MadKudu pulls data from your system in order to push scores and segmentations into your system.
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 so that they can be reused across the platform. This standardizing process result in what is called a mapping.
The historical data MadKudu pulls from your system brings the "facts" to the machine so that it can learn. These can be fields from free-fill forms prospects fill in, or activity behavioral data. 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?
The following types of mappings will answer these questions:
- Attribute mapping - Standardization of your demo-, firmo-, techno-graphic enrichment.
- Event mapping - Standardization of your behavioral data (activities performed by your leads).
- Audience mapping - Who are we looking at ?
- Conversion Mapping - What success criteria are we trying to predict?
Some common examples 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
On each mapping page in the upper righthand corner, you will find a button that reads "Check Volumes". Following this link will take you to a page where you can gain insights about your mapped data.