This fully automated cloud-based report examines data each day and based on the daily data, it identifies as an item as in being in the top 33%, middle 33%, and bottom 33%. That’s where the high medium and low come from.
It’s possible to go more or less granular:
- average, and
- number of standard deviations.
Notice, also, that it automatically created a pivot of the data. Normally, data is delivered in tabular format, but that makes it difficult to get the trend across time perspective. NEXT Analytics has a built-in engine for pivoting. What does this mean? It means NEXT Analytics can accept dates in any format, and quickly convert them to date periods including but not limited to:
- day of month
- day & month
- day of year (365)
- month & year
- day of week
- special time periods, such as fiscal periods, seasonal periods, and advertising campaign date ranges
With built-in filtering, you can, for examples:
- compare all the fridays for the past year
- compare fiscal quarters over multiple years
- changes from one period to the next, including variance, percent difference, and growth.
When you look at time periods side by side, you can now compare their ratings, knowing that the comparison is based on each particular period (e.g. day). This is important, because we know that performance is often a relative comparison, and certain external factors affect performance such as holidays, weather, advertising campaigns etc.
In the example, you can see that some rows have poor performance each day of the month, no matter which day.
You can also see some other days that toggles between ratings. This is hinting possible opportunities and problems.
This technology can complement your business intelligence platform. It can help you with data mining, by pulling data from internet sources and making it into useful information. You can download from databases, ecommerce systems, Facebook, Google, Twitter, YouTube, and more!