> How MadKudu builds predictions

We built the predictive engine, so that you don't have to. Learn more about the different steps that MadKudu takes to build predictions. You can then use the predictions to unlock revenue in any channel.

1.Data Preparation

We prepare customer data profiles by making sense of your raw data from various connected sources.

1.Data Import

Import raw data from your multiple data sources connected to MadKudu, which includes customer relationship management (CRM) platforms, customer data platforms (CDP), marketing automation platforms (MAP) and product and web analytic tools.

2.Identity Resolution

Resolve identity of records in different data sources and link them to a primary record with individual lead and account entity.


Map raw data to standard MadKudu activity and traits.


Enrich your data with the best in-class third party data on demographic, firmographic and technographic traits.


Build computations on top of your individual activities or traits.

Step 1

Pulling The Data...


We build a model to predict the fit and likelihood of conversion of your leads or accounts.

  1. Create a training set
  2. Analyze which traits or activities contribute most to conversions
  3. Build trees that create paths of ideal customer profile and behavior to conversions
  4. Assemble the trees together
  5. Smooth out the scores
  6. Create signals that highlight what goes into the model

Step 2


We push the MadKudu scores and signals to your platform of choice, and prepare automated workflows based on the scores.


Set up sync to the platform


Build routing workflows


Track how well the prediction model is contributing to bottom-line pipeline generated for the business

Step 3

That's It!
Hope this makes sense. If not, please let me know in the comments.


MadKudu's predictions built

See it in action