Predictive analytics works, by training a machine learning model to identify the outcome you desire.
At MadKudu, we do this with several inputs from you.
1) Leveraging your historical transactions, win or lose - the model needs to know who to target.
2) Leveraging historical behaviors/activity for leads that convert - the model needs to know when your lead is heating up, or cooling off. (For Likelihood to Buy Models)
The above brings the volume of data and historical "facts" for the machine to learn. But, we also need to give it guidance on how you want it to score - who are you trying to predict? This differs between customer to customer, but some of the more common conversions to predict are:
- Leads highly likely to become an Open Opportunity and New Business
- Leads highly likely to become a closed won Opportunity(Or Deal) and New Business
- Leads highly likely to become a closed won Opportunity(Or Deal) and have a minimum expected spend of X $ and New Business
Additionally, many clients have their CRM many years and as we all know, data clean up projects are nearly always running. For example, if you had a major data cleansing and harmonization project complete in the recent past, perhaps from that time onwards, your CRM entries are most reliable. In this case, you will need to advise us if there is a date range from we should focus on or ignore.