MadKudu pulls data from your system on one end of the pipeline to push scores and segmentation back into your system on the other end of the pipeline.
Several steps happen in between to generate these scores. Because your systems don't necessarily have the same format of data, a step of data preparation and standardization is necessary to format your data according to MadKudu standards to be reused across the platform. This process is called a mapping.
- Standardization of your demo, firmo, technographic enrichment : Attribute mapping
- Standardization of your behavioral data (activities performed by your leads) : Event mapping
The historical data MadKudu pulls from your system brings the "facts" to the machine so that it can learn. But, we also need to give it guidance on how you want it to score, i.e. who and what are you trying to predict?
Your mappings will answer these questions.
- Who are we looking at ? : Audience mapping
- What success are we trying to predict? : Conversion Mapping
Some common exemples of audience and conversions to predict are:
- Inbound leads highly likely to become a New Business Open Opportunity
- Inbound leads highly likely to become a New Business Closed Won Opportunity
- Inbound leads highly likely to become a New Business Closed Won Opportunity with a minimum expected spend of $X