Question

Give an example of combining unsupervised and supervise learning methods to provide solutions in real world.

How should companies plan and deal with the consequence of implementing an analytics solution?

Answer 1

Let us see how supervised and unsupervised learning is used in the field of sales.

Supervised learning models are used to prioritize leads- Lead Prioritization. The algorithm analyses leads and classifies them into pre-determined priority levels (for example high, medium or low). The algorithm identifies significant parameters and then scores each lead. The scores are then used for classification.

Unsupervised learning is similarly used to identify what characteristics make a good salesperson. By analysing data from multiple sources on each salesperson (eg. Pipeline size, conversion ratio, follow up frequency, response language etc), the algorithm automatically classifies salespeople into groups. By seeing their actual performance in measurable metrics (like % achievement) we can interpret the classifications of the algorithm. Then by analysing each cluster, we can identify what characteristics differentiate a good salesperson from a bad one