Auto Insights can help finance teams monitor and analyze revenue performance, maximize profitability and uncover new opportunities. You can read more here on our website.
This article will cover:
Example insights from this use case
Recommended data structure for this use case
What sort of insights can Auto Insights help me uncover?
We've outlined some example questions which Auto Insights can help answer through a combination of its proactive insights, Search, and What caused this? analysis:
Analyze revenue, team & product performance
Revenue by product category
Monthly percentage growth of profit by sales office
Worst performing sales offices for Q1 FY20
Highest revenue by account manager last month
Best performing products for FY19
Worst performing products for Q1 FY20
Profit by country and product
Worst performing channel
Quarter on quarter profit compared to the previous comparable period for Q1 FY20
Uncover new opportunities
Growth by channel last quarter?
Best performing product category month on month?
Profit by channel and product category
Percentage growth for revenue by product name last financial year
How do I structure my data?
Auto Insights requires structured, transactional data, with at least 1 measure (e.g. profit) and 5 segments (e.g. product names). In addition, we recommend at least 7 months of data (at monthly or daily granularity) so you can take full advantage of Auto Insights' Unexpected Changes feature.
Auto Insights can connect to databases and .csv files. For this type of use case, we recommend a direct database connection.
Example data structure
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Refer to this article for more information about data structure.
Here are some of the typical segments we find in revenue data. A segment is a qualitative value, like names or categories:
Customer attributes: Customer name, salesperson name, customer size, etc.
Product attributes: Product category, product sub-category, product type, product name, etc.
Other attributes: Channel, sales office location (in natural language, e.g. Sydney, Melbourne, etc.)
A measure is a quantitative, numeric value. Some of the typical measures include profit, revenue, margin, quantity sold, etc.
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