Data Insight Engine
Connect multi-source data, auto-clean & model, and generate visual reports that power data-driven decisions.
What Is Data Insight Engine?
Data Insight Engine is an advanced statistical analysis and data visualization skill on EasyClaw. Designed to help analysts and developers extract deep, actionable insights from raw business metrics, it connects to multi-source datasets to perform complex analytics operations — including cohort retention heatmaps, A/B test significance testing (calculating confidence intervals and p-values), and weekly GMV trend reporting, complete with professional visual charts and executive summaries.
The skill is built for growth marketers running conversion tests, product analysts mapping retention trends, and finance managers compiling performance reviews.
The expected outcome is a structured analytical report: featuring clean statistical summaries, cohort heatmaps, visual charts (rendered in high resolution), and direct, executive-ready interpretations that explain exactly what the data means for your next strategic move.
How Data Insight Engine Works
1. Define target dataset and analysis type. Provide your raw metrics (such as SQL tables or CSV logs) and choose your analytical task (e.g., cohort retention, A/B testing, or trend line charting).
2. Data cleanup and cohort structuring. For retention requests, the skill parses active customer signups and purchase dates, grouping them into weekly or monthly cohorts, and projects retention percentages into a standard cohort heatmap matrix.
3. A/B significance calculation. For conversion tests, the skill processes raw sample volumes and conversions. It runs statistical significance calculations: computing the conversion lift, p-value, and 95% Confidence Interval (CI) to determine if the test outperformed the baseline.
4. Data trend charting. For performance queries, the skill extracts the required time-series data, aggregates metrics by week or quarter, and calls local rendering libraries to generate high-resolution line and bar charts.
5. Executive summary compilation. It translates statistical metrics into executive-ready bullet points, outlining the key performance drivers and action recommendations in clear, non-technical business terms.
Key Features
- Cohort retention heatmaps: Groups signup dates and subsequent purchases into clean weekly or monthly retention matrices.
- A/B significance calculator: Computes lift, p-values, and 95% confidence intervals to validate test outcomes.
- Dynamic trend line charting: Generates high-resolution time-series charts to plot GMV or user growth trends.
- 3-line executive summaries: Automatically compiles brief, high-impact summaries designed for executive review slides.
- SQL data aggregation: Pulls and organizes data from relational tables before running statistical calculations.
- Clean Markdown formatting: Outputs reports with clear data tables and embedded chart previews.
What Problems Does Data Insight Engine Solve?
1. Generating a weekly cohort retention heatmap
A product manager wants to analyze how user retention varies between marketing channels. They provide a raw user signup and order log. Data Insight Engine parses the data, groups users into weekly cohorts based on signup dates, calculates subsequent purchase percentages, and renders a clean, color-graded cohort heatmap table, immediately highlighting the highest-retaining channels.
2. Validating an A/B test conversion lift
A growth marketer runs an A/B test on 50,000 users to test a new checkout button. Control: 25,000 samples, 1,200 conversions. Variant: 25,000 samples, 1,350 conversions. The skill calculates: Control CVR 4.8%, Variant CVR 5.4%, conversion lift +12.5%, p-value 0.002, 95% CI [2.1%, 22.9%]. The p-value being under 0.05 confirms the lift is statistically significant, validating the new checkout button layout.
3. Compiling quarterly GMV reports with executive summaries
A finance manager needs to prepare a Q1 performance slide. They paste raw order logs into EasyClaw. The skill pulls quarterly GMV via SQL, aggregates the revenue weekly, renders a clean trend line chart, and adds a high-impact, 3-line executive summary outlining the Q1 growth inflection points, ready to paste straight into PowerPoint.
4. Analyzing user behavior trends
An analyst wants to map user login frequencies to identify drop-off patterns. The skill aggregates raw login events, filters out bots, and plots a user activity distribution chart, showing clearly at what day of the week user activity peaks and drops.
5. Statistical outlier detection
Before running a pricing model, a data analyst wants to identify and remove skewed order values. The skill runs standard deviation checks over the transaction dataset, flagging and filtering out statistical outliers to ensure clean, representative modeling.
Example Workflow
A marketer wants to calculate the significance of an A/B test they ran on a landing page design.
1. They open EasyClaw and activate Data Insight Engine.
2. They run: *"Analyze our A/B test conversions. Control size 12,000, 480 conversions. Variant size 12,000, 560 conversions."*
3. The skill runs the statistical formulas, calculating conversion rates, lift, and p-values.
4. It outputs the structured A/B Test Report:
- Control CVR: 4.0% | Variant CVR: 4.67%.
- Conversion Lift: +16.7%.
- p-Value: 0.012 (Statistically Significant).
- 95% Confidence Interval: [3.5%, 29.8%].
- Recommendation: Deploy the Variant design immediately; the lift is validated.
5. The marketer saves the report to their test database.
Total significance validation time: under 30 seconds.
Getting Started with Data Insight Engine
Connect & Clean — Auto-link SQL, sheets, BI tools and handle missing or outlier values.
Define & Validate KPIs — Apply built-in KPI contract templates to keep metrics consistent and reusable.
Smart Visuals — Match the best chart to your data in one click and avoid anti-patterns.
Exec Brief — Deliver C-suite-ready reports with confidence intervals and effect sizes.
Core Features
Connect & Clean
Auto-link SQL, sheets, BI tools and handle missing or outlier values.
Define & Validate KPIs
Apply built-in KPI contract templates to keep metrics consistent and reusable.
Smart Visuals
Match the best chart to your data in one click and avoid anti-patterns.
Exec Brief
Deliver C-suite-ready reports with confidence intervals and effect sizes.
Frequently Asked Questions
What is a p-value and why does it matter?
In A/B testing, the p-value is the probability that the observed conversion lift occurred by random chance. A p-value under 0.05 is the industry standard threshold, meaning there is less than a 5% chance the lift was accidental, confirming the test is statistically significant.
What data format is required for cohort retention heatmaps?
You need a list or table containing two key date fields for each customer record: their original Signup/Registration Date (to place them in a cohort) and their subsequent Purchase/Order Dates (to calculate retention over intervals).
Does this skill connect directly to my SQL database?
The skill provides advanced data manipulation, statistics calculations, and SQL query generation based on the schemas and metrics you input. It does not query your live databases directly, protecting your network security.
What is a 95% Confidence Interval (CI)?
The 95% Confidence Interval is the range within which the true conversion lift is projected to fall 95% of the time. If the interval is [2% to 15%], you can be highly confident that the variant design will deliver at least a 2% lift when deployed.
Can I generate the cohort charts as image files?
Yes. The skill renders cohort tables as beautiful Markdown matrices inside your chat window, and can also compile them as high-resolution PNG image charts saved directly to your workspace exports directory.
How does the engine handle statistical outliers?
The skill runs standard deviation filtering (e.g., identifying values beyond 3 standard deviations from the median) to locate and exclude statistical outliers, preventing skewed metrics on transaction averages.
Can I calculate retention on subscription models?
Yes. The cohort logic can be calibrated to subscription renewal intervals (e.g., month-over-month renewals) to map out accurate churn curves for SaaS or subscription box programs.
Does the skill support multi-variant A/B/C testing?
Yes. The significance engine can run pairwise comparisons across multiple variants (A/B/C testing) and apply Bonferroni correction adjustments to prevent false-positive inflation across multiple comparisons.
What is the TaskFlow API?
It is the modern, decorator-driven Python syntax used in Apache Airflow 2.x, which this skill can generate to automate the scheduling of your data-gathering pipelines.
Are my transaction logs kept private?
Yes. All data processing, cohort calculations, and statistical testing are executed locally inside your workspace session. No customer data or transaction records are ever shared or stored on public servers.
Browse more in General Tools or all skills.
Add Data Insight Engine to Your Workflow
Get EasyClaw, add this skill, and start building AI agent workflows in minutes.
Get EasyClaw Free →