Conversation intelligence
In-Store Conversation Analytics: The Metrics, the Reports and What They Change
In-store conversation analytics is the analysis of captured, consented sales conversations from a retail floor into metrics a store network can act on: how many conversations were captured, how closely each followed the playbook, whether the offer was quoted correctly, which objection ended the unconverted ones, which competitors were named, and how many walk-outs left a number. It is the reporting half of retail conversation intelligence, and it is where the conversation stops being a recording and becomes a decision. This guide sets out the metrics, the four reports that use them, how they join the POS and the footfall counter, and the decisions they change.
The metrics
Every metric is a count of things said, and every count can be traced back to the lines that produced it. That is what separates conversation analytics from a survey or a manager's impression: the number has evidence. Definitions and a 30-day plan to a baseline are at /learn/how-to-measure-in-store-sales-interactions/.
| Metric | Computed from | Read by |
|---|---|---|
| Coverage | Conversations captured over conversations that happened (walk-ins engaged, or bills plus walk-outs) | Store manager daily; the health check of everything below |
| Playbook adherence per step | Share of conversations in which each named step happened, with the transcript line | Store manager, area manager, training |
| Offer accuracy | Conversations in which the published price, exchange or finance terms were quoted correctly | Brand, compliance, area manager |
| Objection mix | The objection that ended each unconverted conversation: price, stock, finance, timing, trust, competitor | Store, brand and product teams, each for the objections they own |
| Competitor mentions | Rival brands or stores named by customers, by store and week | Brand and trade marketing |
| Numbers captured | Unconverted conversations in which a phone number was taken with consent | Store manager; the walk-out recovery owner |
| Unmet demand | Products or variants asked for that were not stocked or not pitched | Merchandising and category teams |
| Conversion by adherence | Conversion rate of conversations grouped by how many steps they contained | Regional head; proves or disproves the playbook |
The four reports
- Store manager, daily, two minutes: yesterday's coverage by advisor and hour, the most-skipped step, and the walk-outs with a number not yet followed up.
- Area manager, weekly, fifteen minutes per store: adherence per step against the region, objection mix, competitor mentions, and three transcript lines for the coaching conversation. The set-up and the review are at /learn/how-to-monitor-in-store-sales-interactions/.
- Brand, monthly: offer accuracy by region, the objection the product team owns, competitor mentions by market, unmet demand by category. The data a brand used to buy from a research agency, from its own floor.
- Training, quarterly: which step, once taught, stuck in which region; which stores run the playbook and which run their own; the library of best conversations by objection and by language.
Joining the POS and the footfall counter
Conversation analytics on its own describes the conversations that happened. Joined to the POS it describes which conversations sold, so that adherence can be read against conversion rather than against itself. Joined to a footfall counter it gains a denominator, so that engagement rate and time to engage can be read and the unattended walk-out can be counted. The join is by store and by time; no customer identity is needed for it. Borentis reads a store's existing counter and POS through its API and shows the result on one card per store next to the operations score from BorentisOps; camera-based visit signals that join the individual walk-in to the conversation are on the roadmap, not shipped.
The decisions it changes
- Where the area manager goes this week: to the two stores whose adherence fell, with the lines that show it, instead of the two stores on the rota.
- What the training team teaches next: the step that is skipped most across a region, not the module that is next in the calendar.
- Whether the offer is the problem: an offer quoted correctly in most conversations and still losing to price is a pricing decision; an offer quoted in half of them is a coaching one.
- What the brand hears from the market: a competitor named in eleven stores in a week is a market move, three days before the trade team hears of it.
- Who gets a call tomorrow: every walk-out with a number and a reason, drafted for a person to send.
What it is not
It is not surveillance of staff: the score is of the conversation, the advisor sees it first, and the evidence is a line they said. It is not customer profiling: no identity is needed for any metric above, and a phone number is kept only when the customer gave it for follow-up. It is not video analytics: cameras count and track and hear nothing, and the comparison is at /learn/conversation-intelligence-vs-cctv-footfall-analytics/. And it is not a dashboard: a metric nobody reviews on a rhythm is a camera nobody watches.
On the floor
A jewellery chain's monthly brand report shows an objection it had never recorded: customers in one region asking about a making-charge offer a competitor had launched that fortnight. The chain's own trade team heard of the offer a week later. The report also shows the chain's best store answering the objection with a design argument rather than a discount, and converting. That store's three best lines become the region's coaching note.
Frequently asked questions
What is in-store conversation analytics?
The analysis of captured, consented in-store sales conversations into metrics with evidence: coverage, playbook adherence per step, offer accuracy, objection mix, competitor mentions, numbers captured, unmet demand and conversion by adherence. It is the reporting half of retail conversation intelligence.
How is it different from retail analytics or video analytics?
Retail analytics describes what sold, from the POS; video analytics describes who came and where they went, from cameras. Conversation analytics describes what was said between the two, which is the part that decides conversion and the part neither of the others can see.
Does it need customer identity?
No. Every metric is computed per conversation, store and time without knowing who the customer was. A phone number is kept only when the customer gave it with consent for follow-up.
Related reading
- What is retail conversation intelligence?
- How to measure in-store sales interactions
- Retail KPIs only the conversation can measure
- Retail analytics: the hub
- In-store conversation intelligence vendors map
Where Borentis applies this
- Execution Scorecards: See the floor before the P&L does.
- Objection Intelligence: The reason they did not buy, in their own words.
- Unmet Demand Signals: Demand for what you did not have.
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.