Owners take their e-readers to bed, but keep their iPads in the living room, according to a Nielsen study of 12,000 smart device users.

• 70% of tablet owners, and 68% if smartphone owners use their devices while watching TV.
• 61% of e-reader owners use their devices in bed.

This makes sense: It’s easier to multi-task while surfing the web than it is while reading in bed.

Impact on Microstock

If you search iStock for lifestyle and technology, you’ll mostly see images of attractive people looking at laptops. I think there’s an opportunity to update the technology in these images. iPhones, iPads, and e-Readers are selling at a tremendous rate, but haven’t yet appeared in many microstock images.

Keep actual usage in mind when planning your shoots — imagery will follow consumers as they move beyond laptops.

After I was surprised by the low numbers of images for a range of ethnicities on iStock, I analyzed annual downloads (Total DLs/age of image) to see if the low result counts were justified.

Average Age of Image in BM vs. Download Sorts

• Given the high number of Agency images in Best Match, I wasn’t surprised by the low average age of BM results.

• Although Caucasian images have been downloaded the most often, the ratio between them and images from other ethnicities is closer than the spread between them based on total number of results.

Conclusions

• I think there’s an opportunity to shoot imagery of different ethnicities across the major stock categories.
• I would start with Hispanic and Asian imagery, based on the high growth rates of those segments in the USA.

I searched iStock for the Top 100 Best Match results for: African American, Asian, Caucasian, and Hispanic. I recorded the total number of results, and the composition of the Top 100 by Collection.

Total Number of Results:

• The above are the number of results that contain the keyword in question. So, if you looked images that only contained Hispanic models, the number would be lower.

Composition of Top 100 Best Match Results by Collection

Things That Stood Out:

• There were four times as many Caucasian images as there were of the other three ethnicities combined. I expected a disparity, just not one this pronounced. (I’m going to analyze the downloads for each of the searches to see if I find a similar trend.)
• The number of Agency & Vetta images in this dataset surprised me. 272 out of 400 (68%) of these results were in the most expensive collections.
• Top Three Agency Contributors by Number of Images: Blend Images(19), Albany Pictures(18), Rubberball(17)
• Top Three Vetta Contributors by Number of Images: Morganl(11), Uberstock(7), Bortonia(6)

For yesterday’s post on Summer images, I analyzed data on 1,000 images across ten summer-related searches. I was curious about the relationship between the age of an image and the number of downloads it had.

Total Downloads vs. Age of Image

• The images with the most lifetime downloads are not the oldest ones — rather they are between 3-5 years old.
• The correlation between age & downloads is very close to zero which suggest little to no correlation between the two.

Annual Downloads vs. Age of Image

• The distribution looks very similar here.

Conclusions:

• Just because an image is old, doesn’t mean it will get a lot of downloads.
• New images have come in that are downloaded at a rapid rate.
• Again, as we saw with contributors, age doesn’t appear to be the dominant factor.

I wanted to see which summer activities were downloaded the most on iStock. To do this, I searched for them on iStock using the keywords: “summer <activity>” and recorded the total number of results, and information on the top 100 images when ranked by downloads.

Total Result Counts

• The charts above show: the number of images returned when you search for “summer + keyword”, and the percentage of overall results when you search for “summer”, by itself.
• There are 680,019 images returned when you search for ‘summer’ by itself.
• The set above accounts for 330,660 images or 49% of the total summer images.

Average Downloads Per Image for the Top 100 Search Results

• The chart above shows the average number of downloads per image for the Top 100 images returned when sorting by downloads.

Items of Note:

• I was surprised to see each of the activities accounting for less than 3% of the total summer images in each case.
• Running stands out in the average DLs chart — I would have expected it’s DLs per image to be about half of what they actually were.

I analyzed the top 50 Best Match results when searching for ‘summer’ and broke out the results by collection:

Number of Images

• Exclusive collections account for 43/50 or 86% of the results.
• The total number of results for the search was: 676,858 (~8% of 8 million total)

Average Age of Image

• This the difference in years between the upload date, and the date of the analysis – May 3, 2011

Average Total Downloads

• This is the average lifetime downloads for the images in each collection.
• Given that Agency exceeds Vetta, it suggests that old images were moved into the Agency Collection.

Average Annual Downloads

• This is the total downloads divided by the age of the image broken out by collection.

Things of Note

• Exclusive+ images in this set were best-sellers that were moved to E+. This analysis doesn’t show what’s happening right now, so the impact of this move is something each person will have to analyze for themselves. (You can do this using LookStat.)
• Agency images in this result set are outperforming Vetta, in terms of downloads. Again, I find this hard to believe and it’s likely being skewed by older images. If it is true, then buyers are indeed price-insensitive, and this is good news.

I searched for ‘business’ on iStockphoto, and analyzed the top 100 results returned for Best Match & Downloads sorts. For each image, I recorded:

• Total Views & Downloads
• Upload Date
• Search Position

Using the data above, I was able to calculate the age of each image, and therefore, calculate the average annual downloads for each image. I used the median instead of the mean for averages, to prevent outliers from skewing the results too badly.

Median Age of Image

• This is the difference between the upload date of each image and the date of the analysis – April 30, 2011.

Median Value for Total Downloads

• This is the median value of the total downloads for each images in the Top 100 search results.

Median Value for Annual Downloads

• This is the Median Value of the Total Downloads divided by the age of each image.

Things That Stood Out to Me

• 46 of the 100 images were present in both Best Match and Downloads sorts.
• I expected a bigger spread in median age of image between Best Match and Downloads.
• I was also surprised by the parity between the median annual downloads in each case.

Over the next few days, I’ll analyze these searches in more detail, and compare them to other subject categories.

If there are topics that interest you, please let me know in the comments.

I studied the difference in counts and downloads between people vs. non-people images by running searches on iStock for: business, education, industrial, lifestyle, and medical images. I recorded and analyzed the number of images and also calculated the number of downloads for the Top 200 images.

Image Count

• 7.6% of the images in this sample had nobody in them. (Note: there may be some distortion here due to limited use of the keyword ‘nobody’. But, the ‘No People’ filter includes images of body parts, which I don’t want to count in this analysis).
• 16.4% of the images in the industry category had no people in them (2.3x average).
• I expected business and lifestyle to account for more than 10% of iStock’s eight million or so images, overall.

Average DLs/Image for the Top 200 Images in Each Search

• I obtained the average DLs per image by summing the downloads of the Top 200 images in each search and dividing by 200.
• Overall average DLs/image for the categories shown: 1,153 DLs/image.
• Overall average DLs/image for images with ‘nobody’:    588 DLs/image.

Some Caveats:

• This analysis only applies to the chosen categories. I think the performance of people vs. nobody will be different when looking at subjects like ‘Christmas’.
• I used ‘nobody’ instead of the ‘No People’ filter to exclude images of handshakes, or fingers pointing etc. (Also, the ‘No People’ filter doesn’t work.)
• The reason lifestyle & medical are at exactly 1000 DLs/image is because each of the Top 200 images are in the 1000+ download band.
• Data is cumulative.

“It is true that you can succeed best and quickest by helping others to succeed.” – Napoleon Hill

Although Napoleon Hill wasn’t talking about microstock, his advice remains relevant. If you focus on helping image buyers succeed, you’ll make more money in microstock.

One way you can do this is by studying how images are used in ads. A recently launched site, Moat.com, let’s you do just that. (Moat also intends to provide engagement analytics, but you’ll have to wait, and pay for that service.)

I searched for some major advertisers on Moat and found a range of ads that are worth examining.

From right to left, we have Netflix, Pfizer, Aetna, and Lifelock. Netflix is demonstrating their service, while the others are selling peace of mind.

Some Observations:

• Images are horizontal, and subjects are off-center in 3 out of 4 ads to leave room for copy.
• Models are looking directly at the camera in 2 out of 3 of the ‘peace of mind’ ads. Eye contact does suggest trust, so this makes sense.
• Netflix’s models are pointing at the ad, and the ad is directly above the ‘Click Here’ button. (This makes sense since we read from left to right in English.)
• Models in portrait style shots are looking at the camera, but their shoulders are at an angle. (I don’t know if this is significant, but I think it’s interesting.)

This isn’t a statistical analysis, but I think studying how buyers pair images with concepts will tune you in to their needs. In turn, this will help you plan your shoots and produce more salable images.

Other Examples:

To save you time, here are links to some of the brands I searched for:

• Healthcare: Aetna, Pfizer, United Healthcare.
• Financial: Citigroup, Lifelock.
• Travel: Princess Cruises, Travelocity, Westin Hotels.
• Lifestyle: Kohl’s, Netflix, Target.

NB: Product retailers use their own products, but I think it’s still instructive. Service providers seem to use more stock imagery.

The Hispanic and Asian population in the USA grew over 43% from 2000-2010. You can use LookStat collections to explore the impact of this growth on your microstock sales. For example, if you wanted to analyze Hispanic vs. Asian models in business and lifestyle shoots, you would proceed as follows:

1. Create a collection

2. Search for the following terms & add all results.

• HAVING KEYWORDS: business%, hispanic%
• NOT HAVING KEYWORDS: african%, caucasian, asian%, indian%, divers%, mixed%

3. Repeat steps 1 & 2 for Asian models by using the following terms:

• HAVING KEYWORDS: business%, asian%
• NOT HAVING KEYWORDS: african%, caucasian, hispanic%, indian%, divers%, mixed%

4. Analyze the Results:

• LookStat updates collection stats hourly. After that, you can compare the performance of each set of models.
• Pay attention to RPI and Price per download numbers since the collections will likely be of different sizes.

5. Notes:

• The ‘%’ sign symbol is a wild-card character that adds all variations & endings
• Because we’re trying to isolate each group, I’ve added exclusion keywords so each set only contains the relevant models.

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