AI Revenue Analysis Tool — Upload Sales Data, Ask Questions

What this export contains

What makes this file hard to work with

• Month-over-month revenue change requires building a summary table each month

  Your billing export has individual invoices sorted by date. Seeing revenue by month requires grouping by month and summing — a pivot table you rebuild every reporting cycle.

  DataMimi Ask 'what was revenue by month for the last 12 months?' and Datamimi groups your billing export by invoice month and returns the monthly revenue series.

• Revenue concentration risk is visible but never measured

  You know your top 5 customers are important but what % of total revenue they represent — and whether that concentration is increasing — requires a calculation nobody makes regularly.

  DataMimi Ask 'what percentage of total revenue comes from our top 10 customers?' and Datamimi ranks customers by revenue and calculates concentration %.

• At-risk revenue is invisible until customers churn

  Customers who haven't paid a recent invoice are in your billing export but identifying outstanding revenue by customer age requires filtering by payment status and date.

  DataMimi Ask 'which customers have outstanding invoices more than 30 days past due?' and Datamimi filters by Payment Status and Due Date to return the at-risk list.

• Revenue by product or service is never cleanly reported

  If you sell multiple products, revenue by product line requires grouping by Product/Service and summing — a pivot table that takes 10 minutes to configure correctly.

  DataMimi Ask 'what is revenue by product line this quarter?' and Datamimi groups by Product/Service and sums Net Revenue for each category.

Common questions