MadKudu alternatives: 7 lead scoring options after the HG Insights acquisition
What MadKudu does today inside HG Insights, seven alternatives grouped by the reason you are looking, and a checklist for moving a scoring model without breaking routing.
MadKudu alternatives fall into five groups: scoring built into your CRM (HubSpot’s scoring tool, Salesforce Einstein Lead Scoring), dedicated scoring platforms (Breadcrumbs), product usage scoring tools (Common Room), account-level ABM and intent platforms (6sense, Demandbase), and transparent, person-built scoring inside HubSpot (BoostIdeal). The right one depends on why you are looking, so start with the reason.
MadKudu was acquired by HG Insights, in a deal announced on August 11, 2025. This guide sets out what the product does today, in its own documentation’s words, then maps each alternative to the need it serves. For roadmap, pricing, and support, ask the vendor.
What MadKudu does today
As of October 1, 2026, madkudu.com redirects to hginsights.com, and the help center at help.madkudu.com is branded “HG Insights (formerly MadKudu)”. The scoring product carries on under HG Insights product names. Here is what the vendor’s own pages describe.
| Area | What the documentation says |
|---|---|
| Customer fit | A customer fit model that uses firmographic, demographic, and technographic data to identify leads that are a good fit to buy. Segments are Very Good, Good, Medium, and Low |
| Behavioral scoring | A lead engagement model, also called Likelihood to Buy in the docs, built on in-app behaviors and marketing and sales interactions over the last 3 months, with time decay. Segments are Very High, High, Medium, and Low |
| Lead grade | A combined model that joins fit and behavior, graded A to E |
| How fit is trained | On Growth and Pro plans, against your existing paying customer database. On Startup plans, an industry-specific model based on aggregated trends |
| Reasons | Signals fields accompany the fit and engagement scores. HG Insights describes Data Studio scoring as transparent, so teams “know why prospects are qualified” |
| Data in | Sources include Salesforce, HubSpot, Marketo, Snowflake, BigQuery, Amazon S3, Amplitude, Segment, and Mixpanel |
| Scores out | Salesforce (lead, contact, account), HubSpot (contact), Intercom, and Segment |
| Salesforce package | A package that adds scoring fields and an embedded Sales Copilot view on lead, contact, and account records. Installs from December 2025 use HG_ field prefixes in place of MK_ |
| Agents and copilot | Sales Copilot puts predictive scores, account briefs, and playbooks inside CRM and sales tools. The acquisition release describes a “no-code and API-driven data studio for agent creation, predictive modeling, and analytics” |
Who it suits. On that description, it fits B2B teams that have enough paying customers to train a model, keep product usage data in a warehouse or product analytics tool, run sales out of Salesforce, and want a predictive score alongside a copilot for reps. That setup is common in product-led software.
MadKudu alternatives at a glance
| Alternative | Type | Best for | What it does not do |
|---|---|---|---|
| HubSpot scoring tool | Built into the CRM | HubSpot teams that want rules-based fit, engagement, and combined scores with no extra vendor | Scores contacts, companies, and deals only. AI-built scores need Marketing Hub Enterprise |
| Salesforce Einstein Lead Scoring | Built into the CRM | Salesforce teams with enough lead history for a predictive model | Needs at least 1,000 leads and 120 conversions for a custom model. Lives in Salesforce only |
| Breadcrumbs | Dedicated scoring platform | Teams that want a standalone scoring and routing engine across several tools | Not built into your CRM. You connect your data sources to it |
| Common Room | Product usage and signal scoring | Product-led teams that score people and accounts on product usage and other buying signals | Scoring is one part of a wider signal platform, not a CRM-native scoring tool |
| 6sense | ABM and intent | Teams whose real question is which accounts are in market | Not a lead-level scoring add-on. The scope is account intelligence, intent, and advertising |
| Demandbase | ABM and intent | Account-based programs that need buying groups, sales intelligence, and advertising together | Not a lead-level scoring add-on. It is a full account-based platform |
| BoostIdeal | Transparent, person-built scoring in HubSpot | HubSpot teams that want to build, read, and change their own model | No Salesforce or data warehouse integration. No predictive model. Does not run outreach, ads, or workflows |
Why are you looking? Match the reason to the type
Most searches for alternatives start with one specific friction. Name it first, because each one points to a different kind of tool.
| Your reason | What it means | Type that fits |
|---|---|---|
| “I want to see why a lead scored what it did” | Reps need the factors, not only the number | First check the signals fields you already have. Otherwise, rules-based or person-built scoring where every factor and weight is visible |
| “We are on HubSpot, not Salesforce” | You want scores and reasons on HubSpot records your workflows can read | HubSpot’s scoring tool, or a HubSpot-native engine such as BoostIdeal |
| “We do not have enough history for a predictive model” | Too few conversions to train on | Rules-based scoring you weight by hand, in your CRM or a HubSpot-native engine |
| “We want to own and change the model” | Weights should move when your strategy moves, on your schedule | Person-built scoring: CRM-native rules, Breadcrumbs, or BoostIdeal |
| “We need product usage signals” | Sign-ups and in-app activity drive your pipeline | A product usage scoring tool, or a platform that reads product analytics and warehouse data |
| “We sell to buying groups” | The account matters more than any one lead | An ABM and intent platform, or account scoring with contact roll-ups |
A note on the first row. MadKudu’s documentation lists signals fields next to its scores. If your reps cannot see reasons today, check whether those fields are on your page layouts before you change tools.
Group 1: scoring built into your CRM
HubSpot’s scoring tool builds fit, engagement, and combined scores from rules you set, with High, Medium, and Low bands and an A1 to C3 grid for combined scores. Event rules can decay every 1, 3, 6, or 12 months, and rules can read associated records by sum, average, minimum, or maximum. It is available on Marketing Hub or Sales Hub, Professional and Enterprise. AI-built scores need Marketing Hub Enterprise and at least 50 contacts, 25 that converted and 25 that did not.
Salesforce Einstein Lead Scoring is the predictive option inside Salesforce. Its setup considerations call for at least 1,000 leads and 120 conversions to build a model on your own data. If you are below that, check Salesforce’s documentation for what applies to your org.
Choose this group when one product sells through one motion and you would prefer not to add a vendor.
Group 2: other dedicated scoring platforms
Breadcrumbs describes itself as a “co-dynamic lead scoring and routing engine”. Its site lists models for acquisition, expansion, and retention, a Copilot for creating a model in a few clicks, and a feature called Reveal that uses machine learning to identify predictive attributes. It also states that sellers can see a breakdown of the fit and intent data behind a score. Listed integrations include HubSpot, Marketo, Salesforce, ActiveCampaign, Intercom, Segment, Pendo, and Mixpanel.
Choose this group when scoring needs to sit across several systems and you want a tool whose whole job is the model.
Group 3: product-led and product usage scoring
Common Room combines “hundreds of buying signals”, from first-party product usage to website engagement, into one buying intent score. Per its lead scoring page, it scores at the person level and aggregates to the account, lets you set weights with sliders, and shows the behaviors and attributes behind a score when you hover over it.
This category is consolidating. Pocus, for example, carries a notice on its own site that it is joining Apollo. So confirm that any vendor on your shortlist still sells the product under the name you remember. We have named only the one we could verify on its own site on October 1, 2026.
Choose this group when sign-ups and in-app behavior are your main source of pipeline.
Group 4: ABM and intent platforms, if the need is account-level
Forrester reports that 95% of B2B purchases involve three or more people. If that is your sale, a lead score may be the wrong unit.
6sense offers keyword-level intent, website deanonymization, and predicted buying stages. Its intent score runs from 1 to 100 with stages named Target, Awareness, Consideration, Decision, and Purchase, and it also sells advertising that targets on those signals.
Demandbase says it “turns signals into coordinated action across your entire GTM to prioritize the right accounts”, spanning account-based marketing, sales intelligence, advertising, and data, with buying groups as a core concept.
Choose this group when you need third-party intent and account-based advertising. Both cover far more than scoring, so check scope with each vendor.
Group 5: transparent, person-built scoring in HubSpot
BoostIdeal takes the opposite approach to a trained model. A person on your team builds the model and sets the weights, with AI helping to analyze your data and draft, and every score writes back to HubSpot with its reasons. It works with HubSpot, or a CSV you upload.
- Company size (200 to 1,000 employees) 30% 8.7
- Industry match 25% 7.4
- Role and seniority 25% 8.2
- Systems they run 15% 6.4
- Personal email domain 5% -3.6
A migration checklist for moving a scoring model between tools
A scoring model is wired into routing, alerts, lists, and reports. Move it in this order, whichever tool you choose.
- Export the factors and thresholds. Write down every factor, signal, weight, segment label, and cutoff the current model uses, plus any overrides. If the old model is predictive, record the signals it surfaces most often on your won deals.
- Inventory what reads the score. Search workflows, lists, reports, routing rules, and sequences for the old score fields. If you run the Salesforce package, check which field prefix your org uses.
- Check the data underneath. Validity found that 76% of CRM users say less than half their data is accurate and complete. A new model on the same gaps will rank the same records badly.
- Rebuild the model in the new tool. If you are rebuilding fit by hand, our five-step framework for building an ICP scoring model gives you the structure.
- Keep the old score running in parallel. Write the new score to its own properties. Nothing should act on it yet.
- Compare rankings on recent wins. Take the last two or three quarters of closed-won and closed-lost deals and see where each model placed them.
- Switch workflows last. Repoint routing, alerts, and lists one at a time, starting with the lowest-risk one.
- Retire the old fields only after a full sales cycle with no surprises.
Worked example: the parallel run
Demo data, for a team with 63 recent closed-won deals and 4,170 open leads.
| Check | Old score | New score | What to do |
|---|---|---|---|
| Won deals that sat in the top band before they closed | 39 of 63 | 43 of 63 | New model is at least as good. Keep going |
| Open leads in the top band | 617 | 384 | A smaller list sales can work. Confirm capacity with the sales lead |
| Won deals the model ranked in the bottom band | 6 | 9 | Read all 9. Find the shared factor and decide whether to add or re-weight it |
| Leads the two models place two or more bands apart | 237 | 237 | Sample 20 with a sales manager and note which model they side with |
The third row is the one that matters. Three more misses is a reason to adjust the model before anything acts on it.
Where BoostIdeal fits
BoostIdeal is a scoring engine for HubSpot teams that want to own the model. It works with HubSpot, or a CSV you upload.
- A person builds it. You choose the factors and set the weights as percentages that sum to 100. AI assists with analyzing your data and drafting. It is not a predictive model, and the weights change only when you change them.
- Reasons on every record. Scores are shown out of 10, and the score and its reasons write back to HubSpot properties, where your workflows, lists, and agents can act on them.
- Any record. Leads, contacts, accounts, and custom objects, including one record scored by data on another, such as a contact by its account’s fit.
- Several models side by side, so you can compare a new model with an old one before you switch.
- Current scores. Recalculation is hourly by default, and in near real time with webhook sync.
When it is not the right fit: your data is not in HubSpot and you do not want to work from CSV uploads, you need Salesforce or data warehouse integrations, you want a predictive model trained on your history, or you want a vendor to hand over a number you never question. For the thinking behind a model like this, start with our guide to ICP scoring.
Frequently asked questions
What are the best MadKudu alternatives and competitors?
It depends on the need. HubSpot’s scoring tool and Salesforce Einstein Lead Scoring cover scoring inside the CRM. Breadcrumbs is a dedicated scoring platform, Common Room scores product usage and other signals, 6sense and Demandbase cover account-level intent, and BoostIdeal offers person-built, transparent scoring in HubSpot.
Is MadKudu still available after the HG Insights acquisition?
HG Insights announced the acquisition on August 11, 2025. As of October 1, 2026, madkudu.com redirects to hginsights.com, and the help center describes the product as HG Insights (formerly MadKudu), with scoring offered through Data Studio and Sales Copilot. For current packaging and terms, check with HG Insights.
Does MadKudu work with HubSpot?
Its documentation lists HubSpot as a data source and as a destination for scores on the contact object. Salesforce is listed as a destination for leads, contacts, and accounts, with a dedicated package. Confirm the current details with HG Insights before you plan around them.
What is the difference between MadKudu and HubSpot lead scoring?
MadKudu’s customer fit model is trained against your paying customers and returns segments from Very Good to Low. HubSpot’s scoring tool uses rules and points that you set, with High, Medium, and Low bands and an A1 to C3 grid. HubSpot also offers AI-built scores on Marketing Hub Enterprise, which you can review and edit.
What if we do not have enough data for predictive lead scoring?
Use a rules-based model that a person weights by hand. Salesforce Einstein Lead Scoring calls for at least 1,000 leads and 120 conversions for a custom model, and HubSpot’s AI scores need 50 contacts with 25 converted. Below those levels, your team’s judgment about won deals is the better input.
How do I migrate a lead scoring model to a new tool?
Export the factors, weights, and thresholds, list every workflow and report that reads the old score, and rebuild the model in the new tool on separate properties. Run both in parallel, compare how each ranked your recent wins and losses, then switch workflows one at a time. Retire the old fields last.
Is MadKudu a black box?
Not by its own description. Its documentation lists signals fields alongside the fit and engagement scores, and HG Insights describes Data Studio scoring as transparent. The difference from person-built scoring is who sets the logic: a trained model, or someone on your team.
The takeaway
Start with the reason you are looking, then pick the group that answers it. If MadKudu’s predictive models and Salesforce package still match how you sell, staying put is a reasonable answer. Whichever way you go, export the old model, run both scores in parallel, check them against recent wins, and switch your workflows last.