Connect store, e-commerce, inventory, customer, marketing, finance, and supply chain data in one governed platform. Ask questions in plain language, build useful dashboards, and connect approved insights to action.
Cloud, private VPC, and on-premise options · Role-based access · Audit trails · Human approvals
Retail data lives in many places. Stores hold POS transactions. E-commerce platforms provide order and settlement files. ERP systems track finance and stock, while CRM, loyalty, marketing, and web platforms hold other parts of the customer journey.
Mimasa AI connects these sources, prepares data with natural-language instructions, saves trusted views, and lets authorised users ask business questions without writing SQL. The same journey can produce charts, reports, presentations, AI-agent decisions, and governed workflows.
This makes Mimasa AI more than a collection of retail analytics solutions. It creates one path from raw retail analytics data to insight, decision, and controlled action.
Retail analytics is the use of sales, stock, pricing, customer, store, web, and marketing data to improve retail decisions. Retail business analytics helps teams see what happened, understand why it happened, and decide what to do next.
Which stores, states, channels, or products drive revenue?
Where could stockouts or excess stock affect sales?
Which offers support sales without damaging margin?
Why are returns rising for a product or marketplace?
Which customer groups buy again, and which leave?
How do online and offline results compare?
Traditional analytics often ends with a chart. Mimasa AI can connect an approved result to a report, alert, task, agent, or workflow with human review where required. That is how analytics for retail becomes useful in daily work.
Bring authorised POS, ERP, CRM, loyalty, inventory, warehouse, marketplace, finance, marketing, web, CSV, and Excel data into one governed journey.
Use natural-language instructions and more than 100 built-in functions to filter, sort, join, merge, clean, normalise, group, and calculate data.
Save governed datasets and Intelligent Data Snapshots so teams reuse the same definitions for sales, margin, stock, returns, customers, and campaigns.
Ask questions such as, “Show sales and gross margin by state and marketplace for last month,” without writing SQL.
Build dashboards, reports, and presentations for store teams, category managers, finance, marketing, operations, and leadership.
Use an AI agent or agentic workflow to monitor a measure, prepare a file, route an exception, request approval, or begin the next process.
Amazon, Flipkart, Myntra, and other marketplaces produce different CSV or XLSX reports. Column names, charges, returns, settlements, and formats can make month-end work slow for e-commerce MSMEs and their chartered accountants.
Mimasa AI can standardise the repeated data work and produce reusable sales analysis from the same governed dataset.
Go deeper into a specific retail decision area, built on the same governed data, agent, and workflow layer.
Use governed retail data insights across inventory, stores, pricing, customers, campaigns, digital journeys, channels, and forecasts.
Combine sales velocity, stock, lead time, and location data. Find products at risk of a stockout, identify slow-moving items, and compare sell-through by store, state, warehouse, channel, category, or SKU.
“Which SKUs may run out in the next seven days?”
Give store and regional managers a shared view of sales, margin, stock, returns, and target performance. Analytics for physical retail can combine available POS, footfall, transaction, staffing, stock, and promotion data.
“Which stores missed target, and what changed?”
Review price, demand, stock, margin, promotion, and approved signals together. Compare list price, realised price, discount, and margin, then route suggested changes for human approval.
“Where are discounts rising without stronger sales?”
Build retail customer insights from authorised transaction, loyalty, CRM, support, return, and channel data. Explore repeat behaviour, product affinity, average order value, and customer groups.
“Which customer groups have high returns?”
Connect campaign cost, audiences, visits, orders, revenue, and margin. Retail campaign analytics shows which activity creates useful business results, not only clicks.
“Did the campaign improve profitable sales?”
Connect approved website behaviour with product, order, and campaign data. Compare traffic and conversion trends, find journeys where customers leave, and alert the appropriate team.
“Where does the product journey lose customers?”
Join store, marketplace, website, returns, stock, fulfilment, and customer activity. Retail omnichannel solutions help teams understand channel effects and decide where inventory should move.
“Are online promotions changing store demand?”
Use historic sales, seasonality, events, stock, and approved data to create forecasts. Agents can flag changing risks and workflows can prepare replenishment recommendations for review.
“What demand risks need attention this week?”
A unified dashboard helps teams compare inventory, sales, customer, campaign, store, and channel measures without creating another private spreadsheet version of the truth.
Which products are close to a stockout?
Stock risk by SKU and location
Alert inventory or prepare a reorder request
Which marketplace drove the best margin?
Sales, fees, returns, and margin by channel
Add the result to the monthly review
Where are returns rising?
Return rate by product, state, and channel
Create a category-owner investigation
Did the campaign improve profitable sales?
Revenue, margin, and uplift by audience
Recommend budget changes for approval
Which stores missed their targets?
Store performance and key drivers
Send a regional exception report
What is needed for state-wise GST work?
Standardised marketplace transactions by state
Route files and exceptions to finance
These retail analytics examples show the difference between dashboard-only software and an action-ready platform. Mimasa AI can connect retail analytics and insights to the next controlled step.
See how Mimasa AI turns natural-language requests into analysis, visual output, and a clearer next step. The same platform journey can be configured around authorised retail data and business controls.
See revenue, margin, stock, customer, channel, and forecast trends in one view, then ask follow-up questions without waiting for a new report.
Compare targets, stock, sales, returns, and local performance. Receive alerts when an agreed measure moves outside its range.
Review product, price, promotion, margin, inventory, and sell-through by SKU, category, store, marketplace, or region.
Connect spend with sales and margin, build reusable campaign views, and schedule clear retail marketing analytics reports.
Standardise marketplace reports and prepare state-wise sales and GST working data from one governed dataset.
Connect sources, control access, manage shared data, and expand self-service while preserving technical ownership.
Support self-service business analytics in retail and governed automation through one connected set of products.
Connect databases and authorised sources, or upload CSV and Excel files.
Explore Product →Clean, filter, join, group, normalise, and calculate data without code.
Explore Product →Save trusted views for reports, comparisons, agents, and workflows.
Explore Product →Create interactive charts and dashboards from plain-language requests.
Explore Product →Turn analysis into written insight, reports, and presentation-ready stories.
Explore Product →Build purpose-specific agents that use approved context and tools.
Explore Product →Coordinate triggers, rules, actions, approvals, and exceptions.
Explore Product →Apply role-based access, controlled sharing, and audit trails.
Explore Product →Many tools stop at a chart. Mimasa AI connects retail data preparation, analysis, dashboards, reports, AI agents, and workflows while preserving human control.
Move from connected sources to retail data analysis and the next approved step.
Let authorised business users ask questions in familiar terms without writing SQL.
Save approved datasets and snapshots instead of creating more spreadsheet copies.
Add schedules, conditions, actions, approvals, and exception paths where needed.
Use cloud, private VPC, or on-premise deployment according to infrastructure needs.
Balance performance, cost, and governance with commercial or open-source language models.
Clear answers about retail business intelligence, marketplace reporting, governance, analytics, and agentic automation.
Unify the data, give your team faster answers, and automate the next repeatable step with Mimasa AI.
