> How MadKudu builds predictive Scoring

We built the predictive scoring engine, so that you don't have to. Learn more about the different steps that MadKudu takes to build predictive lead scoring. 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.

3.Mapping

Map raw data to standard MadKudu activity and traits.

4.Enrichment

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

5.Computations

Build computations on top of your individual activities or traits.

Step 1

Pulling The Data...

2. Configuring

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

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

Step 2

3. Predictive Scoring

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

1. Sync

Set up sync to the platform

2. Workflow

Build routing workflows

3. Monitoring

Track how well the predictive scoring 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.

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MadKudu's predictions built

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