Retail Analytics: A Practical Guide to Customer, Store, Sales, Video, Workforce, Merchandising and Footfall Analytics

What retail analytics is, and what it is built on

Retail analytics is a set of questions asked of store data. Which stores are growing. Which products move. When customers come. Who sells. Whether the display went up. The answers come from systems the retailer already runs: the POS, the footfall counter, the CRM, the HR roster, the audit app, the CCTV recorder. Each system was installed to run the business, not to explain it, and analytics is the work of reading them together.

Almost all of that data is results data. A bill records that a sale happened. A counter records that a person entered. A CRM status records that a lead was marked lost. None of it records the ten minutes in between, where the customer asked a question, heard an answer, raised an objection and decided. That is why most retail analytics is strong on what and weak on why.

The seven branches below are the ones a mid-size or large Indian retail network typically runs, whether from one platform or seven spreadsheets. They overlap, and the useful way to read them is as seven views of the same store day.

The seven branches at a glance

Customer analytics

Customer analytics asks who the customers are, what they buy, how they move through the store and why they do or do not purchase. Its data is transaction and loyalty history, which describes buyers well; footfall and camera data, which describes movement; and surveys, which describe a self-selected few after the fact. Segmentation, repeat rate, basket analysis and journey mapping all sit here.

Its gap is the non-buyer. The majority of walk-ins in assisted retail leave without a bill and without answering a survey, so the analytics describes the customers who stayed and says little about the ones who left. The branch guide covers behaviour analytics, journey analytics and where the conversation adds the objection in the customer's own words: /learn/customer-analytics-in-retail/.

Store analytics

Store analytics compares stores. Performance analytics reads the results side, like-for-like growth, conversion, average transaction value, target achievement, and ranks stores against peers. Operations analytics reads the process side, checklist completion, audit scores, photo compliance, stock outs, and tells head office whether the store is run the way the manual says.

The two usually live in different tools and are read by different teams, which is why a store can be top of the audit table and bottom of the sales table with nobody asking why. The branch guide sets out both views, a fair way to compare stores of different sizes, and how sales execution belongs on the same scorecard as operational execution: /learn/store-analytics/.

Sales analytics

Sales analytics measures how a store turns visitors into revenue. Performance analytics reads totals and trends by store, category, advisor and week. Conversion analytics reads the funnel between walk-in and bill: how many entered, how many bought, and what each bought.

The POS and the footfall counter see the two ends of that funnel and nothing in between. Two stores with the same footfall and the same range can convert at fourteen per cent and twenty-two per cent, and sales analytics can only report the gap. The branch guide covers the funnel stages, the metrics behind each, and why playbook adherence is the variable that explains conversion: /learn/retail-sales-analytics/.

Video analytics

Video analytics applies computer vision to CCTV footage so the camera produces counts and events instead of hours of recording: people entering, paths taken, time spent in a zone, queue length, an advisor approaching a customer, a hand reaching a shelf. Most retailers already own the cameras; the analytics is a software layer on top.

It is the branch that sees the most and hears nothing. It can show that a customer waited four minutes and left the counter; it cannot say what was said there. The branch guide is a short orientation with links to the two deep dives on this site: /learn/retail-video-analytics/.

Workforce analytics

Workforce analytics measures the people on the floor: staffing against footfall by hour, sales per labour hour, sales and conversion per advisor, attach rate, attrition. Sales associate analytics narrows it to the individual and drives incentives, recognition and rosters.

Its data is the roster and the POS, which record when an advisor was present and what was billed against their code. That ranks advisors; it does not explain them, and the attribution is noisy in any store where walk-ins are shared. The branch guide covers the three layers of workforce analytics and how the conversation shows how advisors sell, not only what they sold: /learn/retail-workforce-analytics/.

Merchandising analytics

Merchandising analytics measures what is on the shelf and whether it sells: on-shelf availability, planogram compliance, share of shelf, facings, and the sales each position produces. Shelf analytics reads photos, planogram analytics compares the photo to the plan, placement analytics ties position to POS.

Every one of those starts from what was stocked. A customer who asked for a colour, size or model the store did not carry leaves no trace in a shelf photo or a bill. The branch guide covers the three sub-branches and how the conversation records the request that was never stocked: /learn/retail-merchandising-analytics/.

Footfall analytics

Footfall analytics counts visitors: how many entered, when, how long they stayed, and how that compares with sales and staffing. It gives every store the denominator for conversion and the shape of its day, and it is usually the first analytics a network buys after the POS.

A counter records the door. It does not record whether the visitor was attended, how long they waited, or why they left. The branch guide covers how counters work, what visitor analytics and dwell time analytics add, and the unattended visit that no counter can report: /learn/footfall-analytics/.

The missing input in every branch

Read across the seven branches and the same gap appears in each. The data describes the store before and after the sale, and the sale itself is a conversation nobody recorded. Consented conversation capture, where the advisor records the sales conversation on their phone with the customer's consent and the transcript is scored against the playbook, adds one input that every branch was missing. Borentis is built for that input, in Hindi, English and Hinglish, and it is an addition to the branches above, not a replacement for any of them.

• Customer analytics gains the objection and the request in the customer's words, from buyers and non-buyers alike.
• Store analytics gains sales execution, adherence to the playbook, next to operational execution from checklists and audits.
• Sales analytics gains the step between walk-in and bill that explains why conversion differs between stores.
• Video analytics gains the words behind the approach and the walk-away, once camera events are joined to conversations, which is on the roadmap.
• Workforce analytics gains how each advisor sells, so recognition and coaching rest on evidence rather than bill attribution.
• Merchandising analytics gains the product the customer asked for and the store did not have.
• Footfall analytics gains the share of visits that never became a conversation.

Where to start

1. Pick the branch that matches the question leadership is asking this quarter. A network with a staffing problem starts with workforce and footfall; a network with a conversion problem starts with sales and store.
2. Get the denominators right first: footfall by store, coverage by store, labour hours by store. Every ratio depends on them.
3. Put results next to causes on one page per store. Conversion next to adherence, audit score next to sales, footfall next to staffing.
4. Add the conversation in a handful of stores before the network, and read coverage before reading any score.