This generates a structured framework for analyzing operational expenses. It helps you:

• Compare OPEX actuals vs. budget at a global level and spot top cost centers driving variances

• Flag significant deviations by GL Account × Department where variances exceed ±5k

• Drill down further by country and highlight both absolute (€/$) and percentage (%) differences

• Present results clearly using bullets for short lists or tables for longer ones

• Add concise explanations to contextualize each variance for clarity

It’s designed to ensure consistent, insightful, and well-formatted financial analysis outputs.

Analysis

Start by analyzing the @OPEX metric, compare @Version Actuals vs Budget at global level for the @Current_Month. Identify the top 3 @Cost_centers contributing to the total variance.

Breakdown by @GL Account x @Department and flag only combinations where the absolute variance between @Versions Actuals – Budget exceeds ±5000

For each flagged combination:

• Drill down further by @Country
• Show variance in both Absolute terms (€) and Percentage (%)

Formatting

• Use bullet points to list key GLs and their associated variances. Add short explanations where possible.
• If there are more than 5 items, use a table instead.

Output Format Example

• GL 60000 - Salaries: +€15.2k vs Budget (+18%) - Higher training expenses in Marketing (+€8.7k)
• GL 62100 - Travel: –€11.3k vs Budget (–9%) - Fewer offsites held this quarter

Unlock a clear view of quarterly revenue performance with this analysis example. It enables you to:

• Compare actual revenue against forecasts for the last quarter

• Pinpoint the top 3 underperforming country–retailer combinations

• Drill down to SKU level to surface the main variance contributors

• Summarize findings in a concise executive overview before detailing key drivers

• Present results in a formal quarterly business review tone—with emojis for emphasis

It’s built to deliver structured, insightful, and engaging revenue variance analysis.

Analysis

Analyze @Revenue metric, compare @Version Actual vs Forecast for the @Last_Quarter

• Identify the top 3 @Countries - @Retailers that have underperformed.
• For each flagged combination, drill at @SKU level to identify the top 3 contributors of the variance.

Formatting

• Start with a short executive summary (2-3 lines).
• Then list the key drivers of the variance using bullet points.
• Use a formal quarterly business review tone, with emojis.

Get a comprehensive view of profitability versus liquidity with this analysis guide. It helps you:

• Compare actual EBITDA and cash position against forecast and budget to spot discrepancies

• Correlate Metrics to uncover operational, structural, or investment-driven misalignments

• Track working capital drifts and efficiency ratios (DSO/DPO) to assess cash pressure by country

• Break down cash flows by source to identify unusual movements in CAPEX or collections

• Anticipate future stress from forecasted CAPEX and recommend phased or financed strategies

It’s designed to deliver a structured report with clear conclusions and actionable recommendations on EBITDA–cash alignment.

• Analyse @EBITDA and do a variance analysis between @Versions Forecast and Budget to identify unexpected discrepancies.
• Do a variance analysis as well for @Cash_Position
• Compare variances from the 2 metrics. Identify discrepancies in direction.
• Do a variance analysis for @Working_Capital and compare to the variances observed in @Cash_Position. Filter on @B/S accounts Working Capital. Increasing Working capital may explain reduced cash generation even with stable EBITDA.
• Compare @CAPEX variances to the variances observed in @Cash_Position. If large CAPEX has been paid in cash, recommend financing solutions. Anticipate future stress from forecasted CAPEX at @country level.

Gain clear visibility into organizational staffing gaps with this analysis approach. It guides you to:

• Compare total headcount demand versus supply at a global level, highlighting absolute and percentage gaps

• Identify the entity with the largest shortfalls and zoom into the top 3 cost centers driving them

• Break down gaps further by job profile within each flagged cost center

• Surface any additional critical shortages across the model where gaps exceed 30%

• Present results in structured tables and pair findings with actionable recommendations (open positions, transfers, or scope adjustments)

It’s built to deliver a precise and actionable view of workforce capacity gaps for the current month.

Analysis

Compare @Headcount_Demand and @Headcount_Supply to identify the most critical staffing gaps across the organization for the month of @Current_Month.

Global overview to compare total demand vs. supply (absolute and % gap).

• Identify the main @Entity with the largest negative gaps (Demand > Supply).
• Then for each @Entity, zoom into the top 3 @Cost_Center with the largest shortfalls.
• Then, within each flagged @Cost_Center, list all the @Job_Profile with a negative gap.
• Finally, scan the full model to flag additional critical gaps (Gap > 30%) not covered before.

Output format

Use structured tables with the following columns: Entity | Cost Center | Job Profile | Demand | Supply | Gap | Gap % 

Recommendations

For largest gaps, suggest potential actions: open positions, internal transfers, or scope adjustments.

Track workforce evolution and role dynamics with this structured analysis. It enables you to:

• Compare FTE year-on-year by country, showing values and YoY growth % in a clear table format

• Aggregate results with country rows and yearly totals for a global perspective

• Highlight the top 5 country–role combinations with the largest shifts in distribution

• Use arrow emojis (⬆️/⬇️) to quickly signal increases or decreases in headcount

It’s designed to deliver a concise, data-driven view of staffing evolution across countries and roles.

Analysis

Part 1: FTE evolution

• Analyze @FTE, do a Variance analysis on @Year compare @CurrentYear to @PreviousYear
• Breakdown by @Country and @Department

Part 2: Roles distribution evolution

• List the top 5 @Roles - @Country combinations with the highest variations.
• Use up and down arrow emojis to indicate increases or decreases.

Uncover key attrition risks and workforce trends with this analysis framework. It allows you to:

• Identify the top 3 roles with the highest attrition over the past year

• Correlate attrition with performance, tenure, and location to surface deeper insights

• Start with a concise executive summary on global attrition patterns

• Present results in a structured table (Role | Attrition % | HC Lost | Trend 📈/📉)

• Add focused bullet points under each role to explain the drivers behind attrition

It’s designed to provide a clear, insight-rich view of attrition dynamics without relying on charts.

Analysis

For the whole analysis, filter on @CurrentYear

1. Identify the Top 3 @Employee_Role with the highest attrition using @Attrition metric
2. For each of these flagged roles, provide additional insights by correlating @Attrition to:

• @Attrition_Performance → check if high performers or low performers are leaving.
• @Attrition_Tenure → check if attrition is concentrated among junior or senior staff.
• @Attrition_Location → see if attrition is localized in specific geographies.

Formatting

• Begin with an executive summary (1–2 sentences) on global attrition trend.
• Present the Top 3 Roles with attrition in a table: Role | Attrition % | HC Lost | Trend (📈/📉).
• Add bullet points under each role summarizing key insights
• Don't display charts