CASE STUDY
Boosting E-commerce Retention Through Unified Revenue Analytics
Industry: E-commerce (Beauty & Lifestyle)
Solution Area: Customer Retention & Revenue Analytics
Client: Shopify-based D2C Brand (via WebEngage Integration)
Tools Used: Power BI, Fivetran, BigQuery, Shopify, WebEngage

Business Impact
14% Retention Gap Identified
Enabled targeted re-engagement and repeat-purchase campaigns.
Smarter Discounting
ROI clarity on coupon codes cut underperformers and boosted profitable schemes.
40% Faster Insights
Automated pipeline via Fivetran + BigQuery eliminated manual data refreshes.
The Challenge
The client, a fast-growing D2C beauty and lifestyle brand on Shopify, struggled to monitor revenue trends, customer retention, and discount effectiveness. Data was scattered across Shopify, WebEngage, and marketing platforms, making it difficult to answer:
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How are revenue, orders, and AOV trending over time?
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Which products, coupons, and marketing channels drive repeat purchases?
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What’s the real retention rate by cohorts such as acquisition month or source channel?
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Fragmented reports led to slow, reactive decisions and limited visibility into true customer value.
Our Solution
GrowthBI built a centralized Power BI dashboard powered by a Fivetran → BigQuery → Power BI data stack, integrating Shopify orders and WebEngage campaign data for unified retention and revenue analytics.
Key Features:
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Automated Data Pipeline: Fivetran extracted Shopify and WebEngage data into BigQuery for seamless refresh.
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Centralized Data Model: Unified schema for orders, customers, and coupons designed for cohort and retention tracking.
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Performance KPIs: Revenue, orders, AOV, discounts, and new vs. repeat customer metrics.
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Product & Coupon Insights: Measured revenue lift from top-performing codes (e.g., B1G1, B2G2, FLAT40).
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Customer Value Cohorts: CV tracked across 1/30/60/90/180 days segmented by channel and discount.
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Retention & Repeat Analytics: Captured time intervals between purchases and repeat purchase percentages.
Dashboard Walkthrough
1. Performance Summary – Tracks revenue, AOV, and customer split (new vs. repeat) with period comparisons.
2. Product & Coupon Insights – Identifies top products and evaluates coupon ROI.
3. Customer Value Cohorts – Measures CV over time by channel and discount type.
4. Retention Analysis – Shows intervals between 1st, 2nd, 3rd, and 4th orders with repeat rate trends.
5. Cohort Analysis – Retention and churn by acquisition month, channel, and geography (sample query below)

Technical Architecture & Process
Data Flow
Shopify + WebEngage → Fivetran → BigQuery (Data Modelling) → Power BI (Visualization & Cohort Reports)
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Tech Stack

Automated sync from Shopify and WebEngage.
Central warehouse for order, cohort, and retention models.
Interactive dashboards with dynamic retention visualizations.
This SQL script builds a cohort retention model that tracks customers from their first purchase month and measures cumulative revenue (CV) and activity rates at 30-, 60-, 90-, and 180-day intervals. It helps identify how quickly new cohorts generate revenue and how long they stay active, providing a clear view of customer retention and lifetime value trends.
COHORT_RETENTION (by month of first purchase): day-0/30/60/90/180 CV and retention steps
CREATE OR REPLACE TABLE mart.f_cohort_retention AS
WITH firsts AS (
SELECT customer_id, MIN(DATE(created_at)) AS first_order_date
FROM mart_stg.stg_orders
GROUP BY customer_id
),
orders AS (
SELECT
o.customer_id,
DATE(o.created_at) AS order_date,
(o.order_total_gross - o.order_discount_amount) AS net_rev
FROM mart_stg.stg_orders o
),
joined AS (
SELECT
f.customer_id,
DATE_TRUNC(f.first_order_date, MONTH) AS cohort_month,
o.order_date,
DATE_DIFF(o.order_date, f.first_order_date, DAY) AS day_since_first,
o.net_rev
FROM firsts f
JOIN orders o USING (customer_id)
)
SELECT
cohort_month,
COUNT(DISTINCT customer_id) AS customers_in_cohort,
SUM(CASE WHEN day_since_first BETWEEN 0 AND 0 THEN net_rev END) AS cv_1d,
SUM(CASE WHEN day_since_first BETWEEN 0 AND 30 THEN net_rev END) AS cv_30d,
SUM(CASE WHEN day_since_first BETWEEN 0 AND 60 THEN net_rev END) AS cv_60d,
SUM(CASE WHEN day_since_first BETWEEN 0 AND 90 THEN net_rev END) AS cv_90d,
SUM(CASE WHEN day_since_first BETWEEN 0 AND 180 THEN net_rev END) AS cv_180d,
SUM(CASE WHEN day_since_first BETWEEN 0 AND 30 THEN 1 END) /
NULLIF(COUNT(DISTINCT customer_id),0) AS activity_rate_30d,
SUM(CASE WHEN day_since_first BETWEEN 31 AND 90 THEN 1 END) /
NULLIF(COUNT(DISTINCT customer_id),0) AS activity_rate_31_90d
FROM joined
GROUP BY cohort_month
ORDER BY cohort_month;
Conclusion
The E-commerce Retention Dashboard unified fragmented Shopify and WebEngage data into a single source of truth. Automated pipelines and intuitive Power BI visuals empowered leadership to act quickly on customer retention, discount strategies, and acquisition channel optimization—driving sustainable growth and smarter marketing spend.

