Glossary
Retail Intelligence: Key Terms Defined
The language of physical retail intelligence — from visitor segmentation to the loop that closes between what a store sees and what it earns. All terms as defined and used by Storalytic.
The Loop
The Loop (SEE → ACT → PROVE)
Storalytic's operating principle, and the structure of the product. SEE: behaviour on the shop floor is measured continuously at zone level — who passed, who engaged, who was served. ACT: that measurement is turned into one concrete, named move for a specific zone, ranked ahead of every other move available this week. PROVE: once the move is made, the platform measures whether it worked — comparing equal before/after windows adjusted for the underlying trend — and remembers the result. A store that only sees has a report. A store that runs the loop has a method. Most retail analytics stops at SEE; the loop is what makes the next decision better than the last one.
PROVE (the guidance loop)
The closing step of the loop, and the part no other in-store analytics platform performs. When a store makes a change — moves a display, restaffs an hour, re-signs a zone — the change is logged with its date. The platform then compares an equal window before and after, adjusted for the store's underlying trend so that a rising or falling market is not mistaken for the effect of the change, and reports the outcome honestly as measured, modelled, or still building. Results that came from a tracked action with sales entered are reported as proved €; everything else remains estimated €. Proved outcomes are retained, so the store accumulates a record of what actually works on its own floor. Storalytic measures realised impact; it does not promise a return.
The Four Gears
How Storalytic frames a benchmark. Rather than comparing a store to an abstract ideal, performance is measured against four progressively harder targets: (1) the store's own best weeks, (2) its best comparable zone, (3) the peer cohort at the 80th percentile, and (4) best-in-class, held as a horizon rather than a target. The active benchmark is always the next gear up, never the furthest one — which keeps the goal credible and reachable. The gap to that next gear is what the platform expresses as € within reach.
€ Within Reach
The monetary size of the jump from a zone's current performance to the next gear — the realistic growth available if a specific, achievable improvement is made. It is a forward-looking growth figure, not a loss figure: it describes demand already present on the floor that is not yet being converted. € within reach is always a modelled estimate, derived from measured behaviour combined with the store's own conversion and basket assumptions. Behaviour is measured; money is modelled — and Storalytic labels the difference everywhere it shows a number.
Visitor Segmentation
Clear Lingerer
A store visitor who remains in a defined zone for long enough, and with sufficient behavioural consistency, to be classified as actively engaged with the product or display. A Clear Lingerer has demonstrated purchase intent through their dwell behaviour — examining, comparing, or considering. The classification is made by behavioural dwell-and-consistency models applied to anonymised movement data. Clear Lingerers are the primary commercial signal in Storalytic's analytics layer: their count, their rate relative to total zone visitors, and their conversion or non-conversion define the core opportunity metrics. Sometimes called the physical click — the in-store equivalent of a product page view online.
Short Lingerer
A store visitor who pauses in a zone briefly — longer than a walk-by, but not for long enough or with enough behavioural consistency to be classified as a Clear Lingerer. Short Lingerers typically notice a display or product but do not enter sustained engagement. They represent a mid-tier signal: not passive, but not yet activated. In the engagement funnel, Short Lingerers sit between Walk-Bys and Clear Lingerers.
Walk-By
A store visitor who passes through or near a zone without stopping or engaging. Walk-Bys are detected and counted but represent low or no commercial engagement with that zone's products or displays. A high Walk-By rate combined with a low Clear Lingerer rate typically indicates a display problem, a layout problem, or a product–placement mismatch — the zone is receiving traffic but not capturing attention.
Core Concepts
The Gauges
ATTRACT / SERVE / DEPLOY
Storalytic's three operational gauges, each answering a different commercial question: grow, protect, save. ATTRACT (grow) measures the effectiveness of zones and displays at drawing visitors in — the rate at which visitors transition from Walk-By or Short Lingerer to Clear Lingerer — and expresses the gap as € within reach. SERVE (protect) measures the quality and speed of service at points of assistance, consultation, or checkout, through queue depth, wait time, and service cycle duration. DEPLOY (save) measures staffing efficiency — idle or over-staffed capacity that could be redeployed to where demand actually is. Together the three give store and area managers one reading of what their store needs on any given day, and what it is worth acting on first.
ATTRACT
The grow gauge — the first of Storalytic's three. ATTRACT measures commercial engagement at the zone level: what proportion of zone visitors become Clear Lingerers. A low ATTRACT score indicates that traffic is reaching a zone but not converting to engagement — pointing to display, signage, product mix, or layout issues. ATTRACT measures the front end of the physical conversion funnel, and expresses the distance to the next gear as € within reach.
SERVE
The protect gauge — the second of Storalytic's three. SERVE measures service quality at zones where staff interaction or transaction processing occurs: consultation points, service counters, and checkouts. It captures queue depth, average wait time, and service cycle duration. A low SERVE score indicates that engaged visitors are being lost or frustrated at the point of service — turning potential conversions into abandoned transactions. SERVE surfaces an estimated Queue Loss €, always presented as a modelled figure with a range rather than a hard recovered amount.
DEPLOY
The save gauge — the third of Storalytic's three. DEPLOY measures staffing efficiency: how well staffed hours line up with the demand actually present on the floor. It identifies idle or over-staffed capacity that could be redeployed to periods and zones where visitors are engaging but not being reached, and prices that imbalance as a saving. DEPLOY is deliberately not a measure of movement, congestion, or bottlenecks — queue and waiting losses belong to SERVE, and counting them twice would overstate the opportunity.
Classification
Pattern Classification
The colour-coded chip Storalytic assigns to every zone, describing what is happening in that zone's funnel. It is a diagnostic label: it points to the lever with the most headroom, so the first action is obvious. There are six patterns.
Awareness (blue). Many visitors, low engagement. People pass the zone but do not stop.
Attention (orange). High engagement, low conversion. People stop and consider, but do not buy.
Reach (purple). Few visitors, high engagement. A strong zone that too few people reach, a hidden gem.
Strong (green). Near the ceiling on both engagement and conversion. A top performer.
Neutral (grey). Average on both axes.
Traffic. A counts-only or tripwire zone, where engagement is not measured.
Strategic Positioning
A merchandising view that plots each product zone on two axes: its share of store visitors, and the euro it earns per visitor. Where Pattern Classification says what is wrong inside a zone's funnel, Strategic Positioning says what role the zone plays in the store as a whole. It sorts zones into four roles.
Stars. High visitor share and high euro per visitor. The zones carrying the store.
High Runners. High visitor share, lower euro per visitor. Volume drivers with room to earn more from each visit.
Spotlight Candidates. Low visitor share, high euro per visitor. Worth getting more people in front of.
Cold Spots. Low on both. Underperforming space.
Technology
EdgAlytic
Storalytic's proprietary edge computing device deployed inside retail stores. The EdgAlytic device connects to existing CCTV or IP cameras and runs AI computer vision models locally — on-premises, inside the store. Video is processed entirely on the EdgAlytic device and never transmitted to any external server. Only anonymised, aggregated behavioural metrics leave the store. The EdgAlytic architecture is the foundation of Storalytic's privacy-by-design approach and is the hardware equivalent of the Offline Cookie concept.
Allen
The companion who carries the loop. Allen is a role, not a technology: it translates zone-level behavioural data into plain-language operational recommendations — naming what is within reach, flagging anomalies, answering questions about the store in everyday language, and delivering daily and weekly summaries in Dutch or English without requiring analytical skill from the store team. Allen also carries the PROVE step back to the floor, reporting whether the last change actually worked. It is the interface between what the platform measures and the people who act on it.
Computer Vision (retail)
The application of AI image analysis models to video feeds from retail store cameras to extract behavioural data about visitor movement, dwell, and engagement. In retail, computer vision replaces manual observation with continuous, objective, zone-level measurement. Storalytic's computer vision models detect presence, assign anonymous movement identifiers and classify behaviour on-premises, with zone metrics aggregated each trading day. The output is not video or images but structured numerical data: counts, durations, and classification rates.
Analytics
Missed Value
An older industry term for the gap between the purchase intent a zone attracts and the transactions it completes — calculated as the number of non-converting Clear Lingerers multiplied by the average transaction value for that zone's category. Storalytic no longer uses missed value as a headline metric. The same arithmetic is now expressed as € within reach, because the figure describes demand that is still available to capture rather than money already lost, and because framing it as a loss tells a store manager nothing about what to do next. Any such figure is a modelled estimate, never a guaranteed recovery.
Engaged Value
The total estimated revenue potential represented by all Clear Lingerers in a zone during a given period. Engaged Value = number of Clear Lingerers × average zone transaction value. It represents the commercial opportunity that entered the zone, regardless of whether it converted, and it is the denominator against which a zone's conversion efficiency — and the € within reach to the next gear — is calculated. Like every money figure in the platform, it is modelled from measured behaviour rather than directly observed.
Zone Analytics
The practice of measuring and analysing visitor behaviour within discrete, defined areas of a physical store — rather than treating the store as a single undifferentiated space. In Storalytic, zones are defined during deployment (product areas, displays, service counters, checkout, entrance) and each zone becomes an independently measurable unit. Zone analytics enables funnel analysis at the product-area level: how many visitors entered, how many engaged, how many converted, and where value was lost.
Dwell Time
The duration for which a visitor remains within a defined zone. Dwell time is measured per zone visit and aggregated to produce average dwell times, dwell time distributions, and dwell-by-segment breakdowns (Walk-By, Short Lingerer, Clear Lingerer). Dwell time is a core input into engagement classification — longer, more consistent dwell is a precondition for Clear Lingerer status. It is also used to diagnose specific friction points: unusually short dwell at a high-margin zone indicates a display or product communication failure.
Privacy & Compliance
GDPR Compliance (in-store AI)
The regulatory framework governing the use of AI computer vision in physical retail environments under the European General Data Protection Regulation. Storalytic's architecture is designed for GDPR compliance by default: video is processed locally on EdgAlytic edge devices (never transmitted), only anonymised aggregated data is stored and transmitted, no facial recognition is used, no biometric data is collected, and no individual can be identified from the data Storalytic produces. Processing is justified under Article 6(1)(f) — legitimate interest for operational improvement — where individual identification is not possible.
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