Customer Scorecard

hi DanielASouza 

To design your customer scorecard dashboard effectively, keep it clean and focused by showing each customer’s overall score (out of 20) alongside their tier classification (A–D), supported by clear visuals for each KPI. Use conditional formatting with color bands to highlight risk levels, and structure the layout so the top section gives a quick overview, the middle section breaks down KPI performance with points assigned, and the bottom section shows trends and alerts for customers slipping into lower tiers. Best practices include using Power BI’s goal tracking to automate thresholds, sparklines for quick trend visualization, drill‑through pages for invoice/payment history, and benchmarking against industry averages. This way, the dashboard becomes both a credibility monitor and a decision‑support tool, helping you instantly identify VIP customers and those needing review.

How to Create a Scorecard in Power BI

Create scorecards and manual goals - Power BI | Microsoft Learn

32 Best Power BI Dashboard Examples & Templates in 2026 | Coupler.io Blog

Regards,

Ritesh

Community Champion

Please mark the answer if helpful so that it can help others

Dance-Sing with Data -BI & Analytics

Hi DanielASouza ,

Thank you for your follow up. I understand that you are seeking a foundational structure to reference when developing the scorecard logic and layout. Although I am unable to share a PBIX file directly in the forum, I can provide a straightforward approach to help you build the scoring model in your report. Typically, each KPI is scored using measures, and these individual scores are combined to form a total customer score.

For instance, a KPI can assign points when a condition is met and zero when it is not. For payment reliability, the measure can award the full score if invoices are paid within agreed terms, based on your late payment calculation. Similarly, the profitability measure can check if the net profit margin exceeds your threshold and allocate points accordingly. This method applies to other KPIs such as quote conversion, credit utilization within limits, and yearly sales growth compared to the previous year. Once these measures are established, you can create a total score measure that sums the individual KPI scores.

After calculating the total score, a conditional measure can classify the customer tier by evaluating the final score and assigning categories like VIP Low Risk, Stable Growth, Watchlist Average Risk, or High Risk Review. This tier measure can also be used for conditional formatting in visuals, allowing users to quickly identify customer risk levels.

For the report layout, many scorecard style reports work well with a simple structure where the top section shows the overall customer score and tier for the selected customer, the middle section provides a breakdown of each KPI and the points assigned, and the lower section shows supporting trends such as sales over time, payment behavior, or quote conversion performance. Using a table or matrix that lists each KPI alongside its calculated score is often helpful because it makes the scoring logic transparent to the business users.

If you follow this approach, you will have a flexible scoring framework where all of the logic sits in measures and the visuals simply present the results. This also makes it easy to adjust the thresholds or point allocation later without changing the overall dashboard design. Hopefully this gives you a solid base to start building your customer scorecard mod

Thank you.