I want to generate new leads for a small business banking company and I have decided to use logistic regression with a binary outcome (1=customer of the bank, 0=not a customer of the bank). All the records (people with small businesses) for the 1s were provided by the bank itself from their database. The dataset contained all categorical variables only initially. A match algorithm was run against our own open+public data db with those records from the bank and then we derived some further numerical count information (interval and ratio variables) about those records that matched with our db.
Now to get the 0s (not a customer), I decided to take a subset of small business owners from our own db. However, since I have no idea about population sizes, I decided to use a balanced sample (50% 1s, and 50% 0s) for the modelling process.
I wish to know how this would impact the lift chart and is the model going to be any good even if the lift chart looks convincing? Model accuracy [(TP+TN)/(TP+TN+FP+FN)] is approximately 78-80% with the current approach, however I highly doubt that number.