Good Post on the Long Tail and Microstock (via Microstock Diaries)

Posted on November 29th, 2009 in Uncategorized | 1 Comment »

Lee has a great post on his blog about the Long Tail and the application of some of its ideas to microstock. The post and comments are well worth a read, but Lee’s summary gets to the heart of the matter.

  • If you want to create photos in the most popular subjects, expect that you’ll be competing on quality
  • If you want to create photos in long tail niches, keep your costs (and expectations) modest
  • Photos with a small market (low demand) might earn more in non-microstock markets

If you want to reap the greatest rewards, you have to find a way to play in the competitive categories around business, lifestyle, medical shoots etc. It’s possible to find areas where competition is relatively low, but volume (and therefore potential) rewards are likely to be lower. To cover many topics in the tail to boost your aggregate earnings, you have to pay attention to your production costs to make sure you can stay profitable.

Ultimately, as with everything, there’s no free lunch and you need to assess the market, your abilities and put in the work.

Microstock Pre-Production Article by Diego Cervo

Posted on March 27th, 2009 in Uncategorized | No Comments »

Diego Cervo (terrific microstocker and happy LookStat user!) has a great article on his pre-production process posted at The Factory by Moodboard. As with most production activity (in any domain) the more you can figure out up front, the more efficiently you’ll spend your resources. His site is being worked on but you can check out his portfolio at Shutterstock to see his awesome work.

Getting Emotional About Microstock – Some Thoughts on Supply & Demand

Posted on November 30th, 2008 in Uncategorized | 7 Comments »

Matt’s series on the Long Tail and microstock got me thinking and I decided to do some quick searches on human emotions on Dreamstime to see if anything interesting showed up in the data.

I did searches for 5 keywords (angry, bored, happy, sad, scared), ordered the results by download and captured views and downloads for the top 10 images. I also recorded the number of results returned. While this is completely non-rigorous analysis, there are some interesting observations.

If anyone has thoughts on how to better interpret this data, please don’t be shy. Also, if anyone would like a spreadsheet of the data, let me know and I’ll send it to you.

The things that jumped out at me from this exercise (in no particular order)

  • There are way more images returned from a search for ‘happy’ than for all of the other terms I looked at. While this does make sense from a marketing perspective, the skew seems out of whack when compared to the relative download counts.
  • Conversion rates for ‘bored’ and ‘scared’ are very high and image counts are very low. To only have 3,000 images for a generic emotion like ‘bored’ in a 4 million plus image universe is surprising to me.
  • Don’t ignore emotional variations when conducting shoots. Collectively, they account for a significant number of downloads and there are fewer results in the pool, so in theory standing out should be easier. Long live the Long Tail!
  • Search terms that have high conversion & relatively low result counts may indicate areas of opportunity but (much) more testing is needed before you can draw any conclusions.
  • PIcNiche is a really cool site and they basically automate what I’ve done here using Fotolia data. I think it’s well worth a visit as you plan your next shoot. (Thanks for the pointer to it Lee.)

Image Count (number of results returned when searching for a term)

The number of images that are returned when you search for ‘happy’ versus the other keywords is staggering. There are over 15 times as many images returned for ‘happy’ versus ‘sad’. While this lines up with intuition (happy people are better for marketing) the spread took me by surprise.

Average Downloads & Views for Top 10 Images

To get this data, I just sorted the results for each search by download count, recorded the download and view numbers for the top 10 results and then calculated the average. There are also some interesting things to note in the individual image data which I discuss in the next section.

One thing to note here is that even though there are over 15 times as many images results for ‘happy’ as there are for ‘sad’, the difference between average DLs for the top 10 images is only 3x. While ‘happy’ clearly drives more downloads than the other emotions, if you incorporate them into your shoots, there’s an opportunity to capture incremental downloads.

The results for ‘sad’ were skewed by two images that had an extremely high number of views. When I removed those two data points, the average number of views for sad was: 1,900 which is more in line with the other results. Those two images were in numerous collections and my guess is they were featured on the home page or as editors’ picks at some point.

Average Conversion Rate

For this post, I defined conversion rate at the ratio of Downloads to Views. To some degree, this metric is a measure of quality/keyword relevance. If something is viewed a lot, but not downloaded that much, it either means that it is a low quality image (people viewed it but decided not to buy it) or it could mean that the image is not relevant to a high volume search keyword.

Again, the results for ‘sad’ are skewed by the two high view images. If I remove them, the average conversion for ‘sad’ rises to 9.6%. It’s interesting to look at the PicNiche data for the same search terms. PicNiche rocks in my opinion and I’m looking forward to seeing how it evolves. I think they are using Fotolia data and have access to the total downloads and views for a given term as opposed to the sample approach that I took.

If you superimpose Average Downloads per Image (Left axis) and Average Conversion Rate for the search term (Right axis) you can see that even though all terms apart ‘happy’ have similar download numbers, ‘bored’ and ‘scared’ have significantly higher conversion rates than the others. There are also far fewer of these images than in the other two cases. Clearly more analysis is required, but the hypothesis that high conversion & low image count = opportunity is probably worth exploring further.

Per Image Detail for the Top 10 Images (For the Data Geeks out there…)

Perhaps the most interesting observation here is that there isn’t a ton of variation between the top ranked image and the 10th ranked image in terms of download counts. Another thing that jumped out was how close to each other the download counts for the searches other than ‘happy’ were.

Microstock Metrics – Direct & Hidden Costs

Posted on November 13th, 2008 in Uncategorized | 1 Comment »

When thinking about costs, it’s important to think about all the factors involved. There are the direct, measurable dollar outlays associated with a shoot (location, model fees, travel time, processing time etc), but there are also many indirect costs (rent, equipment depreciation & rental, etc) that need to be considered. Finally, you need to account for factors like rejection rate. When you take into account all of these factors, you arrive at a fully loaded cost for that particular shoot.

A useful way to look at this is to take the number of selects for a shoot and come up with a cost per selected image. Naturally, quality & variety are important drivers here. If you shoot crappy pictures or don’t create enough unique variants, then you’ll have fewer unique, usable images from a particular shoot and as a result your cost per image (that you can use) will go up.

When You Reject Your Own Images, You Save Money

Typical workflow for most microstockers looks something like the following:

  • Plan the Shoot
  • Shoot
  • Review
  • Retouch
  • Add Meta Data
  • Upload & Submit

Each step of the workflow increases the cost of an image. As a result, ruthless editing is always worth it. The earlier in your workflow you reject an image, the more money you save. Every time you touch an image, you add to its cost. The cheapest image is one you didn’t shoot in the first place. After that, when you review it, retouch it, keyword it and finally upload it, you invest time and resources. There is direct cost but there is also opportunity cost. You invest time that might have been spent on other images. As you hear over and over again, get it right in the camera. That’s the cheapest point in the chain.

When Other People Reject Your Images, You Lose Money

It can be counter-intuitive, but the more images you reject yourself, the better off you are. Being rejected by a microstock site at the end of your workflow plays the most havoc with your costs. The most expensive image is one that you invest everything in only to have it rejected.

If your images are rejected 50% of the time, then your cost per image that you can actually sell doubles. If you spent $500 on a shoot that yielded 100 selects and 50 of them were rejected, then your cost per photo went from $5 to $10.

While there will always be some rejections that don’t make sense, in the early stages of your microstock career there may be legitimate, technical reasons why your images are being rejected. When we started, our rejection rates were horrible because we had to learn about the microstock standards for noise, lighting, composition, sharpness, etc. Fortunately, supportive reviewers and helpful feedback from the community helped us figure out what we needed to do to meet the technical requirements for microstock.

Master the technical issues. It takes effort, but it’s worth it. Ultimately, you want to minimize rejections. Getting an image approved is no guarantee of success, but being rejected at the final stage of the pipeline really sucks. The least you can do is manage the factors that you can control.

Efficiency & Profitability

Ultimately, managing the cost equation in microstock is all about efficiency. Minimizing waste and time in your productions is how you keep your costs low. Good planning, strong technical execution and ruthless editing are the key tactics you can employ. Ultimately you want to find a way to minimize the cost of each accepted image. (Note: don’t be penny-wise, pound foolish – make sure you invest enough to meet the minimum quality thresholds.)

Low costs aren’t the full story though. Profitability is about the spread between cost and revenue and the ideal situation is when you can produce images with high earning power at low cost. (I know – I’m incredibly perceptive.)

I’ll be talking more about the revenue side, analytics and how LookStat can help in the coming weeks. In the meantime, happy shooting.