Always-on customer-pain and competitor research
Voice-of-customer intelligence, on autopilot
It reads what customers say in their own words — across forums, review sites, online groups, and video — and watches what competitors are quietly doing, then writes it up. Every week, on its own, instead of once a year by hand.
Knowing what customers really think and what competitors are quietly doing is hugely valuable — and so painful to gather by hand that most companies do it once a year, if ever.
An always-on system that reads what customers say in their own words and tracks competitor moves across the whole web, then writes it up — weekly, on its own, for one operator instead of a research team.
| Before | After | |
|---|---|---|
| How often it runs | once or twice a year, if at all | weekly for customer pain, monthly for competitors |
| Effort per round | days to weeks of manual reading and tagging | set up a profile once, then collect the report |
| What you get back | a stale snapshot | a live feed of real quotes and competitor moves |
| Cost to run again | another full sprint | near zero — the engine just runs |
| Serving more than one company | a whole new project each time | swap one settings file |
A slow, expensive, manual sprint became a standing capability that costs almost nothing to run again — and the same engine serves a different company by swapping one profile file. Intelligence that used to be a once-a-year snapshot is now a live feed: you catch a competitor's price change or a wave of a specific complaint the week it happens, not the quarter after. The kind of market awareness that normally takes a funded research team now runs from one person and a folder of settings.
What I built
A research engine that does the reading, scoring, and writing that normally takes a market-research team. It isn't an app anyone logs into — it runs as a shared engine (search, extract, dedupe, report) with a small profile file per company holding that company's competitors, search terms, target customers, and the categories of pain to watch for. To serve a different company, you swap the profile, not the engine.
- What customers actually say. It mines real customer quotes from forums, review sites, online groups, and video — and from the company's own feedback surveys and support tickets — tags each by theme and sentiment, and links every quote back to its source so you can check it. Built on the data store the company already ran, instead of an expensive specialist license.
- Always-on pain and competitor tracking. Agents that run on their own keep mining customer pain and watching a named set of competitors, surfacing buying signals as they appear and a monthly log of what each competitor changed.
- Quotes turned into copy. A year of open-ended survey answers, mined into the raw material for a landing page aimed at one specific kind of customer — written in words customers actually used.
- Competitor ad intelligence. A tool that collects competitors' ads and "reads" the images, then writes up the gaps worth attacking and turns them into creative briefs.
Each cycle the engine searches, pulls and scores quotes, drops anything it has seen before, and writes a report ready to hand to sales and marketing.
Why it matters
The payoff is awareness that stays current. Instead of a once-a-year snapshot that's stale before it's read, the team gets a live feed — they catch a competitor's price change or a surge of a specific complaint in the week it happens, not the quarter after. Speed is the obvious part; the real unlock is cadence and reach. Because the whole thing is driven by a small settings file, one person can run it for many companies at once, and the customer quotes flow straight into sales calls, landing-page copy, and roadmap calls — the signal moving to where decisions actually get made, continuously.
Normally, "always-on customer and competitor intelligence" describes a funded software product with a research team behind it. Here it's one operator and a folder of settings, running on tools the company already owned.
The hard part isn't collecting text — anyone can scrape a forum. It's telling signal from noise. Most of what people post online is irrelevant, and a system that surfaces everything is as useless as one that surfaces nothing. So the real work is in which sources are worth trusting, how the searches are worded to find genuine pain rather than chatter, and how each quote gets scored for urgency and buying intent. The second hard part is making it never repeat itself: every quote and link is remembered, so each weekly report is only what's new — not the same complaints surfacing again and again. That discipline is what turns a firehose into something a team can actually act on.
The shift here is from snapshot to live feed: the same questions a research team answers once a year now get answered every week, by one person, at almost no cost to run again. Customer pain and competitor moves land where decisions get made — in sales calls, landing-page copy, and the roadmap — while they're still fresh enough to act on.
Which sources to trust, how to phrase the searches, and how each quote gets scored are what separate real signal from noise — and the part clients pay for.