1. Machine Learning is a powerful statistical tool to help predict the probability that a prospect is a good candidate for your product/service.
2. Your firm already has the data it needs to conduct Machine Learning. Find who in your organization is responsible for managing data and nd out how you can work with it.
3. There is a Machine Learning mantra that “more data usually beats better algorithms”. External data combined with internal data leads to better models.
4. Start with an ‘intuition’ algorithm based on your own knowledge supplemented with external data.
5. Show your ML algorithms to Sales, Marketing, Customer Service, anyone who works with the customer to see if it makes sense. ML is part science, part art.

6. Getting a good predictive model requires an iterative process and may require testing many different types of algorithms before finding the best one for your situation. Support ‘failing fast.’
7. ML algorithms continually need to be updated, so review your algorithms at least each quarter. Keep data up to date and use only what is relevant.
8. A common way of using ML is to build an Ideal Consumer Profile combined with Lead Scoring.
9. For best results, work with a company that specializes in machine learning, such as Vijilent.

Read full white paper "Machine Learning for Lead Qualification" here.