MadKudu: What It Actually Does, What It Costs, and Who It Fits
A predictive lead scoring platform that trains machine learning models on a company's own historical conversion data, rather than a generic firmographic rule set, to identify which product-led-growth users and accounts are actually likely to convert or expand.
MadKudu is built for product-led-growth SaaS companies that already have enough historical conversion data (free-trial signups, product usage events, closed-won deals) to train a real predictive model, rather than guess at a scoring rubric by committee. It trains machine learning models on that historical data to score free users, trial accounts, and leads by their actual likelihood to convert or expand, then surfaces Product-Qualified Leads (PQLs) so sales knows who to contact and when. It does not publish a full self-serve rate card; third-party reporting commonly places entry plans starting near $999/month for a small sales team, scaling into custom five-figure annual contracts for larger lead volumes. As of August 2025, MadKudu was acquired by HG Insights and folded into its Revenue Growth Agentic Ecosystem, so buyers evaluating it in 2026 are evaluating a platform now sold and supported as part of a larger data and agentic-AI portfolio, not a fully independent standalone vendor. It is not the right fit for an early-stage company without enough historical conversion volume to train a meaningful model, or a team that wants transparent published pricing before a sales conversation.
What MadKudu Is For
MadKudu is a predictive lead scoring platform built around training machine learning models on a company's own historical conversion data, meaning actual past free-trial signups, product usage events, and closed-won or expansion deals, rather than applying a generic firmographic rule set written once and left unchanged. The model learns which combinations of behavior, product usage, and firmographic fit have actually correlated with real conversions in that specific business, then scores every new lead or account against that pattern and surfaces the resulting score with an explanation of which signals drove it, rather than a black-box number.
It's built primarily for product-led-growth (PLG) SaaS companies that have a self-serve or freemium funnel generating enough volume of free users and trial accounts that a rep can't manually review each one, and that already have enough historical conversion history to train a model worth trusting. As of August 2025, MadKudu was acquired by HG Insights and integrated into its Revenue Growth Agentic Ecosystem, so it is now sold and supported as part of a broader data and agentic-AI portfolio rather than as a fully independent standalone company.
Key Features
- Machine learning models trained on a company’s own historical conversion data (free-trial signups, product usage events, closed-won and expansion deals) rather than a static firmographic rule set
- Product-Qualified Lead (PQL) identification that combines product usage signals with firmographic and behavioral data to flag which free or trial users are actually likely to convert
- Explainable scoring that shows which signals (behavior, firmographic fit, product usage) drove a given lead or account score, instead of a single opaque number
- CRM, marketing automation, and data warehouse integrations that surface scores and PQL status inside existing sales and marketing workflows
- Model recalibration as new conversion data comes in, so the scoring criteria can shift as a product or ideal customer profile evolves
- As of 2026, delivered as part of HG Insights’ Revenue Growth Agentic Ecosystem following the August 2025 acquisition, adding access to HG Insights’ broader market and sales intelligence data
Pros & Cons
Pros
- ✔ Scores are trained on a company’s own real historical conversion data rather than a generic rule set, so they reflect what has actually predicted conversions in that specific business
- ✔ Explainable scoring shows which signals drove a given score, which is easier for a sales or RevOps team to trust and act on than a black-box model
- ✔ Purpose-built for product-led-growth motions, combining product usage data with firmographic and behavioral signals to identify PQLs specifically, not just generic marketing-qualified leads
Cons
- ✕ No fully published self-serve rate card; most real deployments beyond the smallest plans require a custom quote
- ✕ Needs a meaningful volume of historical conversion data to train a useful model, so an early-stage company without much closed-deal or usage history won’t get as much value yet
- ✕ Now operates as part of HG Insights’ Revenue Growth Agentic Ecosystem following the August 2025 acquisition, which is worth factoring into a 2026 buying decision as roadmap and support may shift under the larger portfolio
Pricing
| Tier | Price (approx) | What you get |
|---|---|---|
| Starter | Commonly reported near $999/month (about $12,000/year) for up to 2 sales reps | Configured predictive lead scoring and standard lead enrichment for a small team starting to route PQLs |
| Growth | Commonly reported near $2,499/month (about $24,000/year) for up to 10 sales reps | Customized scoring models, deeper enrichment, and PQL routing across a larger sales team |
| Enterprise | Custom quote, commonly reported in the $40,000-$100,000+/year range depending on lead and account volume | Full model customization, expanded integrations, and enterprise-scale lead volume, delivered as part of the HG Insights Revenue Growth Agentic Ecosystem |
MadKudu does not publish a complete self-serve rate card. The figures above are third-party-reported starting points for its Starter, Growth, and Enterprise tiers, not an official price list, and actual cost depends heavily on monthly lead volume, seat count, and which enrichment or integration add-ons are included, per third-party buying guides current as of 2026.
MadKudu sells its higher tiers through a custom-quoted, sales-led process, and full pricing detail is not published on its website. The figures above are third-party-reported approximations current as of 2026, not an official rate card, and actual cost depends on lead volume, rep seats, and enrichment add-ons. Get a quote scoped to your team's actual conversion volume directly from MadKudu before budgeting.
Guides for MadKudu
Common questions
A manual rubric or firmographic filter is a fixed rule someone wrote once, for example company size over 100 employees plus a job title containing VP equals a high score, and that same rule keeps applying whether or not it still actually correlates with real conversions. MadKudu instead trains a machine learning model on a company's own historical conversion data, so the score reflects patterns that have actually predicted real paying customers and expansions in that specific business, and it shows which signals drove a given score rather than leaving the logic as a black box or a static point value assigned by committee.
Not fully. Third-party reporting commonly places a Starter plan near $999/month for up to 2 sales reps and a Growth plan near $2,499/month for up to 10 reps, but larger deployments with higher lead volume require a custom quote, and MadKudu does not publish a complete self-serve rate card on its website.
No, not as of the current version of this page. MadKudu was acquired by HG Insights in August 2025 and is now delivered as part of HG Insights’ Revenue Growth Agentic Ecosystem alongside its other data and intelligence products, rather than being sold as a fully standalone platform.
Not well. Its scoring models are trained on a company's own past conversions, free-trial signups, product usage, and closed-won or expansion deals, so a business without much of that history yet will not get a model worth trusting. It's built for companies with enough volume and history to train something meaningful, not an early-stage product with a handful of closed deals.
Not sure if MadKudu’s predictive model fits your PLG funnel?
We'll look at how much historical conversion data your product actually has before recommending a predictive scoring platform, sometimes a real ML model is worth the investment, sometimes a lighter rules-based setup covers what your team needs today.
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