LookStat – Official Blog

Steve Gibson (@microstockin on Twitter) at Microstock Insider has a couple of great posts up that are well worth your time. The first is a post about popular search keywords and the second, from last year, is about seasonal stock images.

Lots of good data and exploration of the relationship between popular searches/data on Google vs. search data from a free stock site that Steve has access to. He concludes that the correlation is quite high, but there are likely to be situations where the two diverge. I think this is absolutely true. Not every search query makes sense on microstock.

Having said, that, I think it’s important to remember that just because a topic isn’t well covered already, it doesn’t mean that there isn’t demand for it. The beauty of microstock is that it is possible to test the waters by shooting some more experimental concepts and examining the results in views and sales. PicNiche can also help you figure this out.

In general, if people are looking for something, there’s a high likelihood that marketers will try and advertise it to them. (Of course, you have to figure out where in the long tail you are operating to figure out how much to invest in that image.)

Environmental themes are common in microstock and I thought it would be helpful to share the top search keywords associated with ‘energy’ to aid in planning and keywording energy related microstock concepts.

Top Search Keywords (via Google Adwords Query Tool)

The big takeaways here are is that ‘solar energy’ is searched for twice as often as ‘wind energy.’ I also initially found ‘jobs’ surprising. With hindsight, it makes current sense given the current economic climate and that concept might be worth exploring as part of a shoot.

Google Insights Trends

I plotted 2008 data to get a sense for full-year seasonality and apart from a steady rise from Jan to April, there doesn’t appear to be a significant seasonality barring a decline from November to Jan which is likely due to Thanksgiving & Christmas.

While drilling into solar energy a little more, I found the regional data interesting as well. The top five regions are Nigeria, Pakistan, India & South Africa from a search volume perspective.

Implications for Microstock

Given the above data, it’s clear that solar should be at the top of your concept list and given the regional trends, varying the ethnicity of your models is probably worthwhile as well.

While playing around with Google Insights data for Valentine’s day trends, I was somewhat surprised to find that there are on average 6 times as many searches for ‘valentine’s gifts for him’ as there are for ‘valentine’s gifts for her.’

I guess men just stick to flowers, chocolates & jewelry while women have a harder time with men’s gifts. On a slightly related note, flowers peak on Valentine’s Day & Mother’s Day with jewelry ruling the roost at Christmas time. I was a little surprised to see such a small uptick for jewelry at Valentine’s day but I suppose it’s fairly soon after Christmas.

When planning your shoots for Valentine’s day, it looks like flowers & chocolates will be your friends.

Matt’s series on the Long Tail and microstock got me thinking and I decided to do some quick searches on human emotions on Dreamstime to see if anything interesting showed up in the data.

I did searches for 5 keywords (angry, bored, happy, sad, scared), ordered the results by download and captured views and downloads for the top 10 images. I also recorded the number of results returned. While this is completely non-rigorous analysis, there are some interesting observations.

If anyone has thoughts on how to better interpret this data, please don’t be shy. Also, if anyone would like a spreadsheet of the data, let me know and I’ll send it to you.

The things that jumped out at me from this exercise (in no particular order)

• There are way more images returned from a search for ‘happy’ than for all of the other terms I looked at. While this does make sense from a marketing perspective, the skew seems out of whack when compared to the relative download counts.

• Conversion rates for ‘bored’ and ‘scared’ are very high and image counts are very low. To only have 3,000 images for a generic emotion like ‘bored’ in a 4 million plus image universe is surprising to me.

• Don’t ignore emotional variations when conducting shoots. Collectively, they account for a significant number of downloads and there are fewer results in the pool, so in theory standing out should be easier. Long live the Long Tail!

• Search terms that have high conversion & relatively low result counts may indicate areas of opportunity but (much) more testing is needed before you can draw any conclusions.

• PIcNiche is a really cool site and they basically automate what I’ve done here using Fotolia data. I think it’s well worth a visit as you plan your next shoot. (Thanks for the pointer to it Lee.)

Image Count (number of results returned when searching for a term)

The number of images that are returned when you search for ‘happy’ versus the other keywords is staggering. There are over 15 times as many images returned for ‘happy’ versus ‘sad’. While this lines up with intuition (happy people are better for marketing) the spread took me by surprise.

Average Downloads & Views for Top 10 Images

To get this data, I just sorted the results for each search by download count, recorded the download and view numbers for the top 10 results and then calculated the average. There are also some interesting things to note in the individual image data which I discuss in the next section.

One thing to note here is that even though there are over 15 times as many images results for ‘happy’ as there are for ‘sad’, the difference between average DLs for the top 10 images is only 3x. While ‘happy’ clearly drives more downloads than the other emotions, if you incorporate them into your shoots, there’s an opportunity to capture incremental downloads.

The results for ‘sad’ were skewed by two images that had an extremely high number of views. When I removed those two data points, the average number of views for sad was: 1,900 which is more in line with the other results. Those two images were in numerous collections and my guess is they were featured on the home page or as editors’ picks at some point.

Average Conversion Rate

For this post, I defined conversion rate at the ratio of Downloads to Views. To some degree, this metric is a measure of quality/keyword relevance. If something is viewed a lot, but not downloaded that much, it either means that it is a low quality image (people viewed it but decided not to buy it) or it could mean that the image is not relevant to a high volume search keyword.

Again, the results for ‘sad’ are skewed by the two high view images. If I remove them, the average conversion for ‘sad’ rises to 9.6%. It’s interesting to look at the PicNiche data for the same search terms. PicNiche rocks in my opinion and I’m looking forward to seeing how it evolves. I think they are using Fotolia data and have access to the total downloads and views for a given term as opposed to the sample approach that I took.

If you superimpose Average Downloads per Image (Left axis) and Average Conversion Rate for the search term (Right axis) you can see that even though all terms apart ‘happy’ have similar download numbers, ‘bored’ and ‘scared’ have significantly higher conversion rates than the others. There are also far fewer of these images than in the other two cases. Clearly more analysis is required, but the hypothesis that high conversion & low image count = opportunity is probably worth exploring further.

Per Image Detail for the Top 10 Images (For the Data Geeks out there…)

Perhaps the most interesting observation here is that there isn’t a ton of variation between the top ranked image and the 10th ranked image in terms of download counts. Another thing that jumped out was how close to each other the download counts for the searches other than ‘happy’ were.