salepredict3: automated test results
Based on a suggestion by Ché Lucero (LinkedIn), I wrote a test to see exactly how accurate this machine is.
I had 41 domains already entered into the engine and categorised as Sale or Fail, so the test was based on those.
For each of the domains, the test:
- changed the domain’s type from sale/fail to prospect
- retrained the neural net using the rest of the domains as its reference data
- calculated how much of a match the domain was to a sale using that neural net
- if the calculation indicated correctly a sale or a fail, then that counted as a correct test
- finally, clean up – reset the domain’s type back to sale/fail, ready for the next test
After 41 tests, it got 27 correct – an accuracy of 65.85%. That’s much more than chance (50%).
I’m going to get some more data now, but I expect it will only improve the value, not decrease it.
What does this mean for your own business?
Well, let’s say you have 100 companies you can potentially sell to, and you expect that 50% of them might end up being a waste of time, but you still need to spend about 2 hours on each in order to find that out.
Without using my engine, after 100 hours of selling, you will have made 25 sales. (100 hours is 50 companies. 50% success rate so 50/2 = 25).
With my engine, after 100 hours of selling, you will have made 33 sales, because it will have pre-ordered the companies and got it 66% correct, so in the first 50 companies, it will have correctly placed 66% of all successful sales.