Inquiry Lead Scoring: Find Which Requests Convert in Your Own Data

BOTrento Team23 October 20268 min read

Why gut feeling about "good" enquiries is often wrong

Most sales teams have a sense of which enquiries are worth their time: the big ones, the ones from known brands, the ones with a budget. Sometimes that's right. But the loudest memories are the deals you won or lost painfully, not the quiet pattern behind dozens of ordinary enquiries.

Looking at your own history answers questions a hunch can't. Do enquiries from your website form close differently from referrals? Do requests with a date and venue turn into orders more often than "can you send prices?" Does a particular industry go quiet after the quote? Your answers will differ from another company's, which is why generic scoring models are of limited use.

Step 1: Build a clean enquiry table

Pull your history from wherever it lives: your inbox, Pipedrive or HubSpot, a quote spreadsheet, or all three. Aim for one row per enquiry, not per email. Use these columns:

• Date received
• Source: website form, direct email, phone, referral, repeat customer, tender portal, other
• Customer type or industry
• Request type: price-only, full RFQ, availability check, repeat order, vague idea
• Completeness: did it include date, quantity or scope, location and a contact person?
• Quote sent? yes or no
• Outcome: won, lost, no reply, declined by you, still open

Be strict with the outcome column. "No reply" and "lost" are different things, and mixing them hides where you actually lose deals. If you aren't sure how to define conversion, this guide to calculating quote conversion rate covers the choices you need to make.

Step 2: Tag the enquiries you can't tag from the data

Source and outcome are usually easy. Request type and completeness often aren't recorded anywhere, so you'll need to read the first email and tag it by hand. Do this for a sample if the volume is large, for example every enquiry from the last three months plus a spread across the rest.

Keep the tag list short. Five request types are plenty. If you find yourself inventing a new label every few rows, you're describing individual cases, not patterns. Write down your rules for each tag so a colleague would tag the same way.

Step 3: Compare outcomes by one dimension at a time

Build a simple pivot table. For each source, request type and customer type, count how many enquiries you received, how many got a quote and how many were won. Look at two ratios separately:

1. Enquiry to quote: how many you were able or willing to quote.
2. Quote to order: how many quotes were confirmed.

Splitting them matters. A source might convert well at the quote stage but produce so many unquotable enquiries that it eats your time. Another might be rare but almost always closes. The cost of unclear requests is worth measuring too; counting the hours lost to clarification loops shows how.

Watch the small numbers

If a category has only a handful of enquiries, don't draw conclusions from it. Group it into "other" or wait for more data. A pattern from three enquiries is an anecdote.

Check speed as a hidden factor

Compare how quickly you replied to won and lost enquiries. If the fast replies win more, some of your "source" effect may really be a response-time effect. Measuring enquiry response time is a good companion analysis.

Step 4: Turn patterns into a simple scoring sheet

Pick the three to five factors that showed the clearest differences and give each a small score. Keep it to points you can add up in your head.

These weights are only an illustration. Yours should reflect what your data shows. Then set three bands: high (reply first, today), medium (reply today with a clarifying question), low (send a short standard reply and ask for the missing details).

Never use the score to ignore an enquiry. It only decides order and effort. A low score still gets an answer.

Step 5: Test it on past enquiries, then review quarterly

Before you rely on the sheet, score 30 or so past enquiries blind, without looking at the outcome. Then check whether high scores really did win more often. If not, adjust the weights or drop a factor.

Once it's live, record the score on each new enquiry and revisit it every quarter. Markets, channels and your own offer change. A source that used to bring in good work may not any more. A short weekly look at open quotes helps keep this honest; a 30-minute pipeline review agenda fits well alongside it.

Where the scoring breaks down in practice

• Data is scattered. Half the enquiries are in a shared inbox and half in a CRM. Fix the table first, not the model.
• Nobody applies the score. If it takes extra clicks, it gets skipped. Put it where people already look each morning.
• Slow replies pollute results. If good enquiries wait three days, the data will say they convert badly.

For rental firms, event agencies and B2B service teams, the third problem is the most common. Enquiries arrive in bursts, and the ones needing the most thought wait longest.

How BOTrento supports this

BOTrento Chief of Staff is a managed process for enquiries, quotes and follow-ups. It doesn't replace your CRM or accounting, and it doesn't send anything on its own. What it does is watch enquiries and quotes in your mailbox and in the data you upload, then show a "Today 1+3" overview each working day, in the app and by email. It also prepares draft replies, quotes and follow-ups in four languages and picks up promises like "I'll get back to you by Friday". A person always approves a draft before anything is sent.

For this topic, that means you can bring your enquiry history as a CSV or XLSX export, or connect a mailbox via IMAP, and have a place where new enquiries and open quotes are tracked consistently. You then apply your own scoring rules to decide what to handle first. At the start of the trial, a "Lost money" report shows what has slipped through (looking back only for a mailbox connected via IMAP).

The 14-day trial is free, needs no card and no API key of your own. It starts when you first upload data, and you have 7 days to prepare. Someone from our team contacts you within 24 hours on working days to help connect your mailbox. See the process demo or the Chief of Staff overview for details, and the pricing page if you're comparing plans.