LookStat – Microstock Analytics

I searched iStock for the Top 100 Best Match results for: African American, Asian, Caucasian, and Hispanic. I recorded the total number of results, and the composition of the Top 100 by Collection.

Total Number of Results:

• The above are the number of results that contain the keyword in question. So, if you looked images that only contained Hispanic models, the number would be lower.

Composition of Top 100 Best Match Results by Collection

Things That Stood Out:

• There were four times as many Caucasian images as there were of the other three ethnicities combined. I expected a disparity, just not one this pronounced. (I’m going to analyze the downloads for each of the searches to see if I find a similar trend.)
• The number of Agency & Vetta images in this dataset surprised me. 272 out of 400 (68%) of these results were in the most expensive collections.
• Top Three Agency Contributors by Number of Images: Blend Images(19), Albany Pictures(18), Rubberball(17)
• Top Three Vetta Contributors by Number of Images: Morganl(11), Uberstock(7), Bortonia(6)

I searched for ‘business’ on iStockphoto, and analyzed the top 100 results returned for Best Match & Downloads sorts. For each image, I recorded:

• Total Views & Downloads
• Upload Date
• Search Position

Using the data above, I was able to calculate the age of each image, and therefore, calculate the average annual downloads for each image. I used the median instead of the mean for averages, to prevent outliers from skewing the results too badly.

Median Age of Image

• This is the difference between the upload date of each image and the date of the analysis – April 30, 2011.

Median Value for Total Downloads

• This is the median value of the total downloads for each images in the Top 100 search results.

Median Value for Annual Downloads

• This is the Median Value of the Total Downloads divided by the age of each image.

Things That Stood Out to Me

• 46 of the 100 images were present in both Best Match and Downloads sorts.
• I expected a bigger spread in median age of image between Best Match and Downloads.
• I was also surprised by the parity between the median annual downloads in each case.

Over the next few days, I’ll analyze these searches in more detail, and compare them to other subject categories.

If there are topics that interest you, please let me know in the comments.

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 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.

I’m planning a series of posts on summer sports and activities (and related keywords etc) to help with your shoot planning.

While doing some background research I came across a couple of very interesting data repositories at the Census & The National Association of Sporting Goods Manufacturers.

I was playing around with the data for consumer spending and participation and decided to run a quick analysis on the average spending per participant for each of the Top 5. The data blew me away.

Top 5 Recreational Pursuits by Spending

NB: Spending & Participation Data in Millions; Source: NSGA

The Fastest Growing of the Top 5? Hunting…

Source: NSGA

Things That Surprised Me

To be honest, almost everything about the data above surprised me. I expected golf to top the total and per person spending charts and I definitely didn’t expect hunting to be on this chart in the first place.

Based on my surprise around hunting & golf, I decided to do a quick search on iStock & Getty Images to see how many results showed up for stock imagery around these subjects. I expected more golf than hunting shots all around.

At iStock, there were 16,576 images returned for a search for ‘golf’ and 6,806 images returned for a search on ‘hunting’. So far so good. I expected to see the same thing at Getty but it was not to be. Getty returns 10,291 results for golf and 12,990 results for hunting. I’m not really sure how to explain the fact that there are more hunting images at Getty but if you have some ideas, please share them in the comments.

Conclusions & Observations

I’m going to have to add hunting to the list of activities that I profile over the next few weeks!

If you believe that how much someone is willing to spend on something is a proxy for how much a marketer will spend to sell them that something, then hunting is clearly an important activity to study.

A Small Request

If you’ve read this far and you found this post interesting, please tweet about it, follow us on twitter, or leave a comment on our blog. In return, I will contact you to get your vote on which activities you’d like to see us profile in our sports/leisure series.

If you help us share our content, the least we can do is make sure we listen when figuring out what to write about next. (Of course, if you just write to me, I will listen anyway, but that is neither here nor there.)

Thanks for reading this far.