The User Summary App
Revenue Analysis Overview
The Revenue Analysis app is where you look for information about total and average revenue, customers and order counts. It is also where you can analyze the effectiveness of various sales-generating activities, such as different kinds of tasks (prospecting, reconnect, customer care, etc) and content-sharing.
The main sheet, Revenue Summary contains a table and a graphical view of the total and average revenue over the past 2 years. The chart allows you to toggle between various date dimensions and the filters allow you to look into specific time periods or consultant segments.
The other sheets are for looking at specific areas relating to revenue-generation:
Prospect Conversion provides detail on the number of prospects that are converted into customers, the average number of days to convert a prospect and from the first to second order, as well as the average number of tasks completed before a prospect makes their first purchase.
Sales Activity Analysis allows you to look at specific revenue-generating activities (prospecting tasks, reconnect tasks, custom lists and content-sharing) and determine the financial impact of those activities. See How do I find out how much revenue was generated from reconnect tasks? for a detailed walk-through of this sheet.
Comparative Analysis - Revenue Summary is useful for comparing overall revenue numbers between different consultant segments (country, activity level, task completion, etc.) or different time periods.
Comparative Analysis - Prospect Conversion is useful for comparing the success in converting prospects between different consultant segments (country, activity level, gross revenue, etc.) or different time periods.
Order-Level Data will allow you to drill into specific, order-by-order data, including consultant ID, order ID and specific order totals.
How do I find out how much revenue was generated from reconnect tasks?
Determining how many users are active depends on whether you define active as having logged in within the previous 30 days (and you want to know how many users were active as of June 1, for example), or whether you determine active based on who actually logged into Penny during a given time period.
For an overview of the difference and how to find the various numbers, see the video below.