Retail analytics
Store Analytics: Measuring Store Performance and Store Operations Together
Store analytics is the measurement and comparison of individual stores in a network: how each performs against its targets and its peers, and how well each runs its operations. Store performance analytics reads results, sales, conversion, average transaction value, footfall. Store operations analytics reads process, checklists, audits, stock, staffing. Most retailers run the two in different tools, read by different teams, and neither can see the third thing that decides revenue: whether the store sells the way it was trained to.
Store performance analytics: the results view
Performance analytics answers which stores are ahead and which are behind. It is built almost entirely on the POS and the footfall counter, and it is the view the sales head and the CFO read every Monday.
- Like-for-like growth: this month against the same month last year, for stores open in both.
- Target achievement: sales against plan, by store and by category.
- Conversion: bills as a share of footfall.
- Average transaction value and units per transaction.
- Sales per square foot, for comparing stores of different sizes.
- Rank within region and within footfall tier.
Store operations analytics: the process view
Operations analytics answers whether the store is run the way the manual says. Its data comes from checklist apps, audit visits, photo submissions and stock systems. It is the view the operations head and the area manager read, and it is what most retail execution software in India is built for. BorentisOps covers this half with daily checklists and photo audits.
- Checklist completion: opening, closing, hygiene, cash, by day.
- Audit score: the periodic visit by the area manager or an agency.
- Visual merchandising compliance: display up, on time, matching the guide, proved by photo.
- On-shelf availability and stock-out duration.
- Staffing against roster: who was present when.
Why the two views disagree
Put the performance table and the operations table side by side and four kinds of store appear. Reading them together is the point of store analytics, and each kind needs a different response.
- Strong operations, strong sales: the store to learn from. Its advisors' conversations are the network's examples.
- Strong operations, weak sales: the store looks right and does not sell right. It does not need another audit; it needs to know which step of the sale is being skipped.
- Weak operations, strong sales: the store sells despite itself. Fix the process before the selling advisor leaves and takes the revenue with them.
- Weak operations, weak sales: usually a store manager problem, and the first store the regional head should visit.
Store scorecard metrics: definitions, sources and owners
| Metric | Definition | Source | Cadence | Owner |
|---|---|---|---|---|
| Like-for-like growth | Sales versus same period last year, comparable stores | POS | Monthly | Sales head, finance |
| Footfall conversion | Bills divided by counted walk-ins | POS, footfall counter | Weekly | Store manager, sales head |
| Checklist completion | Share of scheduled checklist items done on time | Checklist app (BorentisOps or equivalent) | Daily | Area manager |
| Audit score | Weighted score from the periodic store audit | Audit visit, photo audit | Monthly or quarterly | Operations head |
| On-shelf availability | Share of listed SKUs in stock and on display | Stock system, shelf photos | Weekly | Merchandising |
| Coverage | Consented conversations as a share of walk-ins | Conversation capture against footfall | Weekly | Store manager |
| Playbook adherence | Share of conversations in which each named step happened | Consented conversations, scored | Weekly | Sales head, trainers |
| Offer accuracy | Current scheme quoted with the right amount and terms | Consented conversations, scored | Weekly | Sales head, marketing |
Sales execution next to operational execution
Operations analytics measures whether the store followed process. It has never measured whether the store sold as trained, because the sale is a conversation and there was no record of it. Borentis captures that conversation on the advisor's phone with the customer's consent and scores it against the playbook, which gives store analytics a third column: adherence by step, offer accuracy and objection handling, per store per week, with the transcript line behind each score.
The practical form is one scorecard per store with all three columns, results, operations, sales execution. The store with a perfect checklist and a skipped finance step gets a trainer. The store with weak merchandising and strong selling gets an operations visit. Networks that read the three together stop sending the trainer to the store the auditor complained about and start sending them to the store the conversations complain about.
Comparing stores fairly
- Group stores by footfall tier and format before ranking. A high-street flagship and a mall kiosk are not peers.
- Show the denominator with every ratio: conversion with its footfall, adherence with its coverage.
- Compare a store with its own last eight weeks before comparing it with the network.
- Separate tenure effects: a store with three new advisors will lag on adherence for a quarter.
- Publish the scorecard to store managers, not only to head office. A number nobody at the store can see changes nothing.
Frequently asked questions
What is store analytics?
Store analytics is the measurement and comparison of individual stores in a retail network, covering performance (sales, conversion, growth against targets and peers) and operations (checklists, audits, stock, staffing), ideally read together on one scorecard per store.
What is the difference between store performance analytics and store operations analytics?
Performance analytics reads results from the POS and footfall counter: growth, conversion, transaction value. Operations analytics reads process from checklist and audit tools: completion, compliance, availability. Neither sees whether the store sells as trained, which needs the conversation.
What metrics should a store scorecard include?
Results (like-for-like growth, conversion, ATV), operations (checklist completion, audit score, on-shelf availability) and sales execution (coverage, playbook adherence, offer accuracy). Every ratio should show its denominator.
How do you compare stores with different footfall?
Group by footfall tier and format, compare ratios rather than totals, show each store against its own trend before its peers, and account for advisor tenure. Sales per square foot and conversion are the usual size-neutral measures.
Related reading
- Retail analytics: the full guide
- Network Performance
- BorentisOps, operations checklists and audits
- How to measure sales execution across hundreds of stores
Where Borentis applies this
- Execution Scorecards: See the floor before the P&L does.
- Playbook Adherence: Your playbook, finally observed.
- Coaching from Best Conversations: Your best advisor, teaching everyone.
Borentis is the Agentic Operating System for Customer Interactions, built for Indian retail floors: consented one-tap capture on the advisor's phone, every conversation scored against your playbook with the evidence behind every number, leads created when a number is heard, and coaching from your own best conversations.