# 解析iPhone应用销量趋势的计算公式

Chart 1 from Gamasutra.com

103    95     92     94     93     112     114

chart 2 from Gamasutra.com

chart 3 from Gamasutra.com

chart 4 from Gamasutra.com

Daily iPhone App Sales Trends

by William Volk

In marketing iPhone apps it is useful to see the effects of app description, screen shots, advertising campaigns and public relations on the sales of your app.  The best way to do that is to see how these changes effect the number of downloads you are seeing on a daily basis.

There is a problem with this approach.  What developers have long observed is that download volume varies,  based on the day of the week.  For example: If you changed a screen shot in your app description (on the app store) on Friday, is the 10% increase in sales on Saturday (compared to Friday) a good or bad thing or just a reflection of more downloads on Saturday vs Friday?

You could always compare the sales numbers to last week, but that could reflect larger trends.  If you are making frequent changes it’s good to have a way to compare day to day sales/downloads.  So we set out to determine the day to day trends for app downloads.

We started with a free popular game that had been released in January, Bocce-Ball.  Bocce-Ball had hit #6 in all apps, and was continuing to see a good number of downloads even after promotions and advertising had stopped.  What’s more, we were weeks away from an update so we believed we would see consistent download numbers and definite patterns emerging on the sales based on the day of the week.  The weekly download volume remained relatively stable during this time.

We used the daily download numbers starting on Monday Feb. 28th (2011) and ending on Sunday March 10th.  For each week we normalized the numbers to an average of 700 downloads a week (100 per day) and looked at the daily trends for these six working weeks.  Once that data was complied, we removed two of the weeks with the greatest deviation from the norm from the analysis and came up with the following results as represented by these graphs:

In tabular form this came out to the following matrix:

Monday Tuesday Wednesday Thursday Friday Saturday Sunday

103 95 92 94 93 112 114

What’s surprising was how small the deviations for the entire data set were from this model: