LookStat – Official Blog

One of the most powerful ways to use LookStat is to answer questions about your sales stats. With LookStat, you can use collections to analyze segments of your portfolio and then derive a rating to compare them.

For example, in this case, I was curious about whether the ethnicity of models in shots of couples had any impact on sales and performance.

I’ll walk through the analysis with a sample account so you can do the same for your stats.

Question:
Does the ethnicity of the models in couple shots have an impact on sales performance?

Answer:
Create two LookStat collections and then compare the performance ratios:

1. Log in to your LookStat account.
2. Click ‘Create Collection’ in the left nav bar.
3. Create two collections: one with the search ‘couple, caucasian’ and the other using ‘couple, african american’ – in each case, adding all images to the collection.
4. The system will update the collections within an hour and you can then dig into the analysis.

(For a tutorial with screenshots, check out our recent post – “How Big is Your Spreadsheet?“)

Interpreting Results
Screenshots of the collections results are shown below (you can click through on each image to see a full-size version.) The screenshots show the earnings & uploading trend as well as key performance metrics.

African American Couples

Caucasian Couples

Comparing Collections:
Given the disparate data above, it can be hard to compare the collections. However, as you can see below, even though all the metrics vary significantly, you can use the performance ratios in your LookStat console to come up with a collection rating.

This clearly shows that even though Earnings per Download are lower for the collection of African American couples, it’s a stronger performing collection once you account for Sell Through Rate and RPI. (Since bigger is better for each of the metrics, a multiplicative combination works well.)

The Collection Rating lets you compare collections even though all the individual components are different. The best part of this is that the components of that number are computed for you automatically.

LookStat Makes It Easy
LookStat makes it very easy for you to do this kind of analysis on your shoots and for your overall portfolio. You can see which shoots or concepts perform better or worse than average by setting up a few collections and letting the system crunch the numbers. While you could theoretically do this by hand, the amount of data you’d have to gather and analyze would be a nightmare.

Sign up for a LookStat account and let me know what you think!

PS: The great thing about the Collection Rating is that you don’t have to publish any earnings figures. LookStat automatically calculates the ratios for you. I’d love to hear your thoughts on this and I hope you’ll share the results of your analysis.

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.

I decided to analyze the orientation of the Top 60 images from the Active Seniors searches that I’ve been writing about and the results of that analysis are in the table below:

Key Points

• Horizontal images are the most numerous & best performing, on all metrics – conversion (dls/views), views per image and downloads per image.
• No square images in the top-sellers, in spite of the notion that square thumbnails do better.
• You are leaving money on the table if you don’t shoot horizontal and vertical formats

This isn’t really all that surprising. When we last analyzed the impact of shooting horizontal vs. vertical vs. square across millions of transactions, we came to the same conclusion – Revenue per image for Horizontal shots was twice that of other formats. Our reasoning was that as usage shifted online & microstock is purchased for online uses, horizontal image formats work best.

Are you using LookStat collections to see these trends in your sales? What are you seeing?

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!

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.

Last week I wrote about total earnings and downloads and the trends associated with pricing, downloads and earnings. In this post, I looked at the same data and adjusted for active users in that year to get an average per user for the year in question. The results are shown below (the earnings per download line is carried over from last week.)

User-adjusted Earnings and Downloads 2002-2009

Key Takeaways

• Declining downloads per user after 2007
• Slowing growth in earnings per user
• Increasing earnings per download

Although earnings per user have continued to rise, the downloads per user peaked in 2007 and have since declined about 20% from 2007 to 2009. These data support some of the things I heard at PDN and UGCX about flat downloads and increased earnings driven by price increases.

Increasing Competition & Higher Standards

As more contributors enter microstock, especially in a down economy when people are looking for other sources of income, there has been an increasing sense  that it is harder than ever to make money in microstock. If you layer on tougher acceptance standards, you can make a case that new contributors will have a tougher time establishing themselves in the market. This then suggests lower downloads and lower earnings per contributor

Competition isn’t the Whole Story

Increased competition and tougher standards only account for part of what is happening. I think there is no question that new contributors and images are entering the market at increasing rates. If the growth of new users exceeds the growth of the overall downloads, then we’ll see a decline in the per user averages. If increasing competition was the only factor however, we would also expect to see a decline in earnings per user. Clearly, this is not the case. Also, one thing that isn’t clear here is if competition is actually hurting established players or whether new entrants are just struggling without creating an impact on existing users. (A cohort analysis could help illuminate this but that is a post for another day.)

On Average, Users Are Earning More

Price increases by the agencies and increased pricing of individual images as they begin selling more are factors driving up user earnings. As contributors gain experience and their images sell, they benefit from increased pricing for their images as well as better placement in search results. There is a little survivor bias at work – you only stick around if you’re seeing success. It is interesting that this effect is more than compensating for the reduction due to competition, market factors etc.

Conclusions

It is harder to break in to microstock and succeed but there is no question that the market has grown overall, through difficult times. While there are many new entrants and standards are rising, increasing earnings per user suggest there is still opportunity in the market.

I’d love to hear people’s thoughts and interpretation in the comments.

The conventional wisdom is that photographers should shoot both horizontal and vertical formats and that square images do better because they stand out in search results (see screenshot below.)

The reasoning here is that square images take up more pixels in a thumbnail grid and therefore stand out. While this may be true, it’s instructive to look at the volume and earnings per image data to see what this really means.

Relative Image Volume in 2008

As you can see, photographers are getting good at rotating the camera while shooting. There are equal numbers of horizontal and vertical images being submitted. Square images lag behind, but that’s to be expected since it takes a little extra effort on the part of the photographer to frame and crop the shot.

Royalties Per Image in 2008

The sales data tell a very clear story – horizontal images earned 2x per image what the other formats did. While square images may take up more pixel area on a row of thumbnail results, they are not driving revenue to the same degree.

As food for thought, it’s interesting to note that all computer screens are in horizontal format and given the rise of online vs. print advertising, maybe these data aren’t that surprising after all. Also, given that most of us are pressed for time, microstock buyers may just not be willing to invest the time needed to crop a square image to their liking.

The bottom line, I still think it makes sense to give buyers a choice but you have to pay attention to the numbers to really see what’s going on.

LookStat collections makes it easy to do this sort of analysis on your microstock sales stats.

We did an analysis of  2008 data for images with the keyword ‘cat’ vs. ‘dog’ to see how they did overall. It doesn’t matter what metric you look at, it’s not even close – dogs rule!

Image Volume

There were twice as many images tagged with the keyword ‘dog’ as opposed to ‘cat’ in our system. This is interesting when you consider that according to the American Vetinerary Medical Association, cats outnumbered dogs in the USA in 2007 (81 million cats vs. 72 million dogs.)

Royalties per Image

In addition to there being more images of dogs, they also earned more per image and were downloaded more frequently. Royalties earned per image for dog images were twice those earned by cat images.

Images of dogs also had 30% more downloads per image than those of cats.

Total Earnings

This translates to dogs earning over 4x more than cats in 2008.

I’m confident that dog-lovers everywhere knew that their beloved pets would come out ahead, but it’s nice to see it in the data. Let me know your thoughts and whether you see similar things in your portfolios. Also, if you’re interested in specific keywords, let us know in the comments and we’ll see what we can do.

(Full Disclosure: I am a dog owner, but since Casey has a cat at home, we are a pet-neutral company.)

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.