Data Analysis & Dashboard Generation
Analyzes engagement data to calculate ROI and LTV trends, then generates an interactive Excel dashboard with recommendations and a...
What it does
You gain clear insights into campaign ROI and customer lifetime value trends through an interactive Excel dashboard that visualizes your engagement data. The agent also provides actionable recommendations and prepares an executive summary presentation for your marketing team to streamline decision-making.
Step by step
- Parse the raw CSV or JSON data into a structured pandas DataFrame for consistent processing.
- Calculate standard metrics like ROI and CLV using defined mathematical formulas without ambiguity.
- Generate the Excel file with predefined chart types and cell formatting rules using openpyxl.
Where the LLM does the work
- Interpret complex data trends to write concise, actionable recommendations for each channel.
- Draft the executive summary text that translates technical metrics into business insights.
- Decide which specific visualizations best highlight anomalies or key performance shifts in the data.
Watch out for
Large datasets can exceed Excel row limits so you must aggregate data before writing to the sheet.
Excel formatting styles vary across operating systems so test the generated file on the target platform before distribution.
Ambiguous metric definitions can lead to inconsistent ROI calculations so standardize formulas in code rather than relying on LLM logic.
Tools that fit
The agent brief
Everything your agent needs, including the gotchas. Copy it and go.
You are helping me build the following AI agent workflow. ## Goal Data Analysis & Dashboard Generation: Analyzes engagement data to calculate ROI and LTV trends, then generates an interactive Excel dashboard with recommendations and a... ## Specification - What it does: You gain clear insights into campaign ROI and customer lifetime value trends through an interactive Excel dashboard that visualizes your engagement data. The agent also provides actionable recommendations and prepares an executive summary presentation for your marketing team to streamline decision-making. - Trigger: Run manually (Manual · on demand) - Autonomy: Fully hands-off - Expected setup effort: about an afternoon - Tools/services involved: - LLM: Analyze data and generate insights - Excel: Create interactive dashboard ## Known pitfalls, handle each one explicitly in your implementation 1. Large datasets can exceed Excel row limits so you must aggregate data before writing to the sheet. 2. Excel formatting styles vary across operating systems so test the generated file on the target platform before distribution. 3. Ambiguous metric definitions can lead to inconsistent ROI calculations so standardize formulas in code rather than relying on LLM logic. ## Reference implementation https://www.youtube.com/watch?v=USRfRv34HmQ (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/data-analysis-dashboard-generation/ via usecasesforagents.com
Frequently asked questions
Can I use a different Spreadsheet than Excel?
Yes. Excel is only the example spreadsheet in this recipe. The same flow works with Airtable, Baserow and Google Sheets. Swap the spreadsheet connection and keep the rest of the setup as written.
Want this running in your business?
This is what I do. I design and build AI agents like this one, and keep them running. If you want it set up for your team instead of doing it yourself, get in touch.
Who it's for
Built for data analysts who want the pull-and-summarize grind automated.
Marketers can point this at their own content pipeline with no changes to the pattern.