, or complaints." Respond to every message within 24 hours. Keep a shared spreadsheet with three columns: message type (question, request, complaint), content summary, and date. At the end of each week, review the log for patterns. If five people ask for the same colorway, add it to the product backlog. If ten people complain about packaging waste, redesign the mailer. The cost is time, not money. A solo founder can handle **50 DMs per week** in under two hours if the responses are templated and the logging is disciplined. The payoff is directional certainty. You know what your customers want because they told you in their own words. That intelligence also feeds email copy, landing page bullet points, and paid creative. If twelve people asked whether your cutting board is dishwasher-safe, put the answer in the headline. If eight requested a gift box option, add it to the product page and run a test. The broader pattern here is that the highest-leverage market research for a physical product brand is already happening in your DMs. You do not need to buy it. You need to log it, act on it, and close the loop by telling the customer when you shipped their request. That last step turns a support channel into a retention engine. ## The takeaway Track every Instagram DM in a shared log, review weekly for recurring requests, and prototype the patterns that appear most often. --- ## 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 - Client dashboard: https://dashboard.pops4.com/ - Catalogue: 70,000+ products, 200+ brands