{ "claims": [ { "claim_id": 43, "claim_text": "Roughly 9-10% of contacts (about 18,000 of 197,000) were duplicates, undermining reporting reliability before any scoring work could be trusted.", "taxonomy": "numerical", "sub_taxonomy": "stat", "source_quote": null, "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 2.0 }, { "claim_id": 47, "claim_text": "Approximately 5,900 deals were audited for attribution consistency as part of the scoring/attribution rebuild.", "taxonomy": "numerical", "sub_taxonomy": "stat", "source_quote": null, "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 2.0 }, { "claim_id": 105, "claim_text": "Only about 3% of leads should ever turn into deals; treating every lead as a deal corrupts pipeline metrics.", "taxonomy": "numerical", "sub_taxonomy": "benchmark", "source_quote": "Most people take every lead and make it a deal. No, it's only like 3% of leads turn into deals. So, typically your deal metrics are totally off and your pipeline metrics are totally off because everyone's throwing everything in there for no reason.", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 2.0 }, { "claim_id": 110, "claim_text": "Properly fixed scoring typically improves lead-to-opportunity conversion by 40\u201360%.", "taxonomy": "numerical", "sub_taxonomy": "benchmark", "source_quote": "The lead to opportunity conversion rates typically improve 40 to 60% because you're really focused on the right leads now, not the wrong leads.", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 2.0 }, { "claim_id": 210, "claim_text": "Example baseline: an email click is worth 4 points, capped at 20 total within the engagement-score group.", "taxonomy": "numerical", "sub_taxonomy": "benchmark", "source_quote": "an email click is worth four points and that's up to a total of 20.", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 2.0 }, { "claim_id": 214, "claim_text": "Industry research cited: win rates have declined 18% while sales cycles extended 16%, making efficient lead prioritization increasingly critical.", "taxonomy": "numerical", "sub_taxonomy": "stat", "source_quote": null, "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 2.0 }, { "claim_id": 236, "claim_text": "Forrester research finds that B2B marketers who emphasize lead volume over lead quality reduce sales efficiency and widen the marketing-sales gap.", "taxonomy": "numerical", "sub_taxonomy": "stat", "source_quote": "B2B marketers who emphasize lead volume over lead quality reduce sales efficiency", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 2.0 }, { "claim_id": 253, "claim_text": "Businesses that migrated to the new lead-scoring solution have seen a 22% improvement in MQL-to-SQL conversion rates.", "taxonomy": "numerical", "sub_taxonomy": "stat", "source_quote": "businesses who migrated to the new lead scoring solution have seen a 22% improvement in MQL to SQL conversion rates.", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 2.0 }, { "claim_id": 254, "claim_text": "The 2026 lead-response benchmark is under 2 minutes, down from a 5-minute 2024 benchmark, because buyers are getting answers from AI chatbots and competitors before SDRs review last week's leads.", "taxonomy": "numerical", "sub_taxonomy": "benchmark", "source_quote": "In 2024, the benchmark for lead response was 5 minutes. Now in 2026, it's under 2 minutes. This is because buyers are engaging with AI chat bots.", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 2.0 }, { "claim_id": 255, "claim_text": "Per Gartner and 6sense research, 70-80% of the buying journey happens before a buyer ever talks to your business; by form-fill they've already shortlisted vendors.", "taxonomy": "numerical", "sub_taxonomy": "stat", "source_quote": "Research from Gartner and Six Sense shows that 70 to 80% of the buying journey happens before you ever talk with a business. And by the time they fill in a form, they've already shortlisted vendors.", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 2.0 }, { "claim_id": 260, "claim_text": "Implementing the 2x2 routing framework consistently cuts sales-team wasted time by 30-40% across dozens of client portals.", "taxonomy": "numerical", "sub_taxonomy": "benchmark", "source_quote": "we've implemented this exact framework across dozens of client portals and it consistently cuts your sales team wasting time by 30 to 40%.", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 2.0 }, { "claim_id": 268, "claim_text": "2026 standard: enable native score decay so engagement scores degrade by 30% every 30 days of inactivity (typical range: 20-50% / 30 days).", "taxonomy": "numerical", "sub_taxonomy": "benchmark", "source_quote": "the 2026 standard here is DK engagement scores drops by 20 to 50% after 30 days of inactivity. Enable native score DK. Set it to degrade by 30% every 30 days", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 2.0 }, { "claim_id": 286, "claim_text": "Done right, lead scoring should surface the top 20-30% of leads as self-qualified and let sales know exactly when to engage.", "taxonomy": "numerical", "sub_taxonomy": "benchmark", "source_quote": "20\u201330% of leads self-qualify and sales knows exactly when to engage.", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 2.0 }, { "claim_id": 303, "claim_text": "Score decay typically reduces points after 90 days of lead inactivity.", "taxonomy": "numerical", "sub_taxonomy": "benchmark", "source_quote": null, "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 2.0 }, { "claim_id": 368, "claim_text": "Total Lead Score ceilings are 100 points on Marketing Hub Professional and 500 points on Enterprise.", "taxonomy": "numerical", "sub_taxonomy": "benchmark", "source_quote": null, "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 2.0 }, { "claim_id": 384, "claim_text": "Suggested starting threshold ranges (out of 100 total, 50/50 split): Fit A=38-50, B=24-37, C=0-23; Engagement 1=35-50, 2=18-34, 3=0-17.", "taxonomy": "numerical", "sub_taxonomy": "benchmark", "source_quote": null, "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 2.0 }, { "claim_id": 388, "claim_text": "Recommended engagement point values: Form submission (Demo/Pricing) +30-40; Pricing page view +15 time-boxed; CTA click (high-intent) +10 capped at 3; Marketing email click +5 frequency-based; Meeting booked +35-50; Webinar attended +15 (registered only +7).", "taxonomy": "numerical", "sub_taxonomy": "benchmark", "source_quote": null, "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 2.0 }, { "claim_id": 12, "claim_text": "Final total scores can now go negative if you use a lot of subtraction points \u2014 this was a recent (2026) update.", "taxonomy": "dated", "sub_taxonomy": "product_change", "source_quote": "we recently came out with an update where we allow final total scores to go negative if you use a lot of subtraction points.", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 1.5 }, { "claim_id": 15, "claim_text": "Custom events as a scoring criterion was recently brought down from Enterprise to Professional tier \u2014 Pro users can now use custom events.", "taxonomy": "dated", "sub_taxonomy": "product_change", "source_quote": "Those recently just came down from enterprise to professional. So all you professional users out there, time to figure out how to build and use custom events.", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 1.5 }, { "claim_id": 16, "claim_text": "Segment membership as a scoring criterion just launched \u2014 lets you award points if a contact belongs to a complex AND/OR segment.", "taxonomy": "dated", "sub_taxonomy": "product_change", "source_quote": "we just came out with segment membership. So that's if if you created a segment with really complex andor statements and then you want to say if they belong to this segment give them 30 points only if they belong to this particular segment.", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 1.5 }, { "claim_id": 17, "claim_text": "Native conditional scoring (cross-property AND criteria within one rule, e.g., this job title AND this buying role AND this industry) just launched \u2014 previously required workflow-enrollment or segment-membership workarounds.", "taxonomy": "dated", "sub_taxonomy": "product_change", "source_quote": "conditional scoring. That is a new one. ... if you needed to do like cross property criteria, like they need to have this job title and this buying role and this industry before I give them points, you're able to do this natively within the lead scoring tool.", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 1.5 }, { "claim_id": 19, "claim_text": "Coming this month: from the live score 2x2 distribution view you'll be able to click any cell to see the contacts in it and 'use in segments / workflows / views' will auto-populate the filters.", "taxonomy": "dated", "sub_taxonomy": "product_change", "source_quote": "something that's coming very soon literally this month is once you turn on a score and this 2x two matrix ... you're actually able to click this and you're going to be able to see like who are these 14 contacts that are here. ... when you click on use in it'll automatically be like okay this it already populates the filters of this score this threshold here's your segment.", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 1.5 }, { "claim_id": 30, "claim_text": "Score history card shows which specific rule changed the score; roadmap improvement: only show the one specific triggering rule (currently shows whole group), add timestamps, hyperlink the asset (which email triggered it).", "taxonomy": "dated", "sub_taxonomy": "product_change", "source_quote": "we are working on only showing that one specific rule removing that extraneous information and also adding a timestamp so that you can just look at the information here rather than the activities timeline as well as we're working on can we hyperlink the asset. So like which email did they click on for this rule to change.", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 1.5 }, { "claim_id": 38, "claim_text": "Recently shipped UX wins: faster combined-distribution preview, conditional scoring (cross-property AND), segment-membership criterion, more Loom-based product-update walkthroughs, and ongoing performance improvements to the editor.", "taxonomy": "dated", "sub_taxonomy": "product_change", "source_quote": "the faster and combined distribution preview conditional scoring. That is a new one. ... segment membership. I mentioned this one, which just came out. ... we've done a lot of improvements just in the past week of trying to speed up that performance.", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 1.5 }, { "claim_id": 157, "claim_text": "At the time of recording (Dec 2024), the new lead-scoring tool is in beta \u2014 orgs may need to opt into the beta to see it under Marketing \u2192 Lead scoring.", "taxonomy": "dated", "sub_taxonomy": "platform_state", "source_quote": "this tool is currently in beta at the time I'm filming this video... if you do not see this here that means that it is still in beta you might have to go and opt into that beta um to have access to this tool", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 1.5 }, { "claim_id": 202, "claim_text": "The previous lead-scoring lived inside contact properties, but the new tool is its own standalone module accessed under Marketing > Lead Scoring.", "taxonomy": "dated", "sub_taxonomy": "product_change", "source_quote": "the previous lead scoring was part of properties but now it's its own standalone module here.", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 1.5 }, { "claim_id": 221, "claim_text": "Previously lead scoring was managed as a property in settings; now it has its own channel on the side menu under Marketing > Lead Scoring.", "taxonomy": "dated", "sub_taxonomy": "product_change", "source_quote": "previously the lead scoring was a property that you would manage over in the settings, but now it's got its own channel, really, on the side menu.", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 1.5 }, { "claim_id": 252, "claim_text": "On August 31, 2025 HubSpot threw out the legacy HubSpot Score property \u2014 it was a hard stop, not a soft transition.", "taxonomy": "dated", "sub_taxonomy": "product_change", "source_quote": "On August 31st, 2025, HubSpot threw out their legacy HubSpot score property. This wasn't a soft transition. It was a hard stop.", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 1.5 }, { "claim_id": 315, "claim_text": "HubSpot is deprecating the legacy behavior-score and fit-score properties; everyone is moving to the new module which natively supports per-group caps and decay (RevPartners' previously-custom mitigations are now built-in).", "taxonomy": "dated", "sub_taxonomy": "product_change", "source_quote": "HubSpot's kind of adopted that mentality and they're actually they're actually deprecating these properties. So, behavior score and fit score, these properties are no longer going to be available in HubSpot very soon. And they're turning everybody to this new module.", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 1.5 }, { "claim_id": 329, "claim_text": "The Sept-2024 updated lead-scoring tool sits as a public beta available for Marketing Pro and Enterprise users.", "taxonomy": "dated", "sub_taxonomy": "product_change", "source_quote": "this update currently sits as a public beta available for marketing Pro and Enterprise users", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 1.5 }, { "claim_id": 341, "claim_text": "HubSpot's old legacy property-based scoring system stopped updating after August 31, 2025; users on pro/enterprise should migrate, and lower-tier users should explore alternative solutions before legacy stops working.", "taxonomy": "dated", "sub_taxonomy": "product_change", "source_quote": "HubSpot announced that the old legacy property scoring system will stop updating after August 31st, 2025. So, if you haven't migrated yet and you're on a pro or enterprise plan, make sure to do that soon.", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 1.5 }, { "claim_id": 359, "claim_text": "HubSpot Insights has been phased out (privacy concerns); previously it auto-populated company revenue, role, etc. from email/name lookups \u2014 practitioners now must manually enrich contacts via LinkedIn/conversations.", "taxonomy": "dated", "sub_taxonomy": "product_change", "source_quote": "they are no longer using HubSpot Insights as much anymore to prepopulate a lot of this information... with HubSpot um insights being phased out due to privacy concerns and whatnot, you're actually going to have to put in a lot about the contact and the company yourself now.", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 1.5 }, { "claim_id": 365, "claim_text": "HubSpot has phased out the legacy Score property in favor of the new Lead Scoring tool, which supports Contacts, Companies, and Deals as scorable objects.", "taxonomy": "dated", "sub_taxonomy": "product_change", "source_quote": null, "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 1.5 }, { "claim_id": 5, "claim_text": "Combined score = numerical sum of fit + engagement; creating a combined score auto-generates four properties: fit score, engagement score, total score, and threshold.", "taxonomy": "feature", "sub_taxonomy": "api_contract", "source_quote": "the combined score actually allows you to um combine the two uh is just a like a numerical sum but then you actually when you create a combined score it gives you the fit the separate fit and engagement score as well. ... when you create a combined score, you get a fit score property, engagement score property, the total score property. So that's the combined score and as well as the threshold property.", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 1.0 }, { "claim_id": 6, "claim_text": "The 'create score with AI' feature requires at least ~25 conversion data points (e.g., 25 conversions in the chosen start-to-end stage window) to analyze and produce a score.", "taxonomy": "feature", "sub_taxonomy": "constraint", "source_quote": "I believe you need to have at least 25 like data points. So like say like 25 conversions um for this to work so that they could actually analyze and see a pattern.", "source_practitioner": null, "video_url": null, "credibility": 0.5, "priority_score": 1.0 } ], "total_verifiable": 153, "selected": 35 }