Yesterday, StockPerformer announced their new collection statistic set, and I am amazed at what I get out of it.
Until now, I had collections of the same images from a single shooting day or week for each agency separately. It allowed me to track how images from a particular set would sell at the different agencies. But if I wanted to add up how much I made in total from a set, I had to manually summarize the numbers from 5 or 7 agencies.
This is now completely obsolete. The new collection stats are doing it for me: And StockPerformer was smart enough to already summarize the collections for me as I named them always the same across agencies, so without any doing on my own, I now have a great overview of any given set of images and how they are doing on my agencies.
I don’t spend too much time on doing all the analytics myself these days, so I have an overall impression in mind of “who sells best” – I have to admit looking through some of the collections I was proven wrong. Sometimes the iStock Partner Program is indeed making similar returns to those of Shutterstock. And in some collections Fotolia and Dreamstime are very close to what I make from Shutterstock and iStock.
So this will give me new opportunities to easily analyze which of my images sell where and how often. And hopefully draw some conclusions out of it.
If you are interested in which images sold at the different agencies, you can also get a list of them with thumbnails showing for each agency. This might become a helpful tool to decide if I want to remove certain images from other agencies and only offer them at one place exclusively in the future. At least Fotolia and Dreamstime do offer image exclusivity, so this could be an option to optimize the returns from my imagery.
I am currently in an email exchange with the StockPerformer guys, Luis and Oliver, to see if some of the data can be optimized. And they have also given me a hint that another big feature will be added to StockPerformer soon… so, stay tuned, this tool is really getting better and better. 🙂