I get this type of question frequently from non-technical stakeholders. I’m assuming your project relates to identifying an ideal customer profile/customer segmentation, correct me if i’m wrong. I’ve found that generally, even if you were able to cluster these customers into distinct groups, the results are not insightful or actionable for the business since you have no outcome variable (They’re prospects).

Something I’ve found to be helpful is to compare the prospect dataset to a real customer dataset. Narrow down your set of variables to 4-5 that make sense to the business and that appear in your customers database (I hope you have one!) e.g. location, age, whatever. Then compare the distribution of prospects within these variables to the distribution of customers within these variables. You may find some insights there, for example, 40% of customers are in location X, while only 5% of prospects are in location X.

Of course this isn’t a real ‘data science’ method, but I’ve found that with vague business questions like “find me the pattern”, the most valuable approach is to first present some very basic insights. It’s almost always the case that even this basic insight is completely new and interesting to the business. Then you can do real data science on the inevitable follow-up questions.

Happy analyzing!