LookStat – Official Blog

We just released a new feature which allows you to view the entire sales history for an image over it’s entire lifespan. You can drill down from all time to a single day and can filter transactions by site. To access the feature, just click on a thumbnail in your dashboard. As always, feedback is welcome and appreciated.

We are working on creating a page that allows you to see all sales for a particular time period and that should be available shortly.

Over the past few weeks, I’ve been speaking with a range of micro and macro photographers and one idea that keeps coming up is the notion of Earnings by Shoot and time to break even. The goal here is to track the performance of all images from a particular shoot over time and record the total costs of the shoot to get an understanding of profitability and also time to break-even – how quickly does a shoot pay for itself.

One of the challenges here (in addition to tracking) is interpretation. For example, if Shoot A has earned $10k & Shoot B has earned $5k you might think that A is better unless Shoot A earned it’s sales over 5 years and Shoot B has earned it over 2 years. Alternatively, if time to break-even for A was 2 months and B was 2 months, they might seem equivalent unless A cost more than B in which case, you might have to adjust your assessment. (my apologies if I sound like Captain Obvious)

One mistake that is easy to make (and I’m guilty of this on occasion too) is not putting a dollar amount on your time when thinking about expenses. Time is extremely valuable and the less time you waste, the more you can spend on activities that generate value. Experienced shooters understand this and that’s why they talk about things like shot lists, planning, careful prop selection etc. Their goal is to waste as little time as possible and spend as little time as possible retouching etc.

When tracking the cost of a shoot for your break-even analysis, it’s important to track total cost. This includes things like editing time, retouching & keywording time and also uploading time. In general, anything you do to the images has a cost whether or not you spent cash on it.

The quicker you can go from camera to sites, the quicker a shoot can start paying for itself.

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.

Our vision with LookStat was to create a web platform for microstock contributors that not only tracked sales but made it easy for contributors to really analyze their performance and ultimately use that information to make their microstock activities more profitable.

Over the next few weeks, I’ll be writing about ways to think about your microstock activities as a business and various metrics that are interesting in that regard. This post is focused on Return per Image (RPI).

Acknowledgements

James, Laurent, Lee, Matt & Yuri all have great posts about RPI. I would urge all of you interested in this topic to check them out. One of the things I love about microstock is how willing the community is to share their knowledge.

Return per Image per Month

One of the more commonly quoted metrics in the microstock blogosphere is RPI. This is not revenue per download, but rather it gives you an indicator of how much each image (across all sites) will generate in revenue for you every month. This number is important because it measures the earning power of your images.

While it’s a useful top-level indicator, it does have some limitations. The most glaring one is that it doesn’t really convey what happens over time. Matt has written a great post about this and the core issue is that older images sell less and RPI at the aggregate level obscures this since it only looks at the aggregate portfolio and doesn’t account for age.

Calculating RPI:

Consider the following two scenarios:

Scenario 1:

You have 10 images in your portfolio and they are listed at 5 sites. You earn $100 in sales at each site every month. In this situation, your return per image is:

Scenario 2:

In this scenario, everything is the same as in Scenario 1, except you have 100 images at each site. In this case, your revenue per image would be:

As you can see, the images in Scenario 1 have 10 times the earning power of the images in Scenario 2. Please, keep in mind, these are made up numbers. RPI varies quite a bit and the numbers above are very high based on what I’ve seen published. In general, based on conversations I’ve had, if you’re at $1-$2 RPI/month across all sites you’re in pretty good shape.

Limitations of RPI

RPI is a useful number but as such doesn’t give you enough information on how to proceed. Matt’s thought about selecting the top 100 images in a month and looking at their RPI is a good one. Another option is to look at age. Compare how your latest uploads perform in their first month with how your last batch did in their first month, i.e. cohort analysis. Each image has a revenue curve that varies over time and looking at batches is a way to better compare apples to apples. Laurent has a post discussing this that is well worth your time. In his post about Advanced Stock Theory, Yuri also discusses some of the factors that skew RPI.

Analyzing Sales over Time

The chart below was generated from LookStat data and shows the total revenue curve of a few of our images across multiple sites. Each curve tells a different story. A constant slope means steady sales over time. A flattening curve means declining sales over time etc. RPI at the aggregate level obscures this data. Tracking RPI at the same point in an image’s life cycle would yield more interesting data. For example, looking at RPI for an image for the first 30 days that it is listed would be fairly instructive.

(the data above is total sales hence the curves that rise and flatten)

Other Useful Slices

Another useful way to use RPI is to group images by category, by location, by model, by site and to see if certain groups stand out relative to others. By segmenting your revenues in this way, it’s possible to tease out some of the meaning that’s obscured by the aggregate RPI number. For example, you might find that within the business category, your RPI for isolated images is much higher than your RPI for images shot on location. Again, it’s important to compare apples to apples so looking at RPI at similar stages in an image’s lifecyle is important.

How LookStat can Help

The main problem with increasing the granularity with which you look at RPI is that it’s a giant pain to calculate. If you’re doing it by hand, you need to track sales by image and by day and then you have a dataset that you can slice and dice. This is fine with a handful of images, but it quickly becomes unwieldy. Unfortunately, this analysis is most valuable when you have a larger portfolio spread across multiple sites.

LookStat tracks sales at the individual image level and we aggregate data across sites. As a result, it’s easy for us to get at the details of the data. Basically, for every image in your portfolio, we know when it was uploaded and how many sales it had each day. We also store all the metadata associated with an image so in the future, we will be able to use that information to analyze sales even further.

Ultimately, we want our users to be able to look at their portfolios and see which models in their business images generate the most revenue for them at Dreamstime vs. Shutterstock.

Ultimately, we believe that better data for contributors means they’ll have a better sense for what buyers want which in turn will lead to them producing better-selling images. The beauty of microstock is that this should benefit all participants in the chain.

A Request

Please let me know which metrics you like to track and what you’d like to be able to track if you had access to the relevant data. Hopefully we can start a thread that would be valuable to the community and I’d love to be able to build in some of the most useful pieces of data into LookStat as the service evolves. Because of the granular way in which we build transaction histories, creating new data slices is relatively easy.