Data Analytics for Digital Marketing — India 2026

Why Marketing Analytics Is a Key Domain for Indian Analysts

India's digital advertising market crossed ₹35,000 crore in 2025 and continues to grow at 15–20% annually. E-commerce, edtech, fintech, and D2C brands collectively run thousands of campaigns simultaneously across Meta, Google, OTT platforms, and influencer channels. Every rupee spent needs to be tracked, attributed, and optimised.

Data analysts who understand marketing metrics and can connect ad spend data to revenue outcomes are in high demand across Indian startups and growth-stage companies in Gurugram, Noida, Bengaluru, and Mumbai.

Core Marketing Metrics Every Analyst Must Know

Marketing Attribution Models — Which to Use and When

Attribution answers: "which marketing touchpoints deserve credit for this conversion?" The model you choose changes how you allocate budget — which is why marketing managers and analysts often disagree.

Funnel Analysis — SQL Pattern for Indian E-Commerce

Funnel analysis measures drop-off at each step of the customer journey. For Indian e-commerce, the typical funnel is: Visit → Product View → Add to Cart → Checkout → Payment → Order Confirmed.

-- Marketing funnel analysis by acquisition channel
WITH funnel AS (
    SELECT
        user_id,
        acquisition_channel,
        MAX(CASE WHEN event_name = 'product_view'    THEN 1 ELSE 0 END) AS product_viewed,
        MAX(CASE WHEN event_name = 'add_to_cart'     THEN 1 ELSE 0 END) AS cart_added,
        MAX(CASE WHEN event_name = 'checkout_start'  THEN 1 ELSE 0 END) AS checkout_started,
        MAX(CASE WHEN event_name = 'payment_success' THEN 1 ELSE 0 END) AS purchased
    FROM   events
    WHERE  event_date >= CURRENT_DATE - INTERVAL '30 days'
    GROUP  BY user_id, acquisition_channel
)
SELECT
    acquisition_channel,
    COUNT(*)                                              AS total_sessions,
    SUM(product_viewed)                                   AS product_views,
    SUM(cart_added)                                       AS cart_adds,
    SUM(checkout_started)                                 AS checkouts,
    SUM(purchased)                                        AS purchases,
    ROUND(100.0 * SUM(purchased) / COUNT(*), 2)           AS end_to_end_cvr_pct,
    ROUND(100.0 * SUM(cart_added) / NULLIF(SUM(product_viewed), 0), 1) AS view_to_cart_pct
FROM   funnel
GROUP  BY acquisition_channel
ORDER  BY purchases DESC;

Cohort Analysis for Campaign Evaluation

Cohort analysis groups customers by when they were acquired and tracks their behaviour over time. Critical for evaluating whether different campaigns acquire customers who actually retain and repurchase.

-- 3-month retention cohort by acquisition month
WITH cohort_base AS (
    SELECT
        user_id,
        DATE_TRUNC('month', first_order_date)  AS cohort_month
    FROM customers
),
activity AS (
    SELECT
        o.user_id,
        DATE_TRUNC('month', o.order_date)      AS activity_month
    FROM orders o
)
SELECT
    cb.cohort_month,
    EXTRACT(MONTH FROM AGE(a.activity_month, cb.cohort_month)) AS months_since_first,
    COUNT(DISTINCT a.user_id)                                   AS active_users,
    COUNT(DISTINCT cb.user_id)                                  AS cohort_size,
    ROUND(100.0 * COUNT(DISTINCT a.user_id)
          / COUNT(DISTINCT cb.user_id), 1)                     AS retention_pct
FROM       cohort_base cb
LEFT JOIN  activity     a  ON cb.user_id = a.user_id
                          AND a.activity_month >= cb.cohort_month
GROUP BY   cb.cohort_month, months_since_first
ORDER BY   cb.cohort_month, months_since_first;

Marketing Dashboard in Power BI — What to Include

India-Specific Marketing Analytics Considerations

Frequently Asked Questions

Learn Marketing Analytics as Part of a Full Analyst Programme

Evika Academy, Noida Sector 51, covers SQL, Python, and Power BI in the context of real Indian business data — including marketing analytics use cases.