New Business Model - Issue 6

Posted on 9/21/2007 by Jim Pickerell | Printable Version | Comments (1)

Issue 6 - The explosion of growth in content means the average return per image (RPI) will fall. However, it is no less expensive to produce images than it used to be.

In November 2003, I began tracking Getty Images return-per-image by dividing the total number of RM and RF images on the Creative section of their site into the total revenue generated in the previous four quarters. This has revealed some startling trends.

Since last fall, I have included the Rights Ready numbers in the RM category because Getty combines these two when it reports revenue. Currently, there are 905,922 RM images and 269,770 RR for a total of 1,174,692.

There are currently 844,227 images on the Getty Web site, but Getty has recently pulled 120,000 off its site and placed them into its Valueline brand the company will distribute through Punchstock at discount prices.

Getty's RM revenue for the last four quarters has totaled $317.6 million.  RF revenue totaled $321.07 million for a combined total of $638.67 million.

The total revenue growth for RM over the four-year period has been $49.3 million or about 18%, while the number of images on the site has grown 340%. In the RF category, the revenue has grown an impressive 160%  - but about 124% of that growth was due to price increases, not increased usage. The company also acquired two very large RF distributors - Digital Vision and Stockbyte - during the period. During this same period, the total number of RF images on the site grew 509%, causing the average return per  image to drop about 50%.

 Average RPI
YearRMRFCombined RM &RF





















Number of Images
YearRMRFCombined RM & RF






















In the last four years, Getty's gross revenue from still-image licensing has grown about 50%, but not nearly as fast as the number of images added to the collection. As a result, image suppliers must produce more images each year just to stay even.

In order to determine the number of images in the file, I have searched the RM section for horizontal, vertical, panoramic and square each November, then totaled the four figures. I used the same procedure to find the number of RF images.

This technique results in a rough approximation of the average return-per-image rather than a precise figure, as images are being added throughout the year. There is no way to tie actual images available to revenue generated for a shorter period. Nevertheless, since the same procedure was used each year, I believe the trends are an accurate reflection of what has been happening.

Photographers should keep in mind that these averages reflect the gross return for Getty. Photographers or Image Partner agencies will receive a small portion of this based on their royalty percentage. It should also be recognized that these numbers are only averages.

Copyright © 2007 Jim Pickerell. The above article may not be copied, reproduced, excerpted or distributed in any manner without written permission from the author. All requests should be submitted to Selling Stock at 10319 Westlake Drive, Suite 162, Bethesda, MD 20817, phone 301-461-7627, e-mail: wvz@fpcubgbf.pbz

Jim Pickerell is founder of, an online newsletter that publishes daily. He is also available for personal telephone consultations on pricing and other matters related to stock photography. He occasionally acts as an expert witness on matters related to stock photography. For his current curriculum vitae go to:  


  • Don Farrall Posted Sep 22, 2007
    While I think your analysis is basically valid I would suggest the following things to consider. Getty’s collection has grown in numbers of images and numbers of options for buyers. This growth has been substantial over the last few years, as you clearly document. This is one way that Getty has remained the top player in the stock photography industry. Getty grew this way for several reasons, to make more money, and to maintain their position in the industry. They did not seek to grow their collection to increase RPI. RPI is not of concern to Getty’s clients, buy variety and depth of choice are.

    Businesses don’t exist to serve their suppliers, they exist to serve clients and to make money. It is clearly a consequence of the increased number of images available that has driven down average RPI. The only way for RPI not to have gone down, would have been for Getty to have matched their increase in the number of new images with the increase in sales. This of course would be impossible, since the increase in sales that Getty experienced was a result of the availability of more content, which allowed Getty to become a better competitor.

    The presumption that because a seller has an increase in products available to sell, will result in an equally increased level of sales, is simplistic. Yet for RPI not to decrease this would have to be the case.

    I have been shooting stock for many years and I have been a supplier to Gettyimages for the last ten years. I have had many conversations with other stock photographers, many of whom are major players. I think the concept of RPI as most photographers, and as you have calculated it here, is flawed.

    There is no reason to think that an image should sell at the same level for many years in a row. Images become dated, concepts become over used, and some images reach market saturation as the majority of potential buyers for that specific image have already purchased the photo. ( this is more the case with RF images ). I don’t think too many people would disagree with this premise. For this reason calculating RPI by simply adding up all of the images in a collection and dividing the revenue by sales gives an unclear picture of what is going on.

    If older images are not thrown out of a collection then each successive year the collection has a larger percentage of irrelevant images. For example, a collection of images, ( either an individual photographer’s collection, or the collection of an agency) that is ten years old, is going to include more dated, irrelevant, and unsaleable images than a collection that is five years old.

    Getty’s growth in image numbers comes form a number of factors, and one of them is how long the collection has been growing and how long the collections that they acquired have been in existence. Getty could increase RPI and make all photographers happier if they threw out any image over three years old, Right? Well RPI by this calculation would go up, but total sales would go down by some degree and some photographers would be mad, because some older images do sell and no one wants to see the number of images working for them decreased. New suppliers would like this because they would be competing with fewer images, but for the most part Getty positions the photos in a search result by age with the oldest images buried the deepest in the results. Getty has no incentive to remove images, they are already keyworded and uploaded and they add to the overall “size and depth” of the collection.

    I personally have over 2000 images with Getty. The vast majority of my income comes form the most recent 25% of those images. Tossing out the bottom, oldest, (8-10 year old) images would have very little effect on my revenue; but tossing them out would increase my RPI, by this method.

    However, I don’t care about historic RPI. I hope I have made it clear here why I believe that this number is always going to be going down, and why I don’t find it useful. What I do care about is the potential for revenue from new images.

    To judge this I look at my sales from the last three years. I consider sales form images older than three years to be a bonus. Sure I have a few high earning images that are older than that, but they are the exception and they distort the reality if I try and include them. So I look back three years, and I track the sales per image, I average the three year images, by three, the two year images by two and the one year images by one. As in your calculation, this is only an approximation, because images become available through out the year on different months. I consider the resulting RPI to be a good indication of how any new image that I produce will do over the next three years. For this to be relevant the images should not represent a vast shift in style or subject matter.

    This is a far more relevant number to me. It tells me what I can expect to earn from new work that I produce. It tells me how much I have to work with for production, and it is what keeps me producing new material when many photographers are bailing out on stock. By this method my RPI has remained surprisingly steady. As I produce new material I am earning the next three years Accounts Receivables. How can I be sure I am correct? I can’t. Stock production is, and always has been, a speculative business.

    For the record most of my images are RF. I will also concede that what works for me may not be relevant to others who’s style and subject matter are quite different from mine. Still I can’t help but believe that this method is more valid than a straight historic full collection calculation. My work is commercial, not editorial, for the most part, and I can’t say with certainty how that distinction might effect this process.

    I do believe that overall stock sales are down, and since many more photographers are in the game splitting up the royalty revenue pie, that individual photographers are for the most part earning less. But while individual photographers may be seeing a 50% reduction in earnings (compared to the last 3-5 years) as has been reported, overall stock sales appear to be just flat, not off by anything like 50%. It is the pie splitting that is the biggest driver in what is seen as a decline in the stock photo industry.

    My revenue remains fairly consistent as long as I am continually producing new material.

    In my opinion, micro stock is having a pretty big negative effect on both the industry and individual photographers, a much bigger effect than anyone on the industry side is wiling to concede, but that is a subject for another analysis.

    Don Farrall

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