The Analyst Agent accepts instructions in natural language to undertake detailed analysis of your data. The following examples are for you to copy and adapt for your needs.
Explain OPEX Variance by General Ledger Account and Department
|
|---|
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
|
Identify Drivers of Revenue Underperformance
|
|---|
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.
|
|
|
Investigate cash shortfalls through working capital and collections
|
|---|
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.
|
|
|
|
Analyze staffing gaps
|
|---|
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.
|
FTE Analysis
|
|---|
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.
|
|
Detect and Explain Attrition Trends
|
|---|
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
|
|