WaterfallBridge — Explain KPI Changes Across Value, Percentage and Rate Metrics
Automate waterfall, bridge and contribution analysis for finance, marketing, e-commerce, operations, and product analytics — from revenue and margin to conversion rate, retention, yield, and other hard-to-explain KPI movements. See Use Cases →
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Step-by-Step: How to Use WaterfallBridge
Follow along with our detailed walkthrough and master key driver analysis from day one.
Use Case
How has our profit margin been doing over the past few quarters?
Sales increased by 10%, but overall profit margin decreased by 7pt.
That's not good. Can you give me a breakdown and analysis report of the 7pt profit margin decline? I need to know what specific products and reasons caused this decline. It would be best if you could do a comprehensive analysis by region, product type, channel, or team across different dimensions. You can compare with different time periods like last year, the year before, or our budget. This way we can identify problems promptly and solve them immediately.
If it were just revenue analysis, that would be simple enough. But breaking down the 7pt profit margin decline to specific 1000 SKUs and knowing which factors—price, discount rate, cost, etc.—contributed how many pts to the overall decline, that's not an easy task. If we add regional, product type, and channel dimensions, it becomes even more complex. Then comparing with last year, budget, and other data versions—it's almost impossible...
Do you frequently encounter similar needs in your work? Do you spend a full day, or even days or longer (for multi-dimensional analysis) to reach an analytical conclusion? Does this analysis contain a lot of manual estimation, and the final aggregation of all impact factors doesn't accurately equal the 7pt profit margin decline, requiring manual adjustments? Do you always harbor doubts about whether the calculation process is 100% correct?
Please use WaterfallBridge to complete similar analysis in just minutes, getting accurate analytical results through algorithms and decomposing revenue, profit margin changes into the impact factors, different dimensions, and scenario comparisons you desire. This helps you pinpoint problems precisely, take timely action, and improve enterprise competitiveness.
Who Can Use WaterfallBridge
WaterfallBridge is not just for finance. Any team that needs to explain why a KPI changed — especially percentage and rate metrics — can use it. From margin %, conversion rate, retention, and yield to offer acceptance, SLA, and completion rate, WaterfallBridge helps decompose the change across products, customers, channels, regions, cohorts, and other dimensions with full traceability.
It is especially useful when you need to:
- Explain changes in value metrics such as revenue, profit, cost, spend, or volume
- Decompose changes in percentage or rate metrics such as margin %, conversion rate, CTR, yield, discount rate, or churn rate
- Identify the contribution of detailed dimensions such as SKU, product, customer, channel, region, supplier, campaign, or site
- Produce results that tie out exactly and can be reviewed step by step
| Industry / Domain | Typical Roles | Common Analysis Questions | Core Factors WaterfallBridge Decomposes | Unique Value |
|---|---|---|---|---|
| Finance / FP&A | FP&A Manager, Finance Analyst, CFO Office | Why did Revenue / Profit / Margin % change vs budget or last year? | Price, Volume, Mix, Cost, FX, Structure | Bridges both value and rate metrics (e.g. Margin %) with full traceability |
| Banking / Financial Institutions | FP&A, Treasury, ALM, Finance Manager, Bank Performance Analyst | Why did NIM or NII change? Which product, deposit type, or branch drove the shift? | Rate, Volume, Mix, Balance Sheet Structure, Product, Maturity, Currency, Branch | Decomposes NIM / NII into rate, volume, mix, and structural factors — traceable for management reporting and board review |
| Insurance | Finance Analyst, Underwriting Analyst, Actuarial Analyst, CFO Office | Why did combined ratio, loss ratio, or underwriting margin change? Which product or region drove it? | Loss Rate, Expense Rate, Premium Volume, Product Mix, Region, Channel, Claims Frequency | Decomposes ratio metrics like loss ratio and combined ratio by product, region, and structural composition |
| Marketing / Advertising | Performance Marketing Analyst, Growth Analyst, Marketing Ops | Why did CTR, CVR, ROAS, or CAC change? Which campaign or keyword drove the shift? | Traffic, Channel Mix, Device Mix, Geography, Campaign, Keyword, Offer | Explains rate metric changes and drills down to ad-level dimensions |
| E-commerce / Retail | E-commerce Analyst, Category Manager, Commercial Analyst | Why did sales, gross margin %, discount rate, or conversion change? Which SKU / store / channel contributed? | Price, Units, Discount, Product Mix, Channel Mix, Freight, Cost | Decomposes results to SKU / category / channel / region; handles high-dimensional analysis |
| Procurement / Supply Chain | Procurement Analyst, Supply Chain Analyst, Sourcing Manager | Why did spend or unit purchase cost rise? Which supplier / category / plant drove it? | Unit Price, Volume, Supplier Mix, Category Mix, FX, Logistics | Explains cost rate and procurement structure changes — not just total spend |
| Manufacturing / Operations | Operations Analyst, Plant Controller, Manufacturing Finance | Why did unit cost, yield, scrap rate, or gross margin change? | Volume, Yield, Scrap, Labor, Overhead, Material Cost, Product Mix | Superior for decomposing rate metrics like yield %, scrap %, margin % |
| SaaS / Subscriptions | RevOps, BizOps, Growth Ops, Customer Success Ops | Why did trial-to-paid, retention, churn, ARPU, or NRR change? | Customer Mix, Plan Mix, Price, Volume, Cohort, Region, Channel | Explains subscription and conversion KPI changes with structural attribution |
| Sales / Commercial Ops | Sales Ops, Business Analyst, Commercial Excellence | Why did win rate, average selling price, customer margin, or regional sales shift? | Price, Volume, Customer Mix, Product Mix, Region, Sales Team | Drills from total sales to customer / product / region contribution |
| Product / Data Analytics | Product Analyst, BI Analyst, Data Analyst | Why did engagement, activation, conversion, or retention change? | User Mix, Channel Mix, Version Mix, Region, Device, Feature Adoption | Gives more explainable answers to "why did the KPI move" than a dashboard alone |
| Customer Service / Support Ops | Service Ops, Support Analyst, Call Center Analyst | Why did SLA, resolution rate, complaint rate, or ticket efficiency change? | Ticket Mix, Channel Mix, Team Mix, Region, Product Type | Separates structural factors from execution factors in service metrics |
| Healthcare / Education / Public Sector | Operations Analyst, Planning Analyst, Program Manager | Why did operational or performance KPIs shift? | Volume, Mix, Resource Allocation, Region, Service Type | Applicable to any scenario requiring clear explanation of complex KPI changes |
| Recruitment / Talent Acquisition | Recruiter, Talent Acquisition Analyst, HR Operations, Hiring Manager | Why did interview pass rate, offer acceptance rate, or hiring conversion change? Which role, channel, or region drove it? | Candidate Volume, Source Mix, Role Mix, Region, Recruiter, Interview Stage, Offer Package | Explains hiring funnel rate changes with drill-down by role, source, recruiter, and region |
| Education / Training | Education Operations, Academic Analyst, Program Manager, Learning Ops | Why did completion rate, attendance rate, pass rate, or course satisfaction change? Which course, cohort, or campus drove it? | Student Mix, Course Mix, Cohort, Teacher, Campus, Delivery Mode, Attendance, Assessment Results | Decomposes education KPI changes across cohort, course, teacher, and campus dimensions |
| Gaming / Mobile Apps | Game Analyst, Live Ops, Growth Manager, Monetization Analyst | Why did D1 / D7 retention, payer conversion, ARPPU, or engagement rate change? Which version, country, or acquisition channel drove it? | User Mix, Version Mix, Channel Mix, Country, Device, Cohort, Content / Event Mix | Bridges game and app KPI changes beyond dashboards, with explainable contribution by version, cohort, and channel |
| Call Center / Contact Center | Call Center Manager, WFM Analyst, Service Ops Analyst | Why did abandonment rate, service level, handle time, or first-contact resolution change? Which queue, shift, or team drove it? | Call Volume, Queue Mix, Team Mix, Shift, Language, Region, Issue Type, Staffing | Separates structural mix effects from operational execution effects in service KPIs |
| DTC / Small Business / Creator Commerce | Shopify Store Owner, Creator Manager, Growth Operator, E-commerce Lead | Why did store conversion, refund rate, AOV, or product margin change? Which product, campaign, or traffic source drove it? | Traffic Mix, Product Mix, Price, Discount, Channel, Creator / Campaign, Geography | Makes advanced bridge analysis usable for smaller teams, not just enterprise analysts |
| Role | What They Care About | Value WaterfallBridge Delivers |
|---|---|---|
| FP&A / Finance Analyst | Budget vs Actual, Forecast vs Actual, YoY variance explanation | Produces fully reconciled bridges and supports complex metrics like Margin % |
| CFO / Finance Lead | Management reporting, result attribution, transparent key drivers | Clear, reviewable, and auditable path for explaining performance changes |
| Bank FP&A / ALM / Treasury | NIM, NII, loan yield, deposit cost, repricing risk, funding structure | Decomposes margin changes into rate, volume, mix, and balance sheet structure with full traceability |
| Insurance Finance / Underwriting Analyst | Loss ratio, combined ratio, underwriting margin, premium mix | Explains combined ratio changes by product, region, channel, and structural drivers |
| Marketing Analyst | CTR, CVR, ROAS, campaign performance | Contribution analysis from total result to campaign / keyword / channel level |
| E-commerce Analyst | SKU, discount, channel, category profitability | Drills from total gross margin % down to SKU / Category / Store / Channel |
| Procurement Analyst | Spend variance, supplier impact, unit cost changes | Explains cost drivers and procurement structure — beyond just showing a report |
| Supply Chain Analyst | Cost, logistics, supplier switching, structural shifts | Analyzes how structural changes affect total cost and efficiency |
| Operations Analyst | Efficiency, service levels, capacity, yield changes | Decomposes operational result changes into actionable drivers |
| Sales Ops / Commercial Analyst | Region, customer, product, and price drivers | Identifies which customers / regions / products are truly driving results |
| BI / Data Analyst | The "why" behind dashboard metrics | Provides structured KPI variance explanation beyond monitoring and alerting |
| Pricing Analyst | Impact of price changes, discounts, and mix shifts on profit | Clearly isolates price, mix, discount, and cost effects |
| Growth Analyst | Why did conversion, activation, retention, or funnel performance change? | Breaks KPI changes into structural drivers across channel, cohort, device, geography, and version |
| RevOps / BizOps | What caused shifts in pipeline conversion, ARPU, retention, or expansion? | Gives a reconciled bridge from headline KPI to customer / plan / region / channel contribution |
| Talent Acquisition Analyst | Interview pass rate, offer acceptance, source effectiveness, hiring funnel quality | Explains recruitment funnel rate changes by role, source, recruiter, region, and stage |
| Small Business Owner / Operator | Store conversion, AOV, refund rate, product profitability | Makes advanced KPI decomposition accessible without building a full BI model |
| Analysis Scenario | Common Metrics | Applicable Example |
|---|---|---|
| Budget vs Actual | Revenue, Profit, Margin %, Cost | Explain the gap between actual results and budget |
| Forecast vs Actual | Sales, GM %, Opex, Conversion Rate | Identify where and why the forecast deviated |
| YoY / MoM / WoW Variance | Revenue, Units, Spend, KPI rates | Compare drivers of change across periods |
| Price–Volume–Mix Analysis | Revenue, Gross Profit, Margin % | Decompose price, volume, and mix impact on results |
| Margin % Driver Analysis | GM %, CM %, EBITDA % | Explain percentage metric changes — not just the dollar amount |
| Contribution Analysis | SKU, Customer, Region, Channel, Campaign | Find out who is pulling up or dragging down overall results |
| Channel / Region Bridge | Sales, Conversion, Spend, Profitability | Compare performance changes across channels and regions |
| Product / SKU Analysis | Margin, Discount, Units, Mix | Understand how product mix changes affect overall KPIs |
| Supplier / Procurement Analysis | Unit Cost, Spend, Landed Cost | Analyze supplier and procurement structure impact on cost |
| Operational KPI Analysis | Yield, Scrap, SLA, Resolution Rate | Explain operational efficiency or service metric changes |
| Marketing KPI Decomposition | CTR, CVR, CPC, ROAS | Explain ad performance and traffic quality changes |
| SaaS KPI Bridge | Retention, Churn, ARPU, Trial-to-Paid | Analyze subscription and customer structure changes |
| NIM / NII Variance Analysis | NIM, NII, Loan Yield, Deposit Cost, Spread | Isolate rate, volume, mix, and balance sheet restructuring impact on net interest margin |
| Loss Ratio / Combined Ratio Analysis | Loss Ratio, Expense Ratio, Combined Ratio, Underwriting Margin | Decompose underwriting result changes by product, region, or channel |
| Recruitment Funnel Analysis | Screening Rate, Interview Pass Rate, Offer Acceptance Rate, Time-to-Hire | Explain why hiring conversion changed across role, source, recruiter, and region |
| Retention / Churn Bridge | Retention Rate, Churn Rate, Renewal Rate, Expansion Rate | Identify whether customer mix, product mix, pricing, or cohort quality drove subscription KPI shifts |
| Conversion Rate Decomposition | CVR, Checkout Rate, Trial-to-Paid, Activation Rate | Explain why the total conversion rate changed across device, channel, region, or landing page |
| Customer Service KPI Bridge | SLA, Resolution Rate, Abandonment Rate, Complaint Rate | Separate volume / mix changes from execution issues in support operations |
| App / Game KPI Analysis | D1 Retention, D7 Retention, Payer Conversion, ARPPU | Explain KPI movement by app version, cohort, event, country, or acquisition source |
| Store / DTC Performance Bridge | Store Conversion, AOV, Refund Rate, Product Margin % | Explain what changed for a small online business by product, campaign, and traffic source |
- Revenue
- Profit
- Cost
- Spend
- Units / Volume
- Headcount
- Inventory
- Margin % / GM % / CM %
- Conversion Rate
- Click-Through Rate (CTR)
- Engagement Rate
- Discount Rate
- Yield % / Scrap Rate
- Retention Rate / Churn Rate
- On-time Delivery Rate
- Net Interest Margin (NIM)
- Loss Ratio / Combined Ratio
- SKU / Product / Category / Brand
- Customer / Account
- Channel / Store
- Campaign / Keyword
- Country / Region / Site
- Supplier / Vendor
- Plant / Factory / Team
- Product / Maturity / Currency (Banking)
- Branch / Business Line (Banking)
Every step of the decomposition is visible and traceable — no black box.
The sum of all contributions always matches the total KPI change — no manual adjustment needed.
Not just a chart — a full bridge logic for driver and contribution analysis.
Especially powerful for Margin %, Conversion Rate, CTR, Yield %, and other ratio-based KPIs.
Analyze the contribution of individual SKUs, customers, channels, regions, suppliers, and campaigns.
Practical for teams that need fast answers, management-ready outputs, and a familiar workflow.
How It Works
In practice, breaking down the difference between two results in absolute value terms is usually relatively straightforward. For example, if total Revenue increases from 1,000 to 1,200, the change of +200 can usually be decomposed directly into drivers such as Volume, Price, Product Mix, FX, or other factors. WaterfallBridge can handle this type of analysis very effectively and conveniently.
However, when the difference is expressed as a percentage metric—such as Margin %—driver decomposition and contribution analysis become much more difficult. The reason is that percentage metrics are not additive, and their changes are often affected by weighting and mix effects.
For that reason, the following example focuses on analyzing the change in Margin %, showing how to decompose the variance by driver and calculate the contribution of each factor step by step.
At the same time, WaterfallBridge can also calculate contribution at the lowest level of detail within each factor—for example, at the SKU level—so users can trace not only which high-level driver changed the result, but also which underlying items contributed to that change.
In summary, WaterfallBridge is designed to efficiently handle contribution analysis for both absolute value metrics and percentage metrics across different business contexts and analytical scenarios.
I. Program Principle Introduction
The following example walks through a two-product scenario to illustrate how each driver is replaced step by step and how the contributions tie out exactly.
Core principle
When we analyze changes in overall Margin %, we cannot simply add up the Margin% changes of individual products, countries, or regions, because total Margin% is essentially:
It is a result formed based on revenue weighting, not a simple sum of sub-item percentages.
Therefore, one of the most reliable methods—and one that will always tie out—is:
That means:
- Start from the old scenario (Forecast / Baseline)
- Replace only one variable with its new value (Actual) each time, while keeping all other variables unchanged
- Recalculate overall Margin % after each replacement
- The change versus the previous step is that variable's contribution
- Continue until all variables have been replaced with Actual
This ensures that:
In addition, at the product detail level, if you update Units / Volume from Forecast to Actual, you are usually also updating the sales structure across products at the same time, so mix effect is often already embedded in the Volume step, and you do not necessarily need a separate "Mix" bar.
II. Case Setup
To keep the example simple and clear, we use two products:
- Product A: naturally high margin
- Product B: naturally low margin
1) Forecast (Baseline)
| Product | Units | Unit Price | Unit Cost | Revenue | Profit | Margin % |
|---|---|---|---|---|---|---|
| A | 100 | 10.0 | 6.0 | 1,000 | 400 | 40.0% |
| B | 200 | 8.0 | 7.0 | 1,600 | 200 | 12.5% |
| Total | 300 | — | — | 2,600 | 600 | 23.08% |
Revenue = Units × Unit Price
Profit = Units × (Unit Price − Unit Cost)
Total Margin % = Total Profit / Total Revenue = 600 / 2,600 = 23.08%
2) Actual (Final result)
| Product | Units | Unit Price | Unit Cost | Revenue | Profit | Margin % |
|---|---|---|---|---|---|---|
| A | 140 | 9.5 | 6.2 | 1,330 | 462 | 34.74% |
| B | 170 | 8.4 | 6.8 | 1,428 | 272 | 19.05% |
| Total | 310 | — | — | 2,758 | 734 | 26.61% |
So the total change is: 26.61% − 23.08% = +3.54pp
III. Stepwise Replacement Breakdown Process
Here we use the following sequence:
This sequence works very well for explaining your program, because it directly shows:
- First update volume and structural changes
- Then update price changes
- Finally update cost changes
| Product | Units | Unit Price | Unit Cost | Revenue | Profit | Margin % |
|---|---|---|---|---|---|---|
| A | 100 | 10.0 | 6.0 | 1,000 | 400 | 40.0% |
| B | 200 | 8.0 | 7.0 | 1,600 | 200 | 12.5% |
| Total | 300 | — | — | 2,600 | 600 | 23.08% |
In this step, only Units are replaced from Forecast to Actual, while Price and Cost remain at Forecast values.
| Product | Units | Unit Price | Unit Cost | Revenue | Profit | Margin % |
|---|---|---|---|---|---|---|
| A | 140 | 10.0 | 6.0 | 1,400 | 560 | 40.0% |
| B | 170 | 8.0 | 7.0 | 1,360 | 170 | 12.5% |
| Total | 310 | — | — | 2,760 | 730 | 26.45% |
Total Margin % changes from 23.08% to 26.45%, therefore:
Why does this step already include mix?
Because A is a high-margin product, while B is a low-margin product.
Compared with Forecast, Actual shows:
- A's units increase from 100 to 140
- B's units decrease from 200 to 170
This means the overall sales structure shifts toward higher-margin Product A.
So this step reflects not only "selling more or less," but also "what products were sold." That is exactly why, in this kind of detailed model, mix effect is already reflected in the volume step.
In this step, based on Step 1, Unit Price is also replaced with Actual, while Cost remains at Forecast values.
| Product | Units | Unit Price | Unit Cost | Revenue | Profit | Margin % |
|---|---|---|---|---|---|---|
| A | 140 | 9.5 | 6.0 | 1,330 | 490 | 36.84% |
| B | 170 | 8.4 | 7.0 | 1,428 | 238 | 16.67% |
| Total | 310 | — | — | 2,758 | 728 | 26.40% |
Total Margin % changes from 26.45% to 26.40%, therefore:
Intuitive explanation
Finally, Unit Cost is also replaced with Actual. This fully reaches the Actual state.
| Product | Units | Unit Price | Unit Cost | Revenue | Profit | Margin % |
|---|---|---|---|---|---|---|
| A | 140 | 9.5 | 6.2 | 1,330 | 462 | 34.74% |
| B | 170 | 8.4 | 6.8 | 1,428 | 272 | 19.05% |
| Total | 310 | — | — | 2,758 | 734 | 26.61% |
Total Margin % changes from 26.40% to 26.61%, therefore:
Intuitive explanation
IV. Final Bridge Result
| Step | Margin % | Contribution |
|---|---|---|
| Forecast baseline | 23.08% | — |
| After Volume (incl. mix) | 26.45% | +3.37pp |
| After Price | 26.40% | −0.05pp |
| After Cost | 26.61% | +0.22pp |
| Total change | 26.61% − 23.08% | +3.54pp |
Verification:
+3.37pp − 0.05pp + 0.22pp = +3.54pp ✓
This matches the total change exactly.
That is precisely the value of stepwise replacement: each step is traceable, and the total will always reconcile.
Instructions
Using the WaterfallBridge tool requires Excel 2016 or higher, preferably the latest Microsoft 365 version. Older versions may cause the program to malfunction.
The following instructions use a "Electronics Stores" virtual dataset to demonstrate how to use the WaterfallBridge tool to automatically generate Bridge analysis and identify key driving factors.
Download Excel ExampleThe initial panel displays two buttons: "New Bridge File" and "Open Existing Bridge File"
1.1 Click "New Bridge File" button - Generates a new Excel file containing a new "Data" worksheet. Please enter the corresponding data in the "Data" worksheet for further analysis.
1.2 Click "Open Existing Bridge File" button - Select a previously used Excel file. This file should contain the relevant data and have Data Types configured.
In the Data worksheet starting from column B, enter data types in the first row, field names in the second row, and data in all rows from the third row onwards. Column A contains auto-generated identifiers for the first three rows that need data input. Column A itself has no impact on data and analysis.
In the first row of the "Data" worksheet, use the generated dropdown menu starting from column B to select the data type. The type depends on the meaning the data needs to represent. The program will automatically analyze based on the data type.
Data types are divided into 5 categories: Dimension, Key, SumY, SumN, Result. Each field must be defined with one data type, which can be selected using the dropdown menu.
Now using our simulated supermarket data as an example, let's explain the meaning of each data type. Each type is defined as follows:
For example: If "Product Name" is selected as Key, the tool will calculate the impact of each variable's change on the final result (Result) within this "Product Name" dimension.
For example: Quantity of different products in each row can be summed, and the total represents the total quantity of goods. Therefore, the Type of Quantity can be set to SumY.
SumY can contain direct numerical values or use formulas to calculate values through other fields. However, formulas can only use Excel's basic arithmetic operators: addition (+), subtraction (-), multiplication (*), division (/), left parenthesis "(", and right parenthesis ")". Formula Usage Guidelines
For example: Different products have different Prices. To calculate the average Price of all products, you cannot directly add Product A's Price plus Product B's Price to get the overall Price. Therefore, the Type of Price can be set to SumN.
SumN can contain direct numerical values or use formulas to calculate values through other fields. However, formulas can only use Excel's basic arithmetic operators: addition (+), subtraction (-), multiplication (*), division (/), left parenthesis "(", and right parenthesis ")". Formula Usage Guidelines
For example: If the Result field contains sales, then the cell should input = Price * Quantity. Or if it's margin%, then the cell should input = profit / revenue.
The formula in Result is crucial information that the tool uses to associate variables with results and analysis logic dependencies. Please ensure that each Result cell in every row contains a formula that can calculate the Result. The formula should be concise and clear. Result will analyze the impact of each variable in the formula on the Result, while variables not in the formula are not within the analysis scope. The Result formula should contain at least one field with Data Type SumY to enable aggregated analysis of data from different rows.
Result formulas can only use Excel's basic arithmetic operators: addition (+), subtraction (-), multiplication (*), division (/), left parenthesis "(", and right parenthesis ")". Formula Usage Guidelines
In the second row of the "Data" worksheet starting from column B to the right, enter field names. The meaning of the field names should correspond to the data types in the first row. Field names cannot be duplicated.
In the "Data" worksheet starting from the third row in column B, input specific data downwards.
After clicking "New Bridge File" or "Open Existing Bridge File" to open a file, blue buttons New Bridge and Refresh Bridge will appear on the panel.
3.1 New Bridge button - Generates new dropdown menus and Bridge worksheets and other analysis frameworks Baselined on the Data worksheet data for subsequent data analysis. (Note: Related worksheets such as Bridge worksheets and corresponding data in the original workbook will be deleted.) If there are any changes to the Data worksheet data, you must first click the "New Bridge" button to generate a new analysis framework, then: 1. Make selections in the new Baseline and Comparison dropdown menus; 2. Click the "Refresh Bridge" button to regenerate the required Bridge charts and Contribution Analysis.
3.2 Refresh Bridge button - After selecting options in the Baseline and Comparison dropdown menus according to analysis needs, click the "Refresh Bridge" button to generate the required Bridge charts and Contribution Analysis. Missing data or worksheets will trigger prompts; it's recommended to click New Bridge to regenerate a new Bridge.
The dropdown menu is divided into two areas: Baseline and Comparison
The fields in Baseline and Comparison dropdown menus contain exactly the same options. Users can filter which options serve as Baseline data and which serve as Comparison data in the dropdown menus, using these as the comparison and analysis data for both ends of the Bridge. Note: Initially, by default, all fields in the Baseline and Comparison dropdown menus are selected, so Baseline and Comparison get exactly the same dataset, resulting in a Bridge with no differences.
4.1 Baseline dropdown menu - Select the baseline data for comparison analysis. For example, we often use data from certain years as the comparison baseline, so here we select 2023.
4.2 Comparison dropdown menu - Select the Comparison data for comparison analysis. For example, here we select 2024 as the year of interest to compare with 2023.
The Bridge worksheet displays the final analysis results, including Bridge charts and Contribution Analysis tables.
5.1 Bridge Data Source - Starting from cell B3 in the upper left corner, field names and data will appear. This is the data used to generate Bridge charts.
5.2 Bridge Chart - Displays the Bridge analyzed and obtained after program execution. The blue bar on the left is Baseline, the middle shows the impact contributed by each variable, and the right bar is Comparison.
5.3 Contribution Analysis Table - Directly below the Bridge chart, a Contribution Analysis table will be displayed, showing the contribution of each Item in the Key field to overall changes under different factors.
Through the Bridge Chart and Contribution Analysis Table, you can quickly identify the main Items (SKUs) affecting changes and the factors causing changes (price? cost? or quantity mix?).
You can analyze different dimensions and different result data by modifying 2.1 Data Type Selection to choose which field is Key and which field is Result. You can also use the dropdown menus to filter which data serves as Baseline and Comparison, then click Refresh Bridge to generate updated Bridge charts and Contribution Analysis tables for analyzing actual business situations and proposing improvement suggestions.
6. SumY and SumN Calculation Rules
In the first row Data Type of the Data worksheet, numerical values are divided into two types: SumY and SumN, and are used in formulas in the Result column. To correctly analyze the data, we define some calculation rules for SumY and SumN:
SumY + SumY = SumY
SumY + SumN (will show error)
SumN + SumN = SumN
SumY * SumY = SumY
SumY * SumN = SumY
SumN * SumN = SumN
SumY / SumY = SumN
SumY / SumN = SumY
SumN / SumY = SumN
SumN / SumN = SumN
Use Same Formula for Each Column: The program will automatically copy the formula from the fourth row to the entire column after removing fixed cell reference symbols "$" to ensure all formulas in each column are identical.
6.4.1 In formulas, if parentheses are included, calculations inside parentheses are performed first according to the order of operations, and the result is used as a value to continue calculations using the above rules.
Example: (SumY + SumY) * SumN
Step 1: SumY + SumY = SumY
Step 2: SumY * SumN = SumY
Final result type: SumY
6.4.2 If the Result column formula references other cells that also contain formulas, the analysis will merge all nested formulas into one formula for analysis.
Example:
Column E formula: =B*C (Total Cost = Quantity * Unit Cost)
Column F Result formula: =D-E (Profit = Revenue - Total Cost)
After merging: The program will analyze =D-(B*C)
6.4.3 The program will automatically reorder variables and apply the distributive property based on formula structure for better data analysis.
Original formula: =SumY*(SumN1 + SumN2)
After processing: =SumY*SumN1 + SumY*SumN2
This helps analyze the individual contributions of SumY, SumN1, and SumN2 more clearly.
7. Process Worksheet
The Process worksheet contains the logic and algorithms for generating Bridge charts and data. You can intuitively see how these analysis results are calculated step by step, avoiding black box and inexplicable phenomena.
Bridge Analysis Calculation Logic and Principles
All variables are based on Baseline initial values. Each step changes one variable to the Comparison value and calculates the impact on each Key Item.
Example: Product A sales analysis
Baseline: Price=10, Quantity=100, Sales=1000
Comparison: Price=12, Quantity=80, Sales=960
Step 1: Only change Price: 10→12, Quantity remains 100, Sales=1200
Step 2: Then change Quantity: Price=12, Quantity=100→80, Sales=960
Calculate the impact of each variable change on the summary
Price Impact: 1200 - 1000 = +200
Quantity Impact: 960 - 1200 = -240
Total Change: +200 + (-240) = -40
Verification: 960 - 1000 = -40 ✓
After steps 7.1 to 7.2, replacing all variables from Baseline values to Comparison values, we obtain the impact of each influencing factor needed for the Bridge chart.
In the Contribution calculation table, we calculate the impact of each Key on the overall change under each factor variation.
Overall Price Impact: +500
Product A Contribution: +200 (40%)
Product B Contribution: +180 (36%)
Product C Contribution: +120 (24%)
Added Data represents newly added data compared to Baseline data (existing in Comparison data). Removed Data represents data deleted from Baseline data (not existing in Comparison). These two types of data generally need to be separated from other factor analysis first.
Scenario: Product mix change analysis
Baseline Data: Products A, B, C
Comparison Data: Products A, B, D (added D, removed C)
Added Data: Product D's contribution
Removed Data: Product C's impact (negative contribution)
These changes will be displayed separately, not mixed with price, quantity and other factors.
8. Other Worksheets
For the correct operation of the program, some other worksheets will be generated but hidden. Please do not modify these worksheets.
9. Data Security Assurance
All data exists only in local Excel files. The program runs only locally with no network communication during the analysis process.
10. Transparent & Explainable Analysis
Reject the ambiguous conclusions and inexplicability of AI analysis. The analysis calculation process is completely transparent and explainable.
Process Worksheet Transparency
Contains the logic and algorithms for generating Bridge charts and data. Every calculation step can be traced and verified.
Traditional Black Box Analysis:
❌ "AI model shows 7pt profit margin decline"
❌ Cannot explain specific calculation process
❌ Cannot verify result accuracy
Bridge Transparent Analysis:
✅ "Price factor: -3pt, Cost factor: -2pt, Quantity factor: -2pt"
✅ Every calculation step visible in Process worksheet
✅ All formulas and logic completely verifiable
Local Data Security
Zero network communication, all data processing completed locally, ensuring absolute enterprise data security.
Data Security Guarantee:
✅ Data never leaves local computer
✅ No need to upload to cloud servers
✅ No network connection required
✅ Complies with enterprise data security policies
✅ Avoids data breach risks
Precise Mathematical Calculation
Based on rigorous mathematical formulas, ensuring accuracy and reliability of analysis results.
Traditional Estimation Analysis:
❌ Manual estimation with potential errors
❌ Cannot precisely quantify impact of each factor
❌ Aggregated results require manual adjustment
Bridge Precise Calculation:
✅ Mathematical formulas guarantee 100% accuracy
✅ Each factor's contribution precise to decimal places
✅ Totals automatically balance, no adjustment needed
Efficient Analysis
Complete complex analysis that traditionally takes days in just minutes, dramatically improving work efficiency.
Multi-dimensional Analysis
Support in-depth driving factor analysis by product, region, time, and other dimensions.
Flexible Configuration
Adjust analysis dimensions and comparison scenarios anytime to meet various business analysis needs.