After I was surprised by the low numbers of images for a range of ethnicities on iStock, I analyzed annual downloads (Total DLs/age of image) to see if the low result counts were justified.
Average Age of Image in BM vs. Download Sorts
- Given the high number of Agency images in Best Match, I wasn’t surprised by the low average age of BM results.
Conclusions
For yesterday’s post on Summer images, I analyzed data on 1,000 images across ten summer-related searches. I was curious about the relationship between the age of an image and the number of downloads it had.
Total Downloads vs. Age of Image
- The images with the most lifetime downloads are not the oldest ones — rather they are between 3-5 years old.
- The correlation between age & downloads is very close to zero which suggest little to no correlation between the two.
Annual Downloads vs. Age of Image
- The distribution looks very similar here.
Conclusions:
- Just because an image is old, doesn’t mean it will get a lot of downloads.
- New images have come in that are downloaded at a rapid rate.
- Again, as we saw with contributors, age doesn’t appear to be the dominant factor.
The Hispanic and Asian population in the USA grew over 43% from 2000-2010. You can use LookStat collections to explore the impact of this growth on your microstock sales. For example, if you wanted to analyze Hispanic vs. Asian models in business and lifestyle shoots, you would proceed as follows:
1. Create a collection
2. Search for the following terms & add all results.
- HAVING KEYWORDS: business%, hispanic%
- NOT HAVING KEYWORDS: african%, caucasian, asian%, indian%, divers%, mixed%
3. Repeat steps 1 & 2 for Asian models by using the following terms:
- HAVING KEYWORDS: business%, asian%
- NOT HAVING KEYWORDS: african%, caucasian, hispanic%, indian%, divers%, mixed%
4. Analyze the Results:
- LookStat updates collection stats hourly. After that, you can compare the performance of each set of models.
- Pay attention to RPI and Price per download numbers since the collections will likely be of different sizes.
5. Notes:
- The ‘%’ sign symbol is a wild-card character that adds all variations & endings
- Because we’re trying to isolate each group, I’ve added exclusion keywords so each set only contains the relevant models.
Sign up for free today and start using LookStat to make casting decisions that make you more money.
While analyzing data for my last post on downloads per contributor, I became curious about the relationship between downloads per image and portfolio size.
Chart: Total Downloads per Image
- I was surprised to see the spike in downloads per image in the middle band.
- I blame exceptional performers, rather than a magic trend favoring those with 5,000-7,500 images online.
- Downloads per image decline (mostly) as portfolio size increases, but at a slower rate.
Conclusions:
If you keep uploading, your earnings will rise, but at a slower rate than your portfolio does. Although this seems depressing, it shouldn’t be.
- You’ll make more as you upload more.
- You can fight the trend by improving your skills and analyzing your portfolio to uncover better opportunities.
The strong performers in the middle tier are proof that it’s possible.
Methodology:
I analyzed the portfolio and download data of the Top ~2000 contributors (based on Total Downloads) and plotted:
- Histogram of Number of Photographers versus Portfolio Size
- Average Annual Downloads per Photographer for Each Portfolio Bucket
Chart 1: Histogram of Photographer Count vs. Portfolio Size
- 82% of the photographers in the Top 2,000 have portfolios of 2,500 images, or less.
- The last category is larger than the others because there are so few contributors with portfolios over 10,000 images.
Chart 2: Average Annual Downloads by Portfolio Size
Conclusions:
- I was very surprised to find that less than 20% of photographers in this dataset had portfolios larger than 2,500 images.
- I found the download numbers made more sense since all things being equal, more images should lead to higher earnings.
- I’m curious about average downloads per image for each of the above bands — I’ll post on this subject later this week.
Methodology:
I got public data from iStockcharts for the Top 2000 contributors, ranked by Total Downloads. I then plotted the following:
- Rank vs. Years contributing of the Top 2000
- Average & Min Years contributing for the Top 1000
- Histogram of Years Contributing for the Top 200
My goal was to examine tenure vs. performance to see if the number of years you had been at iStock made a difference.
Chart 1: Top 2000 Contributors: Rank vs. Years Contributing
- The average for the entire set was 5.4 years.
- The newest member of the Top 200 had been contributing for less than 3 years.
- The entire set was in the range of 1.5 – 9.0 years.
Chart 2: Top 1000 Contributors — Average & Min Years Contributing
- The most notable thing here is how little variation there is — for the most part, there is no difference in experience between those ranked 1-200 and those ranked 800-1000. I think this is amazing.
Chart 3: Top 200 Contributors by Years Contributing
- 48.5% (97/200) contributors had between 4-6 years of experience.
Conclusions
- I didn’t expect to find this, but there is very little correlation between your iStock performance and your years of experience at iStock.
- The R-squared coefficient is 0.03. It’s range is between 0 (no correlation) and 1 (perfectly correlated).
- Chart 1′s distribution is very uniform for each ranking band.
- No difference in avg & min years contributing for the Top 1000.
- It takes about 2 years to build up a portfolio large enough to break into the Top 2000 at iStock.
- After that, quality and consistency in uploading is what counts.
We built a calculator that shows you how RPI and upload rate affect your earnings. While playing around with it, we discovered that if you have a monthly RPI of $4 and upload 400 images each month, you’ll earn $1,036,800 in 36 months!
Furthermore, if your RPI is $5/month (which is true for many photographers), you can be a millionaire in less than 3 years if you increase your production.
A million dollars in less than 3 years. What are you waiting for? Sign up for LookStat and let’s go.
We care about analytics, not addition. We don’t track your sales at every site, but we do make you more money by increasing your RPI and finding gaps in your portfolio.
The top 5 reasons you need LookStat Analytics are:
5. Know when to upload your images to maximize sales.
4. Increase your RPI.
3. Benchmark your performance.
2. Find out what to shoot next.
1. Make more money.
LookStat: Analytics, Not Addition. Get the LookStat Advantage and sign up for LookStat Analytics today!
According to the Society of American Florists (SAF), Christmas/Hannukah is the top holiday for flower sales in the USA.
These stats surprised me, because I think of Valentine’s Day & Mother’s Day as the primary flower-buying occasions.
The SAF’s data show that centerpieces and red poinsettia purchases are the primary drivers of holiday flower sales. You can read more at the SAF’s site, AboutFlowers.com
Pay attention to these details when planning your microstock shoots so you have the right props and subjects available. (You do plan your shoots don’t you?)
Rasmus Rasmussen, an experienced iStock inspector (among other things), has a great post on his blog called “10 Subjects to Water Down Any Portfolio.” His biggest pet peeve is listed below:
1. Bugs on Flowers
Congratulations on your macro lens. At least I hope you used a macro lens, so you could get somewhat close. Bugs on flowers is my personal pet peeve subject. Sure, flowers are pretty and bugs are interesting creatures, but as a photo it just doesn’t do it for me. Most of the time, it comes out looking like you were just practicing and like the gazillion existing images just like it.
For the remaining nine, read the full post at his blog.
