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Author Topic: 2 yr revenue estimate and other projections seem wrong  (Read 3449 times)

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« on: December 14, 2011, 09:04 »
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Ok, first let me say I'm a big fan of MicrostockAnalytics.

Second let me say I think the 'projections' seem a little off.

For example, the 2 year projected income seem to be off.
I have one shoot that has been online for 6 months and I have made $X over those 6 months
The 2 year projected income is .93*X .. that is to say, it is 93% of what I have already made.  By a simple calculation I would say a 2yr projected income should be 4x what I made in 6 months, not 0.93x

Trailing Revenue also seems to be off
It seems that trailing revenue calculates months where the images weren't live... so if it is a 6month trailing revenue and the images have been online for 1 month that means 0 for 5 months in the calculation.  Is i possible to adjust the months to include only the ones where images are online?


« Reply #1 on: December 14, 2011, 09:14 »
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Hmm, I think any prediction has to take into consideration WHAT you were shooting.

A set of spark plugs won't show as much decline in sales as some editorial shots of a fashion show. Stupid example but I hope you get my drift.

I have images that "wore themselves out" over the course of 6 years. Every month a little more. Other images keep selling exactly the same for straight 6 years. And then I have some that sell for a few weeks well, then drop dead.

Very hard to predict saleability when it comes to that. I think we have a better understanding of projections than any software.

« Reply #2 on: December 14, 2011, 14:45 »
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Since Shutterstock has changed popular sorting all revenue projections are way off. Approximately, I would expect revenue predictions for any photoshoot after April 2011 to be 4-5 times less then actual revenue. Also, even for older photoshoots predictions can be very different from reality if photos on other stocks are selling better then average for your portfolio. Plus any seasonal images are always off ;) My plan is to replace predictions algorithms completely to have more accurate results.


 

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