MadKudu

MadKudu is the scoring platform that PLG companies reach for when they need to figure out which free users are likely to convert to paid. The core problem it solves is specific: you have thousands of users on a free plan, your sales team cannot call all of them, and you need a data-driven way to identify which accounts are worth pursuing. MadKudu builds ML models on your historical conversion data to answer exactly that question.

The models combine three data types: firmographic data (company size, industry, funding), behavioral data (feature usage patterns, frequency, depth), and engagement data (marketing touches, content consumption). By training on accounts that actually converted, the model learns which combinations of signals predict conversion in your specific business. This is meaningfully different from rule-based scoring where an ops person guesses that “used feature X three times equals high intent.”

Score explanations are a practical feature for sales teams. Instead of just seeing a number, reps see the contributing factors: “This account scores 85 because they have 12 active users, used the API integration, and match the firmographic profile of converted accounts.” This context helps reps have better first conversations with prospects.

The main barrier is data requirements. MadKudu needs enough historical conversion data to train a meaningful model, which typically means hundreds of conversions at minimum. Early-stage companies or those with low conversion volume may not have enough data for the ML models to produce reliable predictions. The implementation process also takes weeks, including data integration, model training, and validation.

At $2k+/mo, MadKudu is an investment that makes sense when the alternative is a sales team spending time on low-probability accounts. For PLG companies with real volume, the ROI calculation usually works out. For sales-led organizations without a self-serve product, the tool is solving a problem you do not have.