E-Commerce Analytics & Optimization
A 10-week, 60-hour applied course for learners who want to work as junior e-commerce analysts. Using one fictional Surat home-textile brand, Surat Weave & Home, that sells on Shopify and Amazon India, learners validate data, read Shopify Analytics and the September 2026 session change, interpret Amazon Brand Analytics responsibly, evaluate pricing and contribution, diagnose conversion, forecast inventory, compare channel profitability and finish with a client-ready 90-day optimization report. All practice uses synthetic data.
- Duration: 10 weeks
- Modules: 20
- Fee: Rs 14,999
- Field: E-commerce
Roles this track prepares you for
- Junior E-Commerce Analyst
- E-Commerce Analyst
- Marketplace Analyst
- Growth Analyst
- E-Commerce Reporting Executive
- Marketplace Data Analyst
- Merchandising Analyst
- Inventory Planning Analyst
- Conversion Analyst
- Seller Performance Analyst
Curriculum
- E-Commerce Measurement and the KPI Framework - Turns vague business worries into precise questions and metrics. Learners separate data from insight, define revenue, orders, sessions, AOV, conversion and refunds, and finish with a KPI dictionary and metric tree for Surat Weave & Home.
- From business questions to metrics
- Metric definitions, data grain and sources
- T01 brief: Build the KPI framework
- T01 workshop: KPI dictionary and metric tree
- Data Quality, Cleaning and Reconciliation - Teaches learners to trust data only after checking it. They find duplicates, wrong currencies, missing SKU mappings and misclassified returns, repair what the rules allow, flag the rest and reconcile totals before and after cleaning (T02 and assignment W01).
- T02 brief: Data quality rules and the dirty export
- T02 workshop: Clean and reconcile the order export
- W01 brief: Measurement foundation kit
- W01 workshop: Baseline, cleaned data and limitations
- Shopify Analytics: Sales and Product Reports - Introduces the modern Shopify Analytics dashboard, metric cards and report types. Learners calculate gross and net sales, AOV and refund effects, compare product families and find reasons why exported reports and order records disagree (T03).
- Modern Shopify Analytics: dashboard, cards and reports
- Sales definitions, comparisons and discrepancies
- T03 brief: Shopify sales and product analysis
- T03 workshop: Net sales, AOV and refund checks
- Shopify Custom Reports and the 2026 Session Change - Covers custom data explorations, guided ShopifyQL with no-code alternatives and the September 2026 session measurement change. Learners test whether a conversion jump is real or only a new measurement baseline (T04 and assignment W02).
- T04 brief: Custom reports, ShopifyQL and sessions
- T04 workshop: Session measurement audit memo
- W02 brief: Shopify analytics and reporting kit
- W02 workshop: Report pack and comparability analysis
- Shopify Conversion Funnel Diagnosis - Builds the purchase funnel from session and event data: product view, add-to-cart, checkout and purchase. Learners compare desktop and mobile, separate real behaviour from tracking effects and write testable improvement hypotheses (T05).
- Customer journey and funnel definitions
- Device, channel and attribution limits
- T05 brief: Diagnose conversion drop-off
- T05 workshop: Funnel, device split and hypotheses
- Customer Cohorts and Repeat Purchase - Teaches first-purchase cohorts, repeat purchase rate and fair observation windows. Learners handle cohorts that are too young to judge, estimate customer contribution and suggest retention actions (T06 and assignment W03).
- T06 brief: Cohorts, repeat purchase and windows
- T06 workshop: Build first-purchase cohorts
- W03 brief: Customer journey and retention kit
- W03 workshop: Funnel, cohorts and priorities
- Amazon Brand Analytics and Search Query Performance - Explains who can use Amazon Brand Analytics (Brand Registry, Brand Representative role) and why access on Amazon India must be checked. Learners then analyse simulated aggregate Search Query Performance data and state clearly what it cannot prove (T07).
- Brand Analytics, Brand Registry and access
- Report family, aggregate data and limits
- T07 brief: Analyze Search Query Performance
- T07 workshop: Query priorities and funnel loss
- Search Catalog Performance and Brand Insights - Moves from queries to ASINs. Learners diagnose catalogue drop-offs, use aggregate market basket data for cross-sell ideas and keep Brand Analytics, seller reports and Amazon Ads numbers apart (T08 and assignment W04).
- T08 brief: Search Catalog Performance
- T08 workshop: ASIN drop-offs and brand insights
- W04 brief: Brand Analytics decision kit
- W04 workshop: Query, catalogue and action plan
- Product Economics and Contribution Margin - Builds the full cost stack under a selling price: product cost, packaging, fulfilment, platform and payment fees, returns and marketing. Learners calculate contribution by SKU and channel and spot best-sellers that make little money (T09).
- Price, discount and the cost stack
- Contribution margin and misleading best-sellers
- T09 brief: SKU pricing and contribution
- T09 workshop: Contribution worksheet and price scenarios
- Promotions, Bundles and Merchandising Decisions - Tests whether discounts and bundles really improve contribution once stock limits are applied. Learners compare offers, pick cross-sell pairs and recommend a promotion with guardrails, labelling every forecast as hypothetical (T10 and assignment W05).
- T10 brief: Promotion and bundle economics
- T10 workshop: Discount and bundle decision pack
- W05 brief: Profitability and pricing strategy kit
- W05 workshop: SKU economics and risk register
- Conversion Optimization and UX Diagnostics - Shows how to find friction in a product page and checkout using data, customer feedback and error traces. Learners separate observation from hypothesis, prioritise fixes and define success metrics and guardrails before any change (T11).
- Conversion, UX friction and trust signals
- Hypotheses, heatmap concepts and guardrails
- T11 brief: Audit a low-converting journey
- T11 workshop: Friction log and revised page concept
- A/B Testing and Experiment Evaluation - Teaches learners not to crown a winner because one number is higher. They check random assignment and exposure, calculate lift, read uncertainty, review guardrails and recommend launch, retest or hold (T12 and assignment W06).
- T12 brief: Evaluate an A/B experiment
- T12 workshop: Lift, uncertainty and decision memo
- W06 brief: CRO audit and experimentation kit
- W06 workshop: Hypotheses, test design and decision
- Inventory Health and ABC/XYZ Classification - Covers sell-through, turnover, days of cover, lead times and stockouts, then classifies SKUs by economic importance (ABC) and demand variability (XYZ) so each group gets its own stock policy instead of one rule for everything (T13).
- Sell-through, turnover and days of cover
- ABC, XYZ, lead times and stockouts
- T13 brief: Classify and diagnose inventory
- T13 workshop: ABC/XYZ table and SKU policies
- Demand Forecasting and Replenishment - Builds simple baseline forecasts (naive, seasonal naive, moving average), tests them on past data without using future values, handles stockout-censored demand and turns the result into a reorder point, safety stock and order quantity (T14 and assignment W07).
- T14 brief: Forecasting and replenishment basics
- T14 workshop: Forecast, backtest and reorder plan
- W07 brief: Inventory forecasting and stock kit
- W07 workshop: Classes, forecasts and stock risk
- Cross-Channel Profitability: Shopify versus Amazon - Compares the two channels on one definition of net sales and contribution. Learners reconcile refunds and costs to the ledgers and learn why identified customers must never be matched across Shopify and Amazon aggregate reports (T15).
- Channel economics: Shopify versus Amazon
- Reporting grain, compatibility and identity limits
- T15 brief: Channel contribution analysis
- T15 workshop: Reconcile and compare channels
- Marketing Efficiency and Attribution Integrity - Teaches scoped CAC, ACOS, ROAS and TACOS and the overlap between Shopify campaign attribution, Amazon Ads reports and total platform sales. Learners avoid double-counting and turn the findings into optimisation actions (T16 and assignment W08).
- T16 brief: Acquisition efficiency and attribution
- T16 workshop: Efficiency metrics and reconciliation
- W08 brief: Channel economics and marketing kit
- W08 workshop: Contribution, reconciliation and risks
- Executive Dashboards for E-Commerce Decisions - Shows how to design a dashboard that supports a decision: metric hierarchy, period comparison, filters, drill-downs and visible data warnings. Learners build one Shopify and Amazon dashboard that reconciles to the source files (T17).
- Dashboard design and metric hierarchy
- Filters, drill-downs and data warnings
- T17 brief: Integrated dashboard
- T17 workshop: Build and reconcile the dashboard
- Optimization Prioritization and Scenarios - Teaches how to choose between competing improvements when cash is limited. Learners build a backlog, model low, base and worst cases under stated assumptions, score options on evidence and feasibility and set tests and checkpoints (T18 and assignment W09).
- T18 brief: Prioritize competing improvements
- T18 workshop: Backlog, scenarios and scoring
- W09 brief: Executive analytics and optimization kit
- W09 workshop: Dashboard, scenarios and recommendations
- Analytics Consulting and the 90-Day Roadmap - Treats analytics as a professional service: client discovery, data permissions, deliverables, reporting cadence, scope and fair pricing. Learners turn their analysis into a 30, 60 and 90-day roadmap with owners, KPIs, risks and an escalation process (T19).
- Client discovery, data access and permissions
- Deliverables, cadence, scope and fair pricing
- T19 brief: Ninety-day improvement plan
- T19 workshop: Milestones, KPIs and risks
- Client Proposal and Capstone Portfolio - Final week of integration. Learners prepare a professional report and quotation (T20), assemble the weekly work into one portfolio (W10), correct serious errors and present the 90-day optimization system for Surat Weave & Home. No new analysis is started.
- T20 brief: Client report and quotation
- T20 workshop: Proposal, scope and quotation
- W10 and capstone: Assemble the portfolio
- Capstone review, correction and presentation
Frequently asked questions
How long is the E-Commerce Analytics & Optimization course?
It runs for 10 weeks, with 20 modules. It is self-paced online learning from EasySkill Career Academy, Surat.
How much does the E-Commerce Analytics & Optimization course cost?
The fee is Rs 14,999.
What will I build in E-Commerce Analytics & Optimization?
Every module ends in a graded practical task that AI checks in about 30 seconds, so you finish with a portfolio you can show to employers or clients.
Which roles does E-Commerce Analytics & Optimization prepare me for?
It prepares you to apply for roles such as Junior E-Commerce Analyst, E-Commerce Analyst, Marketplace Analyst, Growth Analyst, E-Commerce Reporting Executive. It does not guarantee a job or income.
Can I learn online, or do I have to visit a centre?
You can learn fully online. EasySkill also has centres in Adajan, Vesu and Bhestan, Surat, if you prefer to visit.
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