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Dataset research report

Ecommerce Funnel research report

A reproducible data report with schema notes, generated chart evidence, suggested follow-up questions, and export-ready Helix queries.

storageSample descriptionecommerce-funnel.csv view_list120 rows

Executive Summary

Ecommerce conversion funnel by cohort, channel, and device for testing funnels, Sankey-style flows, cohort trends, grouped bars, and revenue analysis.

Finding 1The dataset has 120 rows available in the catalog.
Finding 2The catalog exposes 11 documented or inferred columns.
Finding 3Helix has 6 ready query prompts for this dataset.
Finding 4This report includes 6 generated chart views.

Research Context

Ecommerce Funnel: 120 rows by 11 columns. These exploratory charts are generated automatically from the data - open the dataset in Helix to ask your own questions.

Data Profile

Rows120
Columns11
Numeric cols8
Categorical cols2
Date range2025-01-01 → 2025-12-01

Chart Evidence

These views are generated from the dataset profile. Each chart is paired with a Helix query so it can be opened, adjusted, and exported.

Follow-Up Queries

Preview Rows

# cohort_monthtext channeltext devicetext visitorsinteger signupsinteger trials_startedinteger purchasesinteger revenue_usdinteger
1 2025-01Paid Searchdesktop18500111051017113851
2 2025-01Paid Searchmobile1940010084631419729
3 2025-01Organic Searchdesktop21200139960819317177
4 2025-01Organic Searchmobile22100128155716012320
5 2025-01Emaildesktop23900172076522922213
6 2025-01Emailmobile24800158770619016150

Data Dictionary

  • cohort_month date Monthly acquisition cohort
  • channel categorical Acquisition channel
  • device categorical Visitor device class
  • visitors int Top-of-funnel visitors
  • signups int Visitors who created an account
  • trials_started int Signups who started a trial
  • purchases int Trials converted to purchases
  • revenue_usd float Revenue generated in USD
  • signup_rate_pct float Visitor-to-signup conversion rate
  • trial_to_purchase_pct float Trial-to-purchase conversion rate
  • avg_order_value_usd float Average order value in USD

Method And Limits

  • Load the catalog entry and preview rows from the processed dataset file.
  • Infer numeric, categorical, time, and location fields from real columns.
  • Generate a small set of defensive Plotly chart specifications from that profile.
  • Expose each chart idea as a query link so the report can be rerun or exported in Helix.

This report is intentionally reproducible. It uses the local catalog metadata and generated chart specifications rather than claiming external conclusions beyond the dataset.

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