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I have firmographic data of all the possible customers. Data includes sales, profitability, capital, organisation size, geographic location, industry, etc. What is the best way of identifying new potential customers from this data? I want to identify customers matching my existing customer profile. For example, companies from XX industry and YY Sales are likely to buy, or profitable companies with the employee size NN are likely to buy. I want to understand these patterns from existing customers, and potential customers with similar profiles should be identified.

Is it possible to create a predictive model with the data? If yes, what is the approach for it? Or is clustering the preferred approach for similar problems?

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There are multiple approaches that can be taken to identify potential customers from existing customer data. One approach is to use predictive modeling to identify patterns in the data and make predictions about which potential customers are likely to buy from you. Another approach is to use clustering algorithms to group together similar customers based on their firmographic data and identify potential customers that belong to the same cluster as your existing customers.

In terms of creating a predictive model with the data, you could use a supervised machine learning algorithm such as a decision tree or a logistic regression to train a model on your existing customer data. The model would learn the patterns in the data and be able to make predictions about which potential customers are likely to buy from you.

Alternatively, if you want to use clustering to identify potential customers, you could use an unsupervised learning algorithm such as K-means clustering to group together similar customers based on their firmographic data. Once the clusters have been identified, you could then look at the characteristics of each cluster and identify potential customers that have similar characteristics to your existing customers.

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