“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:
NB: Product retailers use their own products, but I think it’s still instructive. Service providers seem to use more stock imagery.
The chart below shows the Top 10 Leisure Activities in the USA based on number of female participants in 2009.
The chart below shows the Top 10 Leisure Activities in the USA based on the % of participants that are women. This data will help you ensure your shoots are realistic.
The actual number of participants for the above activities are shown below to help you prioritize your efforts. For example, gymnastics and roller-blading appeal to a much smaller segment of the population than walking, working out the gym, or yoga.
Source: National Sporting Goods Association
Points to Note:
- Yoga is close to 80% female, growing, and ranked #11 in terms of total participants. When casting yoga shoots, you should keep this mix in mind.
- I was surprised that walking is 60% female in terms of participation. I normally think of this as a couples concept but the data would suggest women walking together would be a worthwhile concept.
According to the 2010 Census, the Hispanic & Asian populations are the fastest growing ethnic groups in the USA.
- Hispanic: 50.5 million people, +43% growth from 2000-2010
- Asian: 14.7 million people, +43.3% growth from 2000-2010
Together, these groups represent 21% of the total US population in 2010 and accounted for 72% of population growth from 2000-2010.
Conclusions:
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Methodology:
I got public data from iStockcharts for the Top 2000 contributors, ranked by Total Downloads. I then plotted the following:
- Rank vs. Years contributing of the Top 2000
- Average & Min Years contributing for the Top 1000
- Histogram of Years Contributing for the Top 200
My goal was to examine tenure vs. performance to see if the number of years you had been at iStock made a difference.
Chart 1: Top 2000 Contributors: Rank vs. Years Contributing
- The average for the entire set was 5.4 years.
- The newest member of the Top 200 had been contributing for less than 3 years.
- The entire set was in the range of 1.5 – 9.0 years.
Chart 2: Top 1000 Contributors — Average & Min Years Contributing
- The most notable thing here is how little variation there is — for the most part, there is no difference in experience between those ranked 1-200 and those ranked 800-1000. I think this is amazing.
Chart 3: Top 200 Contributors by Years Contributing
- 48.5% (97/200) contributors had between 4-6 years of experience.
Conclusions
- I didn’t expect to find this, but there is very little correlation between your iStock performance and your years of experience at iStock.
- The R-squared coefficient is 0.03. It’s range is between 0 (no correlation) and 1 (perfectly correlated).
- Chart 1′s distribution is very uniform for each ranking band.
- No difference in avg & min years contributing for the Top 1000.
- It takes about 2 years to build up a portfolio large enough to break into the Top 2000 at iStock.
- After that, quality and consistency in uploading is what counts.
Methodology:
I ran the searches below on iStock and summed the downloads of the top 200 Best Match results for each one:
Results:
Points to Note:
- 45% of the Total Downloads in this set of searches were generated by Valentine’s Day AND Roses.
- Valentine’s Day AND Roses generated more cumulative downloads than just Valentine’s Day.
- Jewelry and Chocolate are tiny niches compared to roses.
To see how images were slotted in iStock’s Best Match search results, I divided 2,100 results into two groups:
- New Images: Images with 0-1,000 DLs
- Proven Images: Images with 1000-10,000+ DLs
New vs. Proven Images by Search Position
The chart below shows the number of new vs. proven images for the first 25 positions.
Points to Note:
- For popular searches (business, medical, lifestyle), the top positions are reserved for images with staying power.
- iStock is dedicating half of the first page to New images (assuming 10 images per row on a typical screen.)
- 80.9% of the first 5 search results were Proven images even though they only made up 40.8% of the total results.
- Digging deeper, there are no images with less than 100 DLs in the first five search positions.
We built a calculator that shows you how RPI and upload rate affect your earnings. While playing around with it, we discovered that if you have a monthly RPI of $4 and upload 400 images each month, you’ll earn $1,036,800 in 36 months!
Furthermore, if your RPI is $5/month (which is true for many photographers), you can be a millionaire in less than 3 years if you increase your production.
A million dollars in less than 3 years. What are you waiting for? Sign up for LookStat and let’s go.
We care about analytics, not addition. We don’t track your sales at every site, but we do make you more money by increasing your RPI and finding gaps in your portfolio.
The top 5 reasons you need LookStat Analytics are:
5. Know when to upload your images to maximize sales.
4. Increase your RPI.
3. Benchmark your performance.
2. Find out what to shoot next.
1. Make more money.
LookStat: Analytics, Not Addition. Get the LookStat Advantage and sign up for LookStat Analytics today!
We’ve been providing analytics for two years but never explained to you why analytics is important. We were so close to these ideas we didn’t think to share them with you. Simply put, you need to measure and improve your performance to make more money.
Measure The Right Numbers
- Revenue Per Image (RPI) – How earnings are changing.
- Sell Through Rate (STR) – How many of your images sold.
- Earnings Per Download ($/DL) – Pricing trends.
Improve to Make More Money
- Optimize – Analyze the fundamentals of above average performers, such as focus, lighting, and composition to find trends.
- Diversify – Find gaps in your portfolio by classifying your shoots and concepts into these five categories: business, medical, lifestyle, seniors, travel to ensure you’re not missing a money making category.
The LookStat Advantage
- Automatic Data Collection – We collect your data for you.
- Automatic Calculation of Statistics – We calculate RPI, STR, $/DL for you.
- Easy Portflio Analysis – We enable you to group your portfolio by shoots, concepts, and keywords.
Get the advantage today by signing up for LookStat.
Find out more why you should use LookStat for Analytics & Back Office
Thank you,
Casey & Rahul
LookStat metrics and collections makes it incredibly easy to compare how well different concepts perform in your portfolio. For this post, I compared images shot indoors versus images shot outdoors in an account that we are allowed to share publicly. The LookStat Index displayed below is a rating for the entire set.
About the New LookStat Index (LSI)
- LSI Calculation: Earnings per Download x Sell Through Rate x RPI
- LSI provides a complete score for a group of images by capturing:
- Pricing (Earnings per Download)
- Royalties (RPI)
- Overall Portfolio Quality (Sell Through Rate)
- A more detailed post on the LSI will be coming soon.
Indoors vs. Outdoors
Points to Note:
- The Outdoor images have a 31% higher LSI than the Indoor images.
- Indoor images had a better Sell Through Rate, but fell behind on earnings per download and RPI.
- LSI values above are low – they can be up to 100 times higher for higher quality collections.
- LookStat computes these metrics automatically – doing it by hand for 2,000+ images is practically impossible.
Sign up for LookStat and start calculating your own LSI today. Tracking this over time will give you a clear picture of your progression in microstock and LookStat does this automatically for you.
