LookStat – Microstock Analytics

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!

One of the factors I looked at when analyzing 60 best selling images for ‘active seniors’ was whether the subjects were looking at the camera, or elsewhere (e.g. at each other, out at the ocean etc.)

I was pretty blown away by this result – 72% more downloads per image when subjects were looking at the camera? The average for the set was 777.1 so the ‘looking at camera’ group performed well above the mean. This was also true for conversion rate (downloads/views) – Looking at Camera: 11.16% conversion; Looking Elsewhere – 9.24% conversion.

The Eyes Have It

According to this eye tracking study, viewers look at people’s eyes and they look where people are looking. As a result, I’m fairly sure that images of people looking at the camera are more engaging in search results and lead to higher click through and ultimately more downloads. This may also work in ads whereby people find eyes more engaging and arresting and therefore stop to look.

A Potential Problem

As you can see from the screen shot above (which is from the above-linked study), people look where the models are looking. If the model is looking at your text, more people read your text. As a result, the problem is the following:

• Model Looking at Camera -> Arresting ad, people look at it BUT they don’t read about your brand.
• Model Looking Sideways -> Less arresting ad BUT those who look will likely read your ad copy.

We may be caught in a little trap where buyers purchase images that engage them, produce ads that grab attention but make the customer look at the model, not at the product being advertised.

Conclusions

• Looking at the Camera images had more downloads per image and better conversion than images where people were looking elsewhere.

• It’s possible that even though these images are more engaging, they may lead to less effective ads.

• I think this is only something that can be answered by A/B testing, but I’d love to hear what you think. Also, if you create collections to explore this in your own data, I’d love to know what you find. I’ll be looking at this factor as I analyze more categories for future posts.

• Microstock photographers should shoot both.

• Maybe the ideal ad has two people – one looking at the camera to engage the viewer and another to look at the copy to get the viewer to read it   What do you think?

About This Post

This post is the first in a two part series analyzing the top 20 best-selling images for ‘active seniors’ from iStock, Dreamstime & Fotolia. There’s too much data for one post hence my decision to split them up into a series. (By way of information, the image above is the best-selling image for ‘active seniors’ on iStock and has over 4,300 downloads.)

Part 1 will focus on the number of people in the images, ethnicity & common themes. Part 2 will focus on where they are, what they are doing and cross segmentation.

PS: If you want the spreadsheet with the raw data (which contains image IDs, sites, views data, pivot tables and charts for your own use), please tweet about this post or share it on Facebook and I’ll email you a download link.

Methodology

I collected data for the Top 20 most downloaded images from iStock, Dreamstime & Fotolia and categorized them by theme to see which factors were shared across the best-sellers. I’m not a photo editor, so this is not an aesthetic assessment; it’s an analytical look at trends across the best-selling images. I scored the images in the following ways:

• Number of men, women & children
• Ethnicity of Models
• Theme – e.g. couples, grandparents, friendship
• Location & Setting – indoors vs. outdoors etc
• Activities
• Where People are Looking
• Views & Downloads

Note, that in this post, I’m highlighting correlation, but not causation. This is an exploration of the data associated with the Top 20 images from 3 sites. Also, given the small image counts in certain categories, outliers may have a disproportionate impact on the data.

The above caveats aside, I think it’s very instructive to look at your images in this way. You can do this on LookStat by using our ‘Collections’ feature. I’ll have a post on how to do this soon.

Number of People

The chart above is sorted by downloads per image based on the number of people in the image. As you can see, two people shots dominate both in terms of total views & downloads as well as in terms of downloads per image.

Digging further in to the 45 images with two people in them, 82% were of couples. (The others were 2 friends & a grandparent & grandchild.) The downloads per image for these subgroups were: Couples – 781.3, 2 Friends – 877.8, Grandparent & grandchild – 1079.3.

Ethnicity

As you can see from the charts above, while there are significantly more downloads associated with images of Caucasian models, the downloads per image data for both African American & shots of groups is significantly higher than the mean for the set which was 777.1 downloads per image. One of the challenges here is that analyzing all-time data in this way doesn’t give you a sense for velocity. I’m a little surprised that there isn’t more diversity in terms of ethnic backgrounds in the best-selling images.

Common Themes

Points to note:

• Couples refer to a man and woman and they are typically hugging or holding hands. (more on this in Part 2)
• Friendship images were typically those involving 2 women. I was surprised that there were no images of 2 men in a photo together.

Conclusions

• Two is the most common number of people in best-selling images for active seniors. Of those images, 80+% are of couples, but the images with the highest Downloads per Image are those of a grandparent with a grandchild. (Couples – 781.3, 2 Friends – 877.8, Grandparent & grandchild – 1079.3)
• There were no images of two men together engaged in any activity in the top 60 best sellers; the 5 images of friendship were typically of two female friends.
• 92% of the images in the top 60 were of ‘Caucasian’ models only. While the numbers are small, the downloads per image data suggest that there is an opportunity to explore ‘active senior’ themes with more ethnic diversity.

Analyzing this at the site level is great, but one thing you can do with LookStat is to set up collections to analyze all this. Since images can live in more than one collection, setting up overlapping segments is straightforward and you can then start to compare your data with bestsellers. I’ll post more on how to do this next week. Some of the features in our new beta really take this sort of thing to another level and I’m excited to share it with you all.

Feedback Request

Please comment and let me know if you think this sort of post is helpful & valuable to you. Also, if you have thoughts on whether downloads per image is a relevant proxy for opportunity, I’d be curious to hear about that as well.

One of the core features of LookStat is automated image-level stats. When you create a LookStat account, our system builds a complete picture of your sales history for each image. Once this is complete, you can group images into collections and then start tracking performance of things like shoots, concepts, models etc.

Image Detail Screenshot

The screenshot above shows the earnings by week of the image above for last year. Clearly, this is a seasonal image for fall/thanksgiving and you can see how this plays out in the earnings chart. The image starts really selling in July (week 32) and then peaks in October (week 42) and drops down from there.

The lines on the chart are:

• Earnings – Weekly earnings of the image. This is the default time period when viewing a year, but you can also choose to view the data by month or by day.

• Downloads – Weekly downloads of the image. As stated above.

• T30 Earnings - This is the red line in the chart above and is the trailing 30 day moving average of the image’s earnings. This shows the overall trend line and is especially useful when viewing daily or weekly sales.

• Total Earnings – This is the cumulative earnings of the image over it’s entire lifetime. This should always be increasing. As you can see from the chart above, when the image isn’t selling, this line is flat.

• $/DL – This is the weekly earnings divided by the weekly downloads for the image. This line gives you a sense for the image sizes being downloaded. You can see above that the $/DL line peaks before the sales peak which is in line with the idea that print buyers use larger sizes

Transaction Register

In addition to the image charts, there is a transaction register that lists all transactions associated with that image. You can drill down by month and then by day to look at individual transactions.

Sign Up for the Beta

We’re expanding our beta program, so please contact us and we’ll get you activated. We’re really excited about the new functionality and we’d love to know what you think of it.

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 getting some great feedback from our initial set of beta users and we have been making improvements.

Some recent changes:

• Addition of monthly RPI. This is calculated by taking the earnings for a month, divided by the image count at the end of the month and then averaging over the time period being viewed. I’d love some feedback from you on if this approach lines up with what you would expect.

• Addition of all-time Sell Through Rate. Our system already shows the percentage of images that sell each day on average. We are also going to add in all-time Sell Through Rate so you can see how things are looking overall. This is currently a placeholder, but we’ll have it in there soon. This is currently live. The number depicted is the sell-through rate for the period you are viewing.

• Sortable columns. All column headings can now be used to sort lists. So, for example, if you want to sort your collections by RPI, just click.

All these metrics are also available by collection so you can compare your shoots.

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.

Two very interesting charts from www.marketingcharts.com, a site that provides easily digestible snapshots on media and advertising. Both of the charts below focus on display advertising i.e. some form of banner ad. This is where you’re most likely to see microstock & stock photography in action.

Top Online Advertisers by Category – Feb 2010

It’s quite clear that financial services at, 22% of total, is the leader of the pack in terms of ad impressions. This also lines up with business imagery being one of the top selling categories in microstock in general.

Top Financial Services Advertisers – Feb 2010

This too is a fascinating list. Online trading, credit scores, insurance, taxes, retirement – a rich array of conceptual categories worth exploring in your finance shoots if you’re not doing it already. Tax searches are highly seasonal as you would expect, with a steady increase in search volume from Jan-March followed by a dramatic spike during the week of April 15.

Food for Thought – Seasonality

I often think of seasonal events as being more related to holidays and greeting cards, but as the tax example shows, there are often seasonal cycles in other aspects as well. This isn’t something I’d explicitly considered when thinking about categories like business or medical shoots, but it does make sense.