Mastering Retail Analytics: From Data to Strategy

Explore multichannel and omnichannel retailing by analyzing store, web, mobile, catalog, call center, and kiosk channels. Apply analytics to leverage each channel’s benefits and identify niche opportunities.

Explore how multichannel retailing leverages the internet to broaden assortment, provide detailed product information, 360-degree imagery, and personalized recommendations that drive better purchase decisions.

Develop a multi-channel strategy for brick-and-mortar retailers by integrating website stores, kiosks, direct selling, and legacy systems to reduce disintermediation and deliver seamless omnichannel experiences.

Learn to design and interpret dashboards and scorecards to track KPI metrics and targets across store and area managers, using retail analytics frameworks.

Explore how to transform raw data into useful information by distinguishing measures and dimensions, and design dashboards, scorecards, and KPIs aligned with strategic objectives in retail analytics.

Explore RFM analysis that segments customers by recency, frequency, and monetary value to drive retailer strategies and promotions. Learn about mutex and tile versus threshold bucketing on real world data.

Learn to classify retail customers by recency, frequency, and monetary value (RFM) using mutex and threshold methods, create five-bucket segments, and tailor strategies based on bucket combinations.

Build and apply an RFM model for retail analytics by defining scope, time frame, and RFM scoring. Learn to apply RFM to campaigns, prioritize communications, and analyze migration across segments.

Apply rfm analysis to identify drops or rises in recency, frequency, and monetary value. Tailor reactivation, cross-sell, and upsell with Excel and extend to SQL Server, SAS, or R.

Explore market basket analysis (MBA) and association rules to uncover product co-occurrence in POS transactions and guide promotion, merchandising, and inventory decisions with data-driven insights.

Learn the retail analytics MBA methodology part 2 as it outlines base and target products and four metrics—support, confidence, lift, and affinity—for measuring product relationships in transaction data.

Mastering retail analytics explains market basket analysis using support, confidence, lift, and affinity to drive cross-sell, upsell, and store layout strategies with case studies in Excel.

Explore the category scorecard in retail analytics, presenting dashboards tailored to executives and managers—from CEO to category manager—covering out-of-stock issues, pricing, assortment, and RFM segmentation.

Explore how to build and optimize retail category dashboards using data on assortment, buying, pricing, promotions, and sales and inventory to drive holistic performance and better decision making.

Explore how a holistic view of the retail category scorecard informs integrated dashboards for stakeholders. Analyze assortment, pricing, promotions, inventory, and loss leaders to drive growth and profit across stores.

Explore pricing as a personalized strategy integrated with CRM and customer 360 degree insights. Build category score dashboards using loss prevention, assortment, and placement analytics.

Turn dashboard knowledge into action by analyzing issues, identifying root causes, building scenarios, simulating outcomes, and executing approved decisions to optimize retail category performance.

Retool category score cards to analyze store operations, manpower planning, and holiday staffing, alongside customers and products. Identify leakage and shrinkage, and build dashboards to boost profit.

Build a role-based retail scorecard dashboard that tracks store performance, growth, and transactions using analytics such as market basket, RFM, segmentation, and clustering, with inventory alerts.

Analyze store productivity, sales, and growth trends using plan versus performance and category analysis. Apply inventory, buying, and promotion analytics, including aging, stock, OTB, and vendor scorecards, plus predictive regression.

Learn how store clustering groups stores by geography, customer behavior, and price sensitivity to optimize zones, pricing, distribution, and ROI using methods like k-means and expectation maximization.

Learn how to cluster stores to maximize profit and localize pricing for the right customer at the right store, guided by consumer price sensitivity, demographics, and regional data. Apply science-based analytics, business rules, and ROI-focused metrics to optimize store assignments, elasticity, and cost-benefit outcomes across clusters.

Apply store clustering and retail geography to optimize price zones and price sensitivity, mapping zones, concepts, and cities for data-driven store placement and growth.

Apply analytics and loyalty data to a Mumbai hypermarket case study, identifying loyal customers through segmentation, analyzing buying behavior, and recommending actions to boost sales for the CEO.

Apply retail analytics to identify loyal customers and six-month buying behavior, use Pareto analysis to spotlight 80% of sales, and translate data into stock decisions for the case study.

Explore a real retail case study with a nine-column Excel dataset of two lakh 739 rows across six months, and use a pivot to count unique bills (20,395).

Read retail data with pivot tables to count sku numbers, transactions, and unique customers over six months; use date, month, and rfm concepts to identify valuable customers.

Explore retail data understanding by analyzing SKU sales, price variation across customers, and promotional pricing; identify departments and sub-departments, use pivot tables, and integrate customer data for insights.

Classify 7000 customers into high, medium, and low segments using a recency–frequency–monetary value model, deriving days since last purchase from max dates, and illustrating with pivot tables.

Learn how to compute transaction scores and monetary value scores by deduplicating bills, deriving unique transactions, building pivot tables, and classifying customers by transaction counts for retail analytics.

Master advanced customer segmentation by building a weighted model from monetary value, recency, and frequency, using three datasets and Vlookup to merge data and create actionable segments for targeted marketing.

Explore data modelling from multiple viewpoints, build category and customer dashboards, and develop personalized recommendations with RFM segmentation to boost category performance and targeted promotions.

Sell retail analytics decisions to top management by presenting clear data stories and dashboards, using RFM-based segmentation and actionable strategies like cross-sell, pricing, and promotions.

Utilize RFM segmentation and a bubble chart to reveal five customer segments and their value. Guide personalized strategies like repurchase, cross-sell, and upsell across all customers.

From a category perspective, this lecture presents category recommendations and stories for apparel, accessories, children, and footwear, detailing customer, merchandise, and pricing strategies, including untapped promotions and delisting underperforming subcategories.

Learn to align customer, merchandise, and pricing strategies with dashboards that persuade management and translate category insights, top SKUs, and promotions into actionable retail recommendations.