Inquiry Data Analysis: How to Spot Pricing Patterns in Your Own Enquiries

BOTrento Team28 October 20267 min read

What your enquiry history can tell you about pricing

Most small B2B teams treat enquiries as a queue to clear. Read in bulk, they become market research you didn't have to commission. Four things tend to show up:

• Price sensitivity: which request types or sources get "too expensive" or go silent right after the quote.
• Repeated objections: the same few reasons, worded differently by different people.
• Seasonal peaks: months when enquiries rise, and whether your price or lead time changes the reaction.
• Positioning gaps: things customers keep asking for that you don't list, or comparisons they make with other suppliers.

None of this requires a data team. It requires consistent tags and an honest look at the outcome of each case.

Step by step: analyse your own inquiry data

1. Collect a clean sample

Pull the last six to twelve months from your mailbox, Pipedrive, HubSpot or a spreadsheet. One row per enquiry. Include date, sender company, the original request text, whether you quoted, the quote value and the outcome (won, lost, no reply).

2. Add five tags

1. Request type (for example: standard hire, custom production, recurring service).
2. Source (website form, referral, repeat customer, directory).
3. Month of the enquiry.
4. Outcome (won, lost, silent, still open).
5. Price reaction (none, asked for discount, compared to a competitor, said too expensive, asked for a cheaper option).

3. Let AI do the first pass, then check it

Free-text fields such as request type and price reaction are the slow part. An AI model can propose a tag for each email; you review a sample and correct any that are wrong. Keep your tag list short, otherwise the patterns blur.

4. Pivot and compare

Count by tag combination: price reaction by request type, outcome by source, enquiries by month. You are looking for differences big enough to notice without a calculator. If you are unsure, collect more rows before drawing a conclusion.

5. Write down three decisions

For example: add a lower-spec option to one request type, state a minimum order in your first reply, or open the busy month with a firmer price. If an observation doesn't lead to a decision, drop it.

How to read price sensitivity without fooling yourself

"Too expensive" is often a polite way to say several things. Separate them before you change a price:

• Scope mismatch: the customer wanted something smaller. A cheaper option fixes this, not a lower price.
• Missing value: your quote lists items but not what they achieve. A rewrite fixes this.
• Real budget limit: a genuine ceiling. Here you either qualify earlier or accept the loss.
• Silence: no reply is not a price signal on its own. It can equally be slow follow-up.

Two cautions. Small samples mislead, so treat a pattern from a handful of cases as a hypothesis. And lost deals are over-represented in objections, because happy customers rarely explain themselves. For the arithmetic of outcomes, see how to calculate quote conversion rate properly, and for a sorting approach by source and type, inquiry lead scoring from your own data.

Objections, seasons and gaps: what to look for

Common objections

Group them into themes and keep the customers' own words. A phrase such as "we found it cheaper elsewhere" tells you to ask what they compared. "Can you do it without the extras?" points to a missing entry-level option.

Seasonal peaks

Plot enquiries by month, then check the outcome by month. Event and production firms or equipment rental teams may find that the busiest weeks bring the fastest decisions and the least haggling, while quiet months bring more comparison shopping. Check whether that is true for you before you act on it.

Positioning gaps

Look for requests you decline or answer with "not really our thing". If the same request appears repeatedly, either it is a product opportunity or a sign that your website attracts the wrong audience. Also note where incomplete enquiries slow you down; the cost of incomplete enquiries shows how to count that.

A simple template for your findings

Keep the output to one page you can review monthly. A short structure:

Period: January–December, [your number] enquiries reviewed
Pattern 1: Custom requests from directories get "too expensive" more often than referrals.
Likely cause: scope mismatch, not budget.
Decision: add a standard package to the first reply.
Check again: end of next quarter.

Review it alongside your weekly numbers. Six weekly sales KPIs give you the baseline to see whether a pricing change did anything.

How BOTrento helps you do this on real data

Doing this once in a spreadsheet is easy. Keeping it up is where it usually stops. BOTrento Chief of Staff is a managed process for enquiries and quotes: it watches your mailbox and uploaded data, shows a "Today 1+3" overview every working day, prepares draft replies, quotes and follow-ups, and catches promises such as "I'll get back to you by Friday".

You can import CRM or spreadsheet data as CSV/XLSX, and a Seznam or other mailbox can be connected via IMAP. Gmail, Google Workspace and Microsoft 365 work for now by forwarding or import. At the start of the trial you also get a "Lost money" report (retroactive only for a mailbox connected via IMAP).

BOTrento doesn't replace your CRM or accounting, doesn't read attachments (it works from the email text) and sends nothing by itself: every draft is approved by a person. AI is included in the plan with a monthly usage limit, with a warning at 80%. You can see how the flow looks in the process demo, and the pricing page explains the plans. On privacy, data is stored on servers in Germany (EU), and the text needed for a task is processed by the model provider. For a checklist, see whether an AI sales tool is GDPR-compliant; for specific cases ask your lawyer.

No result is guaranteed, which is why you can try it free for 14 days on your own data first, with no card and no API key. The 14 days start with your first successful data upload.

Turn insights into action

Patterns only pay off if they change what you do on the next enquiry. Pick one, for example a faster first reply or a firmer follow-up, and test it for a month. If follow-ups are the weak point, read how to stop forgetting quote follow-ups. If speed is, see how to measure enquiry response time.

Firms that quote manually and often, such as events and production, equipment rental or B2B services, tend to see the payoff from this review first.