An event production company I recently worked with had been experiencing a slowdown in revenue from clients, impacting total profit as a result. So I took it upon myself to look back into the company records to see if history offered any insights. I was right.
I knew every year this company had changed not just the prices, but the price structure of its production offerings. Sometimes they would require a minimum number of services from the client in order to produce an event, and other times, they would offer a flat fee for a package of services, and a higher fee for another package. The prices themselves and complexity of the price structure varied from year to year, but without any kind of pattern. In other words, every year, this company was shooting in the dark in setting prices for one of their most important sources of revenue.
In performing my analysis, I initially looked at two major series of data: first, the prices in each year and second, the total revenue from all clients that year. Pretty simple. But as I went along, I realized there were other important and interesting numbers to consider, such as the total number of clients participating, the average payment per client, the median payment per client for each quarter, etc. Each of these numbers supplied valuable information.
My most important discovery was that the company earned the most money from client fees–by far–in a six month period several years back when the prices were lowest. Moreover, in spite of the myriad jimmying with the price structure and product offerings, that very same period turned out to have the simplest price structure of all the ones on record! Tying it all together was a final key piece of data: this very same period also saw the greatest number of clients on a monthly basis the company had ever had.
Right in line with these observations, I noticed that in the most recent months and quarters, the company earned the lowest amount of total client revenue, and the lowest average payment per client. Were the prices high and the offerings complicated? Indeed. Sure enough, these months also had the most complex set of prices and product offerings, as well as the highest prices overall–ever, in the history of the company.
My conclusions from this analysis were that clients, in the aggregate, were interested in two things: (1) low prices and (2) simple options. It was no coincidence that the company received the least amount of total client revenue when prices were the highest, and when the price structure and offerings were the most complicated.
By offering a simpler set of options and slightly lower prices overall for those options, the company could spark the interest of more clients, and therefore receive more total client revenue during the year. In spite of the lower payment per client, profit would increase. It’s fascinating what the right kind of analysis can do for your company.
