How to Use BigMarker to Score Webinar Leads for Sales

The short answer: if your BigMarker channel has the enterprise licence, its AI Lead Scoring gives every registrant a score out of 100 from factors you write in plain language, and the score lands in a hidden field that exports to HubSpot (lead scoring article), so sales can start at the top of the list instead of the alphabet. The steps: build the registration form with consent fields, connect HubSpot, write three or four weighted scoring factors, add an AI note for sales, read the explanations for the top leads yourself, then export to a static list.

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The job: a webinar ends with 400 registrants, and the sales team can follow up properly with 40 of them this week. Somebody decides which 40. This guide hands that first sort to BigMarker AI and keeps the decision with a person. For what BigMarker is and how it is sold, see the BigMarker glossary entry.

Before you start:

• The licence. Lead Scoring and AI Fields are only available with an enterprise license, per BigMarker's AI fields article. HubSpot is listed among the enterprise integrations in the CRM integrations article.
• Static lists in HubSpot. BigMarker exports to static lists only, not active ones (HubSpot article). Create one for the scored export before the session.
• Last quarter's conversion figure. For example, if 6 of the last 120 webinar leads sales called became meetings, your baseline is 6 / 120 = 5%. The scoring is worth keeping only if the top of its list beats that.
• Your consent wording. Scoring registrants for sales follow-up is profiling for direct marketing, and Article 21 of the GDPR gives people the right to object to it, a right you must bring to their attention by your first communication at the latest (GDPR, EUR-Lex).

Step 1: Choose the session type with reuse in mind

Create the webinar as live, automated or evergreen deliberately. BigMarker does not let you change the type after creation, apart from a recorded live session turning into on-demand (types of webinars). A live session that will later run as on-demand needs no second setup.

Step 2: Build the registration form for the scoring

In the session admin, go to Basic Setup > Registration Fields. Fields fall into three groups: displayed, consent (where the GDPR fields go) and hidden (registration fields article). Ask for what the scoring will read, such as job title, company size as a dropdown and the problem they came for. Add an anti-spam field too: bots fill it in, people never see it, so a filled value marks a registrant you can drop before scoring.

Step 3: Connect HubSpot and map the fields

Open Integrations in the session admin, click HubSpot, then Install HubSpot App and authenticate. In Export to Contacts and Lists, choose the static list for each group (registrants, attendees, no-shows, recording viewers) and map the fields. If you run more than one poll, enter a separator character, or every poll's answers land in one HubSpot field (HubSpot article).

Step 4: Write the scoring factors

Go to Audience > AI Insights and click Configure. Each scoring factor has a name, a weight and a description, and the description is the prompt; the name does not affect the score. Weights are relative, so a factor weighted 10 counts twice as much as one weighted 5 (lead scoring article). Factors can read registration fields, attendee activity such as chat and Q&A, and publicly available company data.

Keep it to three or four factors you can defend to a sales manager: fit (role and company size against your customer profile), intent (questions asked, CTA clicks, time watched) and a spam check on the email address. Use Generate example if you want a starting prompt, then rewrite it in your own terms.

Step 5: Let the AI draft a note for each lead

In the same AI Insights area, add an AI Field in the enrichment category. BigMarker's own example is a "Note to sales" prompt that summarises how a registrant engaged, quoting their chat and Q&A phrases (AI fields article). The note becomes a hidden registration field, so it travels to HubSpot with the score.

This is the agent step: BigMarker AI reads the registration data and the session behaviour, decides a score per factor and writes a note.

Step 6: Check the scores before sales sees them

Open AI Insights after the session and click into the top 20 registrants. In the contact drawer, click each scoring factor to read why it gave that score. You are checking three things: that the reason quotes real behaviour, that no factor rewards something irrelevant (a long watch time from someone who left the tab open), and that the note says nothing you would not send to the prospect. Fix the prompt, not the score; the model reruns across everyone.

Step 7: Export, and keep a holdout

Map the AI Lead Score and the note in the HubSpot export, alongside the other fields (lead scoring article). Then hold back a comparison group. For example, sales calls the top 40 by score plus 10 picked at random from the rest, and you compare meeting rates after 30 days: 40 + 10 = 50 calls, the same workload with an answer attached.

What BigMarker's articles flag

• Renaming system fields. The registration article warns not to rename, delete or edit BigMarker-created fields such as "Responded to survey".
• Active lists. Exports go to static lists only; a list with filters or workflows will not receive them.
• New HubSpot forms. Forms built in HubSpot's updated editor need a hidden field named 'BigMarker Webhook Webinar Id Trigger' with the webinar ID before imports work (HubSpot article).
• The wrong session type. It cannot be changed later.

How to know the scoring earned its place

Lead Scoring is fenced into the enterprise licence, so the upgrade is BigMarker's win on the day you sign, whether or not the scores predict a single meeting for you. Your test is the holdout from Step 7. If the top 40 by score book meetings at a rate above the 5% baseline from the example and above the random 10, keep the model. If not, rewrite the factors before the next session.

A score is a hypothesis about who will buy. The holdout is the experiment.

FAQ

Does BigMarker send registrant data to an outside AI provider?

BigMarker says its AI runs on a proprietary internal model and that user data, session content and configurations stay within BigMarker (lead scoring article). Put that sentence in front of your data protection officer and ask for it in the contract.

Can the score use behaviour from earlier webinars?

Yes. The scoring setup has a setting to use historical data from your channel when calculating scores (lead scoring article).

Can I use the AI note in follow-up emails?

Yes. AI Fields work as email tokens and on landing pages as well as in exports, and BigMarker's examples include a personalised follow-up email body (AI fields article). Read a sample before the send goes out to everyone.