Economic analysis of potential product purchasers by company metrics

Economic Analysis

The profiles of the two companies (yes or no) are actually fairly similar. However, the \\\"yes\\\" company is slightly larger on average, and with a higher growth rate. That company has a higher margin, lower leverage, is larger, and has a better current ratio. The employee count is one of the biggest differences, where the \\\"yes\\\" company is significantly differentiated from the \\\"no\\\" companies. That company is larger in terms of revenue from all sources, as well. In general, the yes companies are also from the English-speaking world. Companies from other companies are less likely to purchase the product. To predict what the remaining companies will do, the biggest thing is that the companies that would purchase are the companies that are the more successful. In general, Australia 33 and South Africa 21 are the most likely of the others to purchase. Both of these companies are from English-speaking countries, and both have a CAGR above the average for companies that would purchase. While India 6 is a large company, it is unreasonable to compare the size of the workforce between a company in, say, Australia, and one in India because of the difference in labor costs. The main edge case is India 18. That company is a little bit smaller than the prototypical customer, but there are definitely customers that size. But it has the financial health that fits the profile. Financial health is one of the main purchase criteria – companies that have relatively poor financial health in general are less qualified candidates. Looking at China .1, and that company has a similar profile to another customer. That makes it an interesting case. In terms of its metrics, it fits the profile of a large company but it not nearly as fast-growing or profitable as the typical customer. However, its profile is quite similar to an existing customer, so they might also be considered to be a potential client. The only one that is a definite no is India 0.06, which is a large company but not growing much. Given that growth matters here, that company is not a great candidate. Honestly, though, I don\\\'t pay sales people to take themselves out of the game. If you\\\'re working on my sales team, you\\\'re calling all of these companies.

Pie #1

Clearly, the first pie chart has a greater percentage of \\\"no\\\" than \\\"yes\\\". This might mean that the identification of metrics used for the last 25 companies did not rule as many of them out as it should have. That said, the change in percentage doesn\\\'t necessarily indicate a flaw in methodology – it could actually just reflect a difference in the two data sets. In essence, the pie chart comparison only indicates a need for further investigation; it says nothing in and of itself.

Bar #1

The bar chart above shows the top ten companies according to their revenue. The calculation are as follows:

Avg 1923072000

Median 1915770000

X Y #1

This chart really says nothing in terms of identifying a correlation between company age and size. For example, there are three companies that are two years old, and these each had different numbers of employees – 300, 640 and 690. The number of employees should correlate more with revenue, and with country of origin (i.e. labor cost) than with age, anyway.

So let\\\'s look at country of origin:

By revenue, South African companies are the largest, and Canadian companies are also a bit of an outlier. The companies we didn\\\'t have revenue figures for were excluded - those companies all were quite small by number of employees as well, so were probably smaller in terms of their revenues, but we don\\\'t know that.

Comparing number of employees by country, we get:

This highlights that there may not be a correlation between the country of origin and the number of employees, which is a bit counterintuitive. South Africa, with low labor costs, have the most employees per company, but also the highest revenue per country.. The final comparison will be between revenue and employees:

The following chart is revenue/employee, broken down by country:

This highlights that Mexican and South African companies are the most efficient, with good performance from Canadian and Emirati companies as well. But it also shows that within the industry, there is not that much deviation in terms of revenue per employee. It might be interesting to go from here in terms of predicting if there is a relationship between revenue/employee and interest in our product. They key is really to figure out if there are predictor variables – at this point there do not seem to be many that would be statistically valid. So in the real world, you keep analyzing until you find the good ones, or can determine that no good predictor variables exist.