Understanding sales performance has always been focused on the ability to uncover areas where sales have increased or dropped significantly, which teams are hitting their goals, which manager has a large group of members with good/poor performance, who requires additional coaching, where new opportunities are (significant growth), etc.
Typically understanding these aspects are apart of a daily standup, weekly huddle or fortnightly and month end reporting.
An example of sales performance data structure
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Please refer to this article for more information about preparing data.
A segment is a qualitative value, like names or categories. Here are some of the typical segments we find in sales performance data:
Customer attributes: Customer name, Customer size, etc.
Product attributes: Product category, Product sub-category, Product type, Product name, etc.
Team attributes: Team Name, Region, Channels, Sales Office Location, Account Manager, etc.
A measure is a quantitative, numeric value. Here are some of the typical measures we find in sales performance data:
Sales Metrics: Revenue, Profit, Margins,
Team Metrics: Targets*, Variance to Target*
Other Metrics: Number of Customers (Client base), New Acquisitions, etc.
What sort of insights can Auto Insights help me uncover?
Understand sales patterns
Product Category that is driving the increase this month
Understand revenue movement across the Channels
Monthly/Quarterly/Yearly revenue compared to the previous comparable period
Best performing products type in the Financial Year
Decline in Top Customers' revenue this Quarter
Optimise team performance
Revenue variance to target by teams/regions/sales offices
Worst performing sales offices for Q1 FY21
Which team/account manager missed targets for 2 months consecutively
Uncover new opportunities
Growth by channel last quarter
Best performing product category month on month
Revenue by channels, products, regions
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