Email inquiry
An email inquiry is an email sent to a slice of users asking them, in their own words, about their situation: what brought them to the product, what they were trying to do, what was going on when they signed up. Replies come back as freeform messages rather than form submissions, and the team reads across them looking for patterns in which situations keep showing up and how users describe what they're after.
Definition
A user value research method that sends an open-ended prompt by email to recent customers, treating freeform replies as both pattern-clustering data and an entry point to deeper qualitative engagement.
A single open question
The instrument is a single open question — "what brought you here," "what were you trying to accomplish" — placed in the body of an email sent to recent customers. The user replies in plain text. The reply lands in the inbox of a real person at the company and gets logged for analysis.
Two jobs, one reply
The first is data: text describing how a user understood their situation when they came in, in their own language. Aggregated across respondents, that text supports intent clustering — what patterns of context, motivation, or starting state recur across the population. The second is relational: a reply that warrants deeper exploration becomes a natural opening for follow-up. "Thanks for sharing — could we jump on a call to dig into it?" lands differently than cold recruitment because the recipient has already engaged once. The continuity from email reply to phone conversation is part of the design.
The six-step arc
- Send the open-ended prompt to recent customers, formatted as a hand-sent-feeling email. The prompt invites a freeform reply in the email body itself.
- Aggregate replies into a spreadsheet — one row per reply, raw text preserved, columns left open for categorization.
- Read for pattern across replies without imposing categories upfront. Look for recurring situations, contexts, or framings that cluster across multiple respondents.
- Propose categories from the patterns observed, adding a column to the spreadsheet for each candidate cluster.
- Re-sort each reply into its best-fit cluster, watching for replies that don't slot cleanly (re-sort validation). Volume per cluster is itself signal — sparsely populated clusters are either smaller in the population or named more narrowly than the underlying pattern.
- Extract the discriminating traits of each validated cluster — the conditions whose values vary meaningfully across them.
The trait list from step 6 feeds the aspect toggle, where cluster names get pressure-tested for scope fidelity and discriminating traits get translated from folk vocabulary to state-vocabulary. Once formalized, the output feeds sorting hat design, differentiated-path investment decisions, or deeper user interviewing structured by the situation explorer.
Hand-sent, small-batch, transition-focused
Three disciplines govern execution:
- Hand-sent-feeling delivery. The email is the first touchpoint of the research relationship and sets the recipient's expectation about whether their voice will be heard. Mass-blast cues (unsubscribe links, template branding, third-person addressing) tell the recipient they're a cog in someone's report and depress reply quality accordingly.
- Scalpel, not sledgehammer. Small batches (~50 subjects per project), iterating on the prompt between batches. Mass outreach produces large corpora of low-quality replies; small batches with iteration produce smaller corpora of richer ones.
- Transition focus. Replies are read as accounts of what the user was trying to do in their life. The offering appears in the reply as one source of affordances among many being marshaled toward that pursuit.
Example
A team running multiplier analysis finds that signups who connect an integration in week 1 convert at 4x baseline. Before designing interventions, they want to know what those users were trying to accomplish. The founder sends a hand-sent email to 50 users from that multiplier cohort: "What were you hoping to use this for when you signed up?" Replies describe scenarios — replacing an existing tool that was breaking, setting up a workflow for a new team they'd just hired, evaluating against a competitor for a procurement decision. Spreadsheet aggregation surfaces three recurring patterns; re-sort validates them; the discriminating traits feed the aspect toggle, which produces named intent categories that downstream sorting hat work can route against.