Greetings! This presentation takes raw data from ContentFly’s customer charges and turns it into graphic visuals that customers, clients and co-workers can easily understand. We first look at cohort data from the three countries that produce the most ContentFly customers - the US, Canada and Australia. This search is limited in its generalizability (see “Limitations” pages) but shows items that might be helpful when combined with other data. Then we look at three visuals that take all of given data and create a layer cake, with cohorts determined by month.

ContentFly appears to have had a strong November cohort when it comes to American customers, accounting for 83% of all known US sales in December.

Canada’s November output was also relatively strong. Compared to the US, Canada’s December cohort brought in a greater (though still small) share of the month’s profits.

The contrast between the pre-November and November cohorts is most pronounced in Australia, where only 3% of December profits came from the pre-November cohort.

• ContentFly had a strong January, both in terms of profit and in terms of retention
• February was a tough month, and the effects cannot likely be explained by COVID-19 only
• ContentFly struggled during COVID-19, and did not recover until at least August
• August and October cohorts retained their profit well
• November was a good month for return business

• First, this study consists of a relatively short time frame, so any longterm trends would be hard to gauge and any seasonal trends almost impossible to gauge. This data would be better served as a guide to see which business decisions and external factors may have helped recruit and retain clients in 2020.
• Second, 2020 was a very strange year! COVID-19 threw a wrench in almost every business, and it is impossible to know what would have happened outside of the global pandemic.

• Lastly, it is important to make a remark about the the American, Canadian and Australian data in the first half of this presentation. The majority of entries into the given database were not coded by country. I took care of a lot of this by adding country codes for every entry that had a repeated customer. For example: if customer X had a country code “US” for a charge made in May, and then later had a charge in November, I added the country code “US” to the November entry. However, 36% of all charges in the data belonged to customers who were never assigned a country code. Moreover, no profits were shown from the three top countries before October 16. This means that our data from the three countries is extremely limited!