Guides guide
How I Found My First Buyers for AutoComplaint
2026-08-05 · 6 min read
TL;DR
I looked for people already dealing with the exact problems AutoComplaint helps resolve: delayed refunds, defective products, missing deliveries, rejected complaints, and support tickets that went nowhere. The useful lesson was to begin with current problem evidence, then qualify for fit and urgency, rather than starting with a large demographic list.
The product and the discovery problem
AutoComplaint helps Indian consumers prepare and escalate complaints involving e-commerce purchases, including unresolved refunds, defective or incorrect products, missing deliveries, and support requests that have stalled. The early GTM challenge was not defining a broad audience. It was finding people experiencing one of those problems at the moment help would be useful.
I searched for the problem, not the product name
Early buyers were unlikely to search for “complaint automation.” They described the event in ordinary language: a refund had not arrived, a marketplace rejected a return, a seller stopped responding, or support kept closing the ticket. Searching for that language produced stronger buying context than monitoring the AutoComplaint name or a formal product category.
- Refund promised but not received after the stated processing window.
- Defective, damaged, counterfeit, or incorrect product with a rejected return.
- Order marked as delivered when the buyer had not received it.
- Repeated support contacts without a resolution or meaningful escalation.
- Questions about consumer forums, notices, chargebacks, or the next escalation step.
The signals that made a conversation worth reviewing
A complaint alone was not enough. I prioritized conversations where the issue was current, the buyer had already tried the normal support route, the amount or inconvenience justified further action, and the person was explicitly asking what to do next. Those details separated a potential buyer from general frustration or an old story.
How I qualified product fit
| Signal | What I looked for | Why it mattered |
|---|---|---|
| Current intent | The person wanted a next step, escalation path, or practical remedy | Created a real reason to consider help now |
| Pain depth | Money, time, access, or trust was materially affected | Showed that solving the issue had value |
| Prior effort | Normal seller or platform support had already failed | Reduced the chance that a simple support reply would solve it |
| AutoComplaint fit | The issue involved an Indian e-commerce transaction and a documentable complaint | Kept the workflow within the product’s actual scope |
What I avoided
- Treating every negative review as a lead.
- Sending a generic product pitch without addressing the person’s actual question.
- Claiming legal outcomes or promising that an escalation would succeed.
- Automating replies, messages, votes, or account activity in public communities.
- Collecting more personal information than the complaint workflow required.
What the first-buyer search taught me
The strongest learning was that useful segmentation begins with the triggering event. “Indian online shoppers” is a very large audience; “a buyer with a rejected refund who has exhausted marketplace support and is asking how to escalate” is a specific, timely use case. That specificity improved both product feedback and the clarity of the AutoComplaint message.
How this shaped MySocialAntenna
The AutoComplaint use case reinforced the reason for building MySocialAntenna around product context instead of keyword volume. The goal is to find public conversations with evidence of intent, pain depth, advice-seeking behavior, and solution fit, then let a human review the original source and decide whether any participation is appropriate.
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Author
Priyanka B
An ex Product Manager and Software Engineer, now building products independently in AI and using AI.