Retail | Lucid Sphere AI

Retail Analytics Solution: Key Features

Automated ingestion of structured and unstructured retail data (e.g., customer data, transaction history, sales by product) from internal and external data sources.
Cost-effective storing of all data types in a data lake.
Batch and real-time retail data processing.
Automated data cleansing: adding missing data to customer profiles, removing redundant product details.
Aggregating structured data in a data warehouse that is optimized for analytics querying and reporting.

Online Analytical Processing (OLAP) for slicing and dicing data across multiple dimensions (e.g., sales per employee, product bundle, store, brand).
Calculating the required retail KPIs: e.g., conversion rate, average order value, foot traffic, inventory turnover/shrinkage, sell-through percentage.
Identification of dependencies between multiple variables (demand, sales volume, season, etc.), performing root cause analysis, spotting anomalies, and highlighting potential issues.
Interactive dashboards for visual data representation with drag-and-drop functionality, slicers, filters, and more.
Scheduled and on-demand reporting.

Automated customer segmentation based on demographic, geographic, behavioral, and other parameters for efficient targeting in loyalty programs, marketing campaigns, and other customer-centric activities.
Analysis of CSAT, lifetime value, loyalty, average basket composition, number and frequency of purchases by customer and customer segment, etc.
Calculating customer profitability per product, including total profit per customer, total revenue, costs incurred, etc.
Retention analytics to identify the expediency of customer retention costs and activities.
Analyzing customer movement in brick-and-mortar stores with the help of a computer vision system.

Supplier KPIs: returns by period, order fulfillment rate, promise day adherence, total transportation cost, share in product category, etc.
Analytics-based selection of the best-fitting product suppliers.
ML-powered supplier risk forecasting, e.g., prediction of how supplier change can affect product profitability.
Monitoring inventory by category (on hand, low stock, aged, perishable, etc.) across all supply channels and stores.
Identifying inventory demand patterns per sales channel, physical location, customer segment, etc. for proper inventory allocation and prediction of out-of-stock and overstock cases.
Multi-echelon inventory optimization.
Modeling different supply chain scenarios to mitigate supply chain disruptions.

Automated identification of perceived value to understand the maximum price that customers are ready to pay for the product.
Automated calculation of price elasticity in relation to season, customer segment, store location, etc.
AI-powered monitoring and analysis of competitor prices and pricing techniques to adjust own tactics.
Price optimization to identify the most profitable base, markdown prices per product and brand.

Identifying top selling products and product categories.
Tracking sales KPIs like sales growth, average conversion time, sales per rep, lead-to-sale percentage, and more.
Analyzing sales performance at fine granularity, e.g., sales per period, payment mode, store, product purchase frequency.
Sales forecasting by brand, SKU, product category, season, etc.

Promotion analytics to identify the best tactics per customer segment, see how the promoted products affect the basket cost, whether a campaign attracted new customers, and more.
Optimizing and planning markdown strategies per brand, product, category.
Calculating marketing campaign KPIs: click-through rate, conversion rate, return on ad spend, etc.
Evaluating the success of marketing channels based on KPIs and prioritizing the best-performing ones.
Automatically calculating product affinity to upsell or cross-sell and prevent product cannibalization.
Analyzing and optimizing loyalty programs based on customer satisfaction and retention rates.

Identifying optimal patterns for product display in online and brick-and-mortar stores and optimizing shelf space.
Calculating assortment performance metrics: product/brand sales per store, margin per unit, product penetration rate across customer segments, etc.
Identifying the potential of different products to attract traffic in order to shape the product range.
ML-based planning of assortment based on its predicted performance.
Identifying products/SKUs/brands to be discontinued/promoted based on the calculated penetration rate, frequency of purchase, customer sentiment, etc.