1. Overview
What it is Your daily starting point: a portfolio-level snapshot of every store you have access to.
What you see
Executive KPIs: total visits for the selected period, growth versus the previous comparable period, and average performance per store.
Store leaderboard: stores ranked by footfall and growth, so you can immediately spot over- and under-performers.
Performance chart: the aggregated visits trend over the selected date range.
Data quality alerts: warnings when a sensor or a scheduled sync did not deliver complete data for a day.
How to use it
Pick your date range at the top right. Every card recalculates instantly.
Use the store picker to focus on a single store, or leave it on all stores for the portfolio view.
Click any store in the leaderboard to jump to its detailed analysis.
Tips
Growth is always calculated against the immediately preceding period of the same length, so a 7-day range compares to the 7 days before it.
If a store shows an unexpected drop, check the data quality alerts first — it is often a sync gap rather than a real decline.
2. Location Insights
What it is The map-based view of your store network and its surrounding urban context in Athens.
What you see
Interactive map with a marker per store, colour-coded by performance.
Store table, bidirectionally synced with the map: filtering or selecting in one filters the other.
Points of interest layer, with category filters and heat strength indicators showing how dense each category is around a store.
Mobility context: traffic camera telemetry and public transport hub/ridership data used as external footfall drivers.
Neighborhood analysis: saturation index and synergy scores describing how crowded or complementary an area is.
How to use it
Zoom to the area you care about, or search a store in the table.
Toggle POI categories to see what surrounds each location (transport, food, retail, services).
Click a marker to open the store detail slide-over with performance, Market Capture Index and store metadata.
Tips
Market Capture Index compares a store's actual visits with the visit potential of its catchment: above 100 means the store outperforms its location, below 100 signals unrealised potential.
White-space analysis highlights areas with demand but no presence — useful for expansion discussions.
3. Visitor Analysis (existing article, updated)
Add these new sections to the article that already exists:
Sales overlay on the In-Store Visits Timeline When sales data has been imported for the store, the timeline can display additional series next to visits:
Transactions: number of receipts per day or hour.
Conversion rate: transactions divided by entrances, on its own 0–100% axis.
Sales value: total value per bucket. Use the legend row above the chart to switch each series on and off — the chart keeps only the axes it needs.
New KPI cards Alongside total visits, growth velocity and Google review score you will now see:
Transactions for the selected period.
Transactions growth velocity versus the previous period.
Conversion rate (transactions ÷ entrances).
Tooltips and exports Hovering a point shows conversion rate, average transaction value (value ÷ transactions) and sales per visitor (value ÷ entrances). CSV and Excel exports include Transactions, Sales Value, Conversion Rate (%), Avg Transaction Value and Sales per Visitor pre-calculated, plus a header sheet documenting each formula.
Note: sales series only appear when sales data exists for the store and selected period.
4. Zone Heatmap
What it is A floorplan-based view of how attention and traffic are distributed inside the store.
What you see
Store floorplan with zones shaded by intensity: darker means more people passing or dwelling.
Zone statistics panel: visits, share of total traffic and dwell behaviour per zone.
Time controls to compare morning, midday and evening patterns, or weekday against weekend.
How to use it
Choose a date range and, if available, a resolution (hourly or 1-minute).
Click a zone to isolate its statistics.
Compare the same zone across two periods to measure the impact of a layout or planogram change.
Tips
A zone with high traffic but low dwell is a corridor, not a destination — do not judge it on engagement.
Cold zones next to hot zones usually indicate a visibility or blocking issue rather than weak product interest.
5. Demographics
What it is Audience composition of your visitors, derived from V-Count sensor demographic detection.
What you see
Composition chart: gender and age-band split for the selected period.
Persona behavioural insights: how each segment behaves differently in visit timing, frequency and dwell.
Trend over time so you can see whether your audience mix is shifting.
How to use it
Select the store and date range.
Compare a promotional week with a baseline week to see which segment the campaign actually attracted.
Use the persona panel to align staffing and assortment with your dominant segments.
Tips
Demographic detection is always sourced from V-Count sensors, even when footfall counting for a stand comes from another source.
Small daily samples are noisy; use at least 7 days for segment-level decisions.
6. Promo Stand Effectiveness
What it is Measures how well each promotional stand attracts and engages shoppers, and which products drive that performance.
What you see
Stand performance table: impressions (passers-by), engagements (stops), engagement rate and share of store traffic.
Engagement vs. Attraction chart: positions each stand by how many people it reaches against how many it converts into a stop.
Product assignments: which product, subcategory, category and brand was on which stand during which period.
Group-by control: analyse results by stand, product, subcategory, category or brand.
How to use it
Select the period. Assignments are period-aware, so results always reflect what was physically on the stand at that time.
Group by Product to compare products, or by Stand to compare locations.
Use stand aliases (the names your store teams actually use) to communicate results internally.
Tips
Stand counting data can come from V-Count sensors, Redash queries or CSV backfill, and can be aggregated at 1-minute or hourly resolution.
A high engagement rate on a low-traffic stand can outperform a busy stand — always read both figures together.
If a stand shows zero impressions for one day only, check the sync status before drawing conclusions.
7. Visitor Flow & Coverage
What it is Shows how shoppers physically move through the store: paths taken, aisles covered, and where they drop off.
What you see
Floorplan flow map: animated parallel flow lines drawn along realistic walking paths that respect shelving and obstacles.
Directional flows: inbound (towards the back) and outbound (towards checkout) are displayed separately on the same diagram.
Aisle traffic and drop-off cascade: how many shoppers reach each successive aisle and where they are lost.
Behavior Intelligence panel: dominant routines, detected bottlenecks and timeline scrubbing to replay the day.
Resolution toggle: hourly or 1-minute flow, when minute-level stand data exists.
How to use it
Select a store, date range and resolution.
Click any node or aisle to enter the "X-ray" state, which dims unrelated flows and isolates that path.
Scrub the timeline to see how flow changes across the trading day.
Tips
Bottlenecks flagged by the pattern analyser are points where inbound volume far exceeds onward movement — usually congestion or a decision point.
Coverage below expectation in a rear aisle is normally a routing problem, not a product problem: consider a destination category or signage there.
1-minute resolution is the most accurate view but requires minute-level data for the selected days.
8. Google Review Sentiment (side panel)
What it is AI analysis of your Google review text, opened by clicking the Google review score card.
What you see
Overall sentiment split across positive, neutral and negative reviews.
Extracted tags and themes: recurring topics such as queueing, staff, cleanliness, stock availability or pricing.
Representative quotes supporting each theme.
Trend of sentiment over time.
How to use it
Click the Google review score card on the store overview or in the store detail slide-over.
Read the negative themes first — they are usually operationally fixable.
Cross-check a theme such as "long queues" against the visits timeline to find the hours it happens.
Tips
Results are cached, so repeat opens are instant; new reviews are analysed on the next refresh.
Tags reflect what customers actually wrote — they are not a fixed list, and they change as your reviews change.
9. Datamine Copilot (AI assistant)
What it is A conversational analyst that answers questions about your data in plain language.
What it knows The Copilot receives the same data the dashboards use, for the store and period you are viewing:
Footfall, visits and growth.
Sales, transactions and conversion rate, when imported.
Demographic composition.
Zone heatmap statistics.
Promo stand effectiveness and product assignments.
Google review sentiment and extracted tags.
How to use it
Open the Copilot from any dashboard — it inherits the current store and date range.
Ask direct questions: "Which promo stand had the best engagement last week?", "Why did conversion drop on Saturday?", "Which age group grew the most this month?".
Ask for actions: "Give me three recommendations to raise conversion rate."
Tips
Narrow the date range before asking; the answer is only as focused as the context you give it.
The Copilot explains its reasoning based on your data — it does not invent figures. If it says data is unavailable, the underlying dataset is missing for that period.
10. Exports and reporting (cross-tab)
What it is Every major table and chart can be exported for reporting.
What you get
CSV for raw data and further analysis.
Excel with formatting and, for the visits timeline, a documentation header explaining each formula.
PDF for board-ready snapshots of charts.
Tips
Exports respect the currently applied store, date range and filters.
Calculated columns (conversion rate, ATV, sales per visitor) are included pre-calculated so recipients do not need to recompute them.