LookStat – Official Blog

LookStat Analytics allows you to ask and answer questions about your microstock performance that are almost impossible to answer any other way. For example:

• Do my business images outperform my travel images?
• Which Image formats sell the best?
• Do this model do better in business or lifestyle shoots?
• Which of my last three shoots is doing the best?

To answer any of these questions by hand, you could construct an enormous spreadsheet and enter thousands of data points, or you could create a LookStat account and do the same thing with a few clicks.

Analyzing Medical vs. Business Images

Let’s consider a scenario in which you wanted to study the sales of all your medical images and business images for the past 2 years to see what you should shoot next. There are two ways to perform this analysis. The hard way using spreadsheets, and the easy way – with LookStat.

The Hard Way – Spreadsheets

To plot and analyze two years  of data for 50 medical images and 50 business images to see which set had a better revenue per image (RPI), you would have to enter up to 73,000 data points to understand what was happening. (365 days x 2 years x 100 images). Even if you wanted to do the analysis for just 10 days, you’d have 1,000 items to record. Unless you really, really love data entry, odds are you won’t bother with this and will just eyeball things and make your best overall guess.

The Easy Way – LookStat Analytics & Collections

LookStat Analytics automatically tracks every transaction for all of your images. As a result, you can do this sort of analysis using LookStat with less than 10 clicks. The steps to do this are listed below:

1. Create a Collection

2. Search for Medical Images & Add All Results

3. Repeat the above 2 steps for your ‘Business’ images and you’re done.

As soon as the system has updated your collection stats, you’ll be able to plot and compare the performance of the two sets.

Collection Summary – Medical Images (All Time)

• 41 images
• $170.87 total earnings; 192 total downloads
• $0.16 Monthly RPI

Collection Summary – Business Images

• 205 images
• $423.61 total earnings, 511 total downloads
• $0.08 Monthly RPI

Interpreting the Results

• The portfolio above has 5 times as many business images as medical ones (205 vs. 41).
• Medical images have twice the monthly RPI of the business images. ($0.18 vs. $0.06)
• When planning future shoots, adding more medical images should be a priority over business images
  • Fewer medical shots in portfolio
  • Higher RPI than business  in this case

Conclusion

• Detailed sales analysis is virtually impossible to do by hand if you are trying to analyze performance at the image or shoot level.
• If you’re not doing this sort of analysis on your portfolios, you’re missing out on actionable data that can help you boost your earnings and improve your return on investment in the future.
• Sign up for a LookStat Account and start gaining insight today!

PS: If you do this analysis for yourself, I’d love to hear about your results if you’re willing to share them!

Performance ratios such as Sell Through Rate, RPI and earnings per download can provide valuable insights into what is happening with your portfolio. One of the new functions enabled by LookStat’s upgraded stats service is the ability to compare trends over different time periods.

To illustrate this, we looked at Summer 2009 vs. Summer 2008 for the portfolio below. In this case, we used June 1 – Sep 30 as the definition of ‘Summer’.

The best way to currently compare time periods is to log in to your LookStat account, open it in 2 tabs and then use the date control in the header bar to enter the date range you’re focused on.

Summer 2008

The above is a screenshot of a daily plot of sales at iStock from June 1 – September 30, 2008. You can see the slow uploading of images (28 at the start of the period to 88 at the end of summer) and you can also see the summary metrics for the time period in question. The other thing you can see is the three best-performing images over that time period.

Summer 2009

The above screenshot is the same period as the prior chart in 2009. You can see that no images have been uploaded to the account for a year. 2 of the top 3 images are the same. The second one, the pumpkin shot, didn’t start selling until the Fall of 2008 and so didn’t break the top 3 in 2008. (As an aside, sales for the pumpkin shot actually climbed over time.)

Performance Metrics

The summary bar below the chart contains core metrics and ratios that give you a snapshot view of overall performance during the period in question. I compared the metrics for Summer 2008 & Summer 2009 in excel and you can see them side by side below.

In this case, increased pricing ($/DL) and slightly improved sell-through rate couldn’t compensate for a 40% drop in RPI. The biggest factor behind the declining performance of this portfolio is lack of uploading. No new images were added over the course of the year.

Analyzing your Own Stats

The example above is for a small, inactive portfolio and doesn’t reflect the market as a whole. I was actually surprised that even with no activity for a year, sales declined by less than 30%. I expect that most of you seeing success with microstock will find that all your key metrics are green.

Ideally, you should be increasing the overall performance ratios of your images. So, for example, if you are adding images but seeing a decline in sell-through-rate, it could mean that you are not shooting enough saleable images, or you are uploading too many similars together. Analyzing your key metrics over time can help you identify opportunities to increase earnings.

The ultimate goal is to combine shooting what you love, with shooting what makes you the most money. We want to make it easy for you to do that.

There are a number of new metrics in the LookStat Analytics upgrade and I wanted to define them and point out how they can help you identify areas of opportunity in your portfolios. When you combine these with LookStat Collections, you can really start to get in and pull things apart to discover insights that will save you money and help you plan more profitable shoots. More on this coming soon. First, I wanted to define some of the metrics that you’ll see.

Revenue Per Image (RPI)

Why it Matters:

Allows you to calculate the earning power of an individual image or group of images. When you combine this with collections, allows you to compare shoots and concepts in an apples to apples fashion. For example, if you have a business shoot & a lifestyle shoot with slightly different image counts, the RPI of the shoot allows you to see which of them earns more per image and you can then use this information to compute your return on investment for the shoot.

How We Calculate It:

This is calculated on a daily and a monthly basis. The monthly calculation is Earnings for the Month/Images Online at the end of the month. This is then averaged over the timeframe being viewed. So, if you are viewing ‘This Year’ than the average monthly RPI refers to your monthly RPI average for 2010.

The daily RPI number is calculated as follows: Day’s earnings/Images Online that day. This too is averaged over the timeframe being viewed. Our view is that the daily number is the most accurate because it takes into account any uploading variations during the month. For example, if you uploading 1 image per day for 29 days and then uploading 500 on the 30th day, the montly RPI would skew very low which the average daily number would be more accurate. We are looking for feedback on this so I’d love to get your thoughts.

Sell Through Rate (STR)

Why It Matters:

Sell through rate allows you to see the overall commercial viability of your portfolio or your collection. If you have 100% sell through rate, that means every single one of your images has had at least one sale. This means that you didn’t shoot, retouch, keyword, upload & submit any images that no one bought. This is a good thing. If you find that certain shoots in your portfolio have low sell through rates, you are investing time and money in images that don’t interest buyers. Naturally, sell through rate is time dependent – for example, if you upload a 100 image shoot, it may take some time before all of those images see the light of day. Tracking STR after 90 days across different shoots for example is a good way to compare how they are doing relative to each other.

How We Calculate It:

We compute a daily STR (number of unique images that sold/images online that day) and a ‘Period STR’ which is the same calculation for the time period that you are viewing. Ideally, both numbers trend towards 100%. It’s normal for your daily value to be much lower than when you view All-time STR, for example. We’ll be working on some benchmarking data so you can start to see how

Trailing 30 Day Moving Average (T30)

Why It Matters:

This is the monthly trend and it smooths out the daily and weekly fluctuations associated with the normal weekly cycle of sales. When you overlay this on a chart showing weekly earnings, for example, you can see the general trend and how periodic peaks and valleys are affecting it. In the chart below, the orange line is the T30 earnings while the red line is the weekly earnings.

How We Calculate It:

As the name suggests, it is a moving average of the last 30 days. Our system also calculates the moving average of the last 7 days but we don’t currently display this. We felt the T30 line showed a better trend but we’re open to changing this. Please let us know what you think.

Getting Access

We’re expanding our beta program steadily and we’d love to have you try it. Please contact us for access. I’d love your feedback!

As part of our upgrade to our stats service, we are adding in a large number of new metrics to give you a better understanding of how your portfolio is performing. Over the next few days, I’ll be walking through the major changes to the site and I’d love to hear back from you about what you think and what we need to do to improve the system and fill in holes.

Dashboard Overview
The metrics you’ll see on the new dashboard are shown below:

All the metrics you see above are dependent on the time-frame you are viewing. The sample data above is an all-time view show is shown by month.

• Earnings, Downloads & $/DL – Earnings & downloads history for all your images. This is automatically initialized when you create a LookStat account and will then be updated once/day.

• Revenue Per Image (RPI) – this is calculated on a daily & a monthly basis and provides a way to normalize performance of groups of images. The numbers are automatically computed based on your chosen time window. The daily calculation looks at each day’s earnings & number of images online. The monthly calculation is similar and uses monthly earnings divided by images online at the end of the month.

• Sell Through Rate (STR) – this is calculated as a daily average and for the time period you are viewing on the site. It shows you the percentage of images that had at least one sale in the time frame you are viewing. Ideally, you want these numbers as high as possible since it shows that most of your images are selling often.

• Images Added – This tells you the number of images added to your portfolio during the time period you are viewing. When looking at an All-Time view, you’ll be able to see how many total images were accepted into your portfolio.

Collections Overview
In addition to a new overview block, there is a new Collections Summary on the dashboard. This is shown below:

This block lists the Top 5 Collections in order of earnings over the time-frame you are viewing. In addition, it provides summary data on each collection so you can tell at a glance how each is performing. In the example above, you can see that even though the ‘Couples’ collection is ranked #3 in terms of Earnings, it has the highest RPI shown. By creating collections for shoots and different concepts e.g. isolated vs. non-isolated images, you can start to get a very granular look at what’s working and what isn’t.

Earnings Overview

The Earnings Overview section shows the top 5 images ranked by earnings over the time period you are looking at. The summary shows the date the image went online (if available) and doesn’t show RPI since that is meaningless when looking at a single image. There is also a Sites Overview that lists a summary of earnings & RPI by site.

Request for Feedback

I’d love to know what you think of the new dashboard and also to see if there are metrics we should include that would be helpful to you. I’ll be writing about each of the major site sections over the next few days. If you are interested in a test-drive, please let me know.

We’ve been working hard to significantly upgrade our analytics product and we’re getting very close to releasing it to our users. The system is about to enter a limited beta so we can get feedback on what’s working and what isn’t.

We are really excited about this upcoming upgrade and the potential it will have to help our users figure out which shoots are working and where to spend their time. There are many new changes and metrics available and I’ll be talking about them all over the next few days and weeks. Some highlights:

• True Daily RPI calculations
• Earnings, Downloads, Images online
• Sell-through rate
• Collection RPIs – compare your shoots apples to apples!
• lots more to come!

It has been too long since we’ve updated our stats product and this has changed. I hope you’re going to be excited about what you see and I’m confident the data will give you an edge that will help you sell more microstock!

As I mentioned, we will be reaching out to a small group of users and will be running a very tightly controlled beta. If you’re interested in trying out the service, please contact us and we’ll add you to our list!

To the many users that have trusted us so far, thanks for your support and please help us spread the word.

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.