Full Domain Training — Retail, E‑commerce & Consumer Goods (REC)
Master retail operations, e‑commerce platforms, trade promotion, customer analytics and AI-driven personalization. Practical labs, production-ready templates and domain playbooks for retailers and consumer brands.
Program Snapshot
- • Merchandising, assortment, pricing & inventory optimization
- • E‑commerce architecture, OMS, PIM, and conversion analytics
- • Trade promotion ROI, CLV, churn, and recommender systems
- • Tools: Databricks, Snowflake, OR-Tools, Power BI, MLflow
Overview
This domain program equips analytics and product teams with the business and technical skills needed to deliver measurable retail outcomes — higher conversions, optimized inventory, better promotions, and personalized customer experiences. We blend commerce operations, data engineering, ML, and productization into hands-on projects.
Who should attend
Retail analysts, e‑commerce product managers, data engineers, growth teams and consultants working with consumer brands.
Core benefits
Deliver data-driven merchandising, increase conversion, reduce stockouts, and build personalized customer journeys using scalable data platforms.
Prerequisites
Basic SQL and familiarity with Python or BI tools recommended; business domain knowledge helpful.
Program Tracks — 5 Core Modules
Track 1 — Retail Operations & Merchandising
- Category management, assortment planning, and planograms
- Inventory policies, safety stock, returns and markdown strategy
- Pricing strategies, elasticity testing, and promotion planning
- Case Study: Category profitability & inventory optimization
Track 2 — E‑commerce Platforms & Digital Retail
- Overview of storefronts, OMS, PIM, and headless commerce
- Product catalog design, content syndication, and API flows
- Checkout optimization, payments, fraud prevention, and A/B testing
- Case Study: Unified e‑commerce data model and funnel analytics
Track 3 — Consumer Goods & Trade Marketing
- FMCG distribution, trade tiers, and retailer collaboration
- Trade promotion planning, uplift measurement and ROI
- Channel strategies, distributor incentives and assortment compliance
- Case Study: Trade promotion ROI modeling
Track 4 — Retail Analytics & Customer Insights
- KPI definitions: GMV, AOV, CLV, retention, churn
- Customer segmentation, cohort analysis, and attribution
- Price elasticity, uplift modeling, and experiment analysis
- Case Study: CLV modeling and churn prediction
Track 5 — AI, Personalization & Digital Transformation
- Recommendation systems (collaborative filtering, embeddings)
- Real‑time personalization, feature stores, and inference scaling
- Demand forecasting, promotions optimization, and MLops in commerce
- Case Study: Real‑time recommender PoC using Databricks + MLflow
Hands‑on Labs & Projects
Each lab delivers deployable artifacts and GitHub-ready projects aligned to business outcomes.
Inventory Optimization Dashboard
Compute SKU-level velocity, forecast demand, simulate reorder policies, and design dashboards to reduce stockouts and improve GMROI.
Funnel Analytics & Conversion Pipeline
Ingest web/app events, build a conversion funnel, instrument A/B tests, and derive growth insights with Databricks + Snowflake.
Trade Promotion ROI Model
Design uplift experiments, estimate baseline sales, and attribute promotional impact using time-series and causal inference methods.
CLV & Churn Prediction
Feature engineering for CLV, build survival models and churn classifiers, and produce actionable segments for retention campaigns.
Recommender System PoC
Implement product similarity and collaborative filtering, evaluate offline metrics, and serve recommendations via a FastAPI endpoint.
Capstone — REC Growth PoV
Produce a capstone project: unified data model, ML models, dashboards, and an executive brief with KPIs and ROI projection.
Deliverables & Certification
- Retail data models and Snowflake scripts for marts
- Notebooks for CLV, churn, uplift, and recommender experiments
- Power BI / Looker dashboard templates and KPI playbooks
- Capstone PoV slides, technical appendix, and Yukti Certified REC Professional badge
Pricing & Delivery Options
Self‑paced
Recorded lessons, datasets, and lab guides — recommended 8–12 week timeline.
Cohort (Instructor‑led)
8‑week live cohort with office hours, code reviews, and capstone feedback.
Enterprise
Private cohorts, tailored pilots, on-site workshops, and data sandbox integration.
Request Info / Enroll
Tell us about your team and objectives — we'll reply with a tailored syllabus, pilot plan, and pricing.