Calculating Monthly Earnings
If you’ve decided that you have a good shot at making money on a per-customer basis, it’s easy to continue this back-of-the-envelope method to calculate projected monthly earnings.
The clicks/month data can be obtained from Baidu’s keyword germany phone number list research tool. However, remember at this point, we’re still using data that is probably understated (more on that below).
How Far Can You Expand?
Baidu is great to start off with as it’s great for spearheading entry and gathering data; however, it’s not a full-marketing solution.
It is quite hard to guess how far a campaign could expand via digital marketing, but here are our guesses based on our decade-plus of experience:
Baidu ads themselves: Potential 200% growth.
Adding content marketing: Potential 300% growth (but it will take two years to do so).
Adding other ad channels: Another 200% increase.
So, if a company is making 16,992 USD/month via only the initial Baidu campaign, and then you expand an additional 700%, the earnings would increase to about 136K USD/month.
I’ve come up with these numbers by running our models for current projects. In other words, we have one person on our team generate the numbers for a given project as if we’ve never worked on it before. We then later compare those projections to the actual numbers. No two projects are the same, so this isn’t an exact science, but for getting a quick sense of the potential, we found it to be more than sufficient.
But What About Cash?
These numbers help you know what to do, especially if you have the cash to do it. But what if you don’t have the cash yet? What if you need to get the cash flowing in to fund the expansion?
A customer paying out 4,776 USD over two-and-a-half years is not the same as a customer paying you 4,776 USD upfront.
By inputting the previous numbers into an Excel/Google Sheet, it’s possible to estimate all the future cash flows. I think this is a bit beyond the scope of this blog post, but I’m glad to work with companies on more advanced models on a case-by-case basis.