Examples

Overview

This section presents practical examples of how the dataset can be used to answer business questions and drive informed decision-making across sales, finance, operations, and customer relations. We explore scenarios that briefly illustrate how billing data translates into measurable insights on customer behaviour, product performance, and operational metrics. For further details on how to create these overviews, reach out to our support team through the customer portal or by sending an email.

• Financial Overview
• Reconciliation Checks
• Understanding Customer Behaviour
• Support Bundle Selection
• Custom Classification for Services and Products
• Product utilisation of customers in the same category

---

Financial Overview

The dataset provides a clear and comprehensive view of financial performance, from high-level trends to granular details. You can track financial KPIs such as revenue and margin over a chosen time period, and see how products and customers contribute to these KPIs. From there, you can drill down from an overview into the specific records to understand what changed and why, keeping the story clear and actionable for different audiences. The list below offers example overviews to showcase what the dataset can reveal.

1. Revenue Overview: Track invoice totals by month or quarter, and break them down by product, region, or customer to reveal growth drivers and top revenue contributors.
2. Margin Overview: Track margin over time to highlight profitable segments (e.g., products, customers, regions) and spot where performance is slipping.
3. Discount and Promotion impact: Assess how discounts affect margin at the product and customer level, highlighting where promotions improve sales but reduce profits. Track discount patterns over time to spot segments with persistent price pressure and identify campaigns that deliver healthy margins.
4. Customer Behaviour: Track financial KPIs for customers or customer clusters to identify top performers and accounts that need attention. Then drill into the underlying transactions to see what drove the results — products purchased, discounts or promotions, and periods of unusually high or low activity.
5. Forecasting: Use historical data to identify revenue growth and seasonal changes. Use these data to forecast customer revenues.
6. Mix and cannibalization: Quantify how new SKUs shift demand among existing ones; measure halo vs cannibalization.
7. Churn and expansion risk: Predict downgrades/churn using signals like declining order frequency or rising discounts.
8. Channel economics: Compare direct vs partner/marketplace net margin
9. Incrementality and uplift: Use A/B tests or synthetic controls to measure true lift and margin ROI of promos and campaigns.
10. Renewal and price uplift: Track renewal rates, realized vs contracted price escalators, and leakage at renewal.
11. Outlier alerts: Flag unusual discounts, negative margins, odd payment behavior, or sudden volume spikes/drops.

These are just some examples of what you can do with the billing data using the datasets. You can always reach out to us and ask about the KPIs and Parameters you would like to track and we will be happy to brainstorm together with you.

Cross-Functional Insights

The CloudBilling dataset can be combined with other company data (e.g., CRM, ERP, support systems, contract management systems) to enable integrated analysis and gain cross-functional insights. By linking billing date to these sources, you create a unified view of customers, contracts, usage, and cash movement that strengthens forecasting, compliance, and decision‑making across finance, sales, and operations. Here are some examples of cross-functional overviews:

1. Perform cash flow management: Track invoice totals against collections to monitor inflows, overdue balances, and the impact of credits or refunds. Build simple forecasts based on billing cycles and payment behavior to plan liquidity and spot potential delays.
2. Ensure contracts match invoices: Compare billed prices, quantities, discounts, and dates with the agreed contract terms to confirm charges follow the agreement. Highlight rate, discount, or dates mismatches for review and resolve them before they affect customers or financials.
3. Support Bundle Selection: Combine billing data with support ticket data to spot products with high support intensity, onboarding friction, or customer retention risk. These products can be prime candidates for bundling. For each product, estimate the typical support cost and set a bundle price that covers this cost and delivers a positive margin. Then, you can also track the bundle performance KPIs against the product performance KPIs.

Reconciliation Checks

The datasets can be used as a tool to perform billing sanity checks. These checks can ensure financial records are accurate and reliable. It creates a clear audit trail from each journal entry back to the source documents, ensuring the data is complete, correct, and unchanged. Here are some example overviews that can be used for performing sanity checks:

1. Detect anomalies and revenue leakage: Track month over month invoice totals to identify unusual spikes or drops. Now break them down by customer, invoice, and product to identify any missing usage, incorrect prices, or double charges.
2. Validate tax and fee calculations: Confirm taxes and fees are applied where expected and calculated proportionally to taxable amounts. Identify invoices with missing tax on taxable items, fees that exceed typical percentages, or tax-to-subtotal ratios outside expected bounds.

Custom Classification for Services and Products

The cost and revenue associated with a customer can be classified using custom tags like ‘ServerName’, ‘InstanceType’, or ‘ApplicationName’. When pushing the data to CloudBilling, these tags should be added to the purchases as metadata. For Microsoft’s cloud products, these tags can be added in the Partner Center. Either way, the tagged data is retrieved by CloudBilling as metadata along with the purchases. Once the LineItems are generated from these purchases and synced with the dataset, the associated metadata is also synced. Now the LineItems can be grouped based on a metadata value. This provides you:

1. Clear visibility: Slice costs and revenues by business unit, project, environment, workload, or customer.
2. Better decisions: For example, grouping LineItems with the same ‘ApplicationName’ tag can help identify products with high-cost and products with idle resources on a specific application. This enables you to see the products that your application use and how much they consume, in return helping you optimise your costs and ROIs on applications.

Product utilisation of customers in the same category

Product utilization patterns across similar customers can reveal quick cross-sell and upsell opportunities. By grouping customers into segments (e.g., industry or market clusters), you can see which products are popular in each group, where there are gaps, and which customers are most likely to adopt. Tracking how buying behavior changes over time helps you spot early signals (like declining use of anchor products) and tailor bundles, pricing, and promotions to each group’s needs.

1. Cross-sell Opportunities: Identify products widely used in each segment. Identify customers in the segment who don’t yet use the segment’s staple products and prioritize them for cross-sell.
2. Buying Behaviour Trends: Track adoption and utilization over time at the segment level to spot rising interest or early declines. Flag segments where usage of anchor products is slipping so you can act before churn risks grow.