Super Vision vs RetailNext for Store Operations

Short answer

RetailNext is a retail analytics platform built on its own ceiling-mounted Aurora sensor. It counts shoppers, measures paths and dwell, joins traffic to POS for conversion, and pilots layout and merchandising concepts against control stores. Super Vision turns the cameras stores already have into evidence of how the floor operates, then helps operators simulate, test and measure operational changes, such as checkout staffing or a shift-change gap, before rollout. Choose RetailNext for audited traffic and merchandising tests; choose Super Vision for testing how stores run.

Last verified: 2026-11-04

Super Vision makes one of the products compared here. We have tried to describe every option fairly from its own public materials; if something is out of date, tell us and we will correct it.

At a glance

Everything said about RetailNext below comes from RetailNext's own pages. Superscript numbers link to the source list below, which gives the date each page was read.

	RetailNext	Super Vision
Built for	A retail analytics platform for physical stores, with Pulse AI for asking questions about the floor in plain language (RetailNext¹). Its data starts with the Aurora sensor (RetailNext²).	AI-powered operational intelligence and optimization for multi-location retail. Maya, the in-product assistant, answers questions about operations, and the platform is organized around deciding and testing operational changes.
Who it is pitched to	Operations, asset protection, merchandising, store design and marketing teams (RetailNext¹). Insights is described for "merchandising and store design teams" (RetailNext⁴).	Multi-location operators: COOs, VPs of store operations, and the district and regional managers who carry changes into stores.
Hardware	Aurora, a single ceiling-mounted sensor that RetailNext designs and manufactures, which "doesn't depend on your existing cameras" (RetailNext²). Sensor hardware is part of the subscription (RetailNext⁵). Asset Protection integrates with existing analog and IP cameras (RetailNext⁶).	Software on the cameras a store already has, with no sensor to install. Compatibility is confirmed during onboarding, and some camera systems need vendor or IT help.
What it measures	Traffic with staff excluded, paths, dwell and engagement (RetailNext²); conversion when joined to POS, plus occupancy, passby and interaction rate (RetailNext³).	Operational activity inside camera coverage: when lines build and clear, whether registers and service positions are covered, how zones are used, plus visitor traffic and peak periods. Findings link to the evidence. It does not claim exact headcounts, and sales come from POS.
Helps decide what to change and test	Traffic Analytics forecasts traffic by store, day and daypart and recommends staffing to match (RetailNext³). Insights pilots a layout, display or merchandising concept in a few stores against control stores (RetailNext⁴).	Three steps for operational changes: Simulations model a staffing or workflow change against each store's history (projected), Benchmarks measure the change after it happens, adjusted for traffic and day of week (observed), and Experiments compare test stores with comparable control stores (validated). It suggests which tests to run, based on history across your locations.
Pricing as published	No list price on its pricing page. An online estimate based on locations, entrances, store type and region returns a cost per store per year; installation and site survey are listed among possible extras (RetailNext⁵).	Starter from $499 per location per month ($449 a month on an annual plan), Core from $799 for 6 to 10 camera feeds; price depends on camera feeds and hours covered (pricing).
Where it is strongest	A sensor-based traffic count that RetailNext says is manually audited at every installation (RetailNext²), traffic-normalized conversion, and pilots of merchandising and layout ideas.	Working out which operational change to roll out, with a record of what happened on the floor and a test behind the decision.

The decision this buyer is making

Most pages that rank for RetailNext comparisons are written by other counting and analytics vendors, and they argue about counting: install effort, accuracy and price per door. That matters if the job is counting. For a VP of store operations, the question is usually one step later.

It tends to sound like this: "Our traffic counts tell us Saturday afternoons convert worse at a dozen stores, even though the schedule matches the traffic forecast. Before we spend another associate-shift in every store, what is actually going wrong at the registers, and which fix holds up at a few stores first?"

RetailNext's own pages make a strong case for the first half of that sentence: knowing traffic precisely and staffing to it. They also describe a test-and-learn product, so the honest comparison is not "testing versus no testing". It is which kind of change each one is set up to test, and what evidence it reads the result from.

Where they differ

What kind of change gets tested

RetailNext⁴ describes Insights as the way to "pilot a new layout, display, or merchandising concept in a handful of stores," measured with shopper dwell, engagement and path data against control stores. That is test-and-learn for what the store looks like and how shoppers move through it.

Super Vision's Experiments run a change at selected stores against comparable control stores too, but the changes it leads with are about how the store runs: when the second register opens, where associates stand at peak, what happens during a shift change, which task pulls people off the floor. The pages we read from RetailNext do not describe testing staffing or process changes against control stores.

Operational consequence: if the proposal on the table is a new fixture or floor layout, RetailNext's Insights is aimed squarely at it. If the proposal is a labor or process change, ask any vendor to show how its test reads the result for that kind of change.

A count versus a record of the floor

Aurora is a purpose-built sensor, and RetailNext² says each installation gets a manual accuracy audit, with staff excluded so metrics are customer-only. That gives a clean, comparable traffic number across a fleet.

Super Vision works from the cameras already in the store and produces a timestamped record of activity in the zones those cameras cover, with evidence attached. It does not claim exact foot traffic or occupancy counts. What it adds is the "what was happening" layer: a line of seven with one register open, or the second register staffed but its associate called away to a pickup counter.

Operational consequence: a count tells a district manager where to look. A record tells them what to fix, and lets a store manager see the same evidence instead of debating the number.

Staffing to forecast versus testing how staff are deployed

RetailNext³ says Traffic Analytics forecasts traffic by store, day and daypart, then recommends staffing matched to that predicted demand. That answers how many people a store should schedule.

A schedule that matches traffic can still go wrong on the floor if the right number of people are in the wrong places. Super Vision measures staff presence and service coverage where cameras can see, so the question becomes whether scheduled hours turned into coverage at the moments that mattered. Simulations then model a change against the store's own history before anyone commits labor.

Operational consequence: forecast-based scheduling and deployment evidence are complements. A chain already staffing to a forecast can still find hours where coverage, not headcount, is the gap.

"Benchmarks" means two different things

The word appears on both sides, so it is worth being precise. RetailNext's Benchmarks Hub⁷ publishes monthly shopper traffic trends across thousands of locations, by region and sector, and RetailNext³ describes benchmarking conversion against industry peers. That tells you how your traffic compares with the market.

Super Vision's Benchmarks compare your own stores before and after a change you made, adjusted for traffic and day of week. That tells you what changed after your decision. It is an observed change, not proof of cause; that is what a controlled Experiment is for.

Operational consequence: use market benchmarks to judge whether a dip is yours or the industry's. Use before-and-after benchmarks to judge your own changes.

Rollout effort and cost shape

RetailNext's subscription bundles the Aurora sensor, software, warranty and annual audits, with an estimate driven by locations and entrances per store; installation and site survey may be extra (RetailNext⁵). Super Vision is software priced per location by camera feeds and daily monitoring hours, with no sensor to install, but each store's camera system has to be compatible and some need the camera vendor's or IT team's help.

Operational consequence: for a 60-store program, one path is a hardware rollout per entrance with a predictable count at the end; the other is a compatibility check per store, which at some stores means help from the camera vendor or IT team.

A worked scenario (hypothetical)

Everything in this scenario is invented to show the method: the retailer, the hours, the costs and the outcome are not from a customer.

A 45-store home goods chain already runs sensor-based traffic counting. Its reports show Saturday 1 p.m. to 4 p.m. conversion is down at 12 stores, while the schedule matches the traffic forecast. The regional team proposes adding a cashier for those three hours every Saturday in all 45 stores: 45 stores x 3 hours x 52 weeks = 7,020 hours a year, or $133,380 at an assumed $19 loaded hourly cost.

With a traffic and merchandising analytics platform. The team can confirm traffic held steady and conversion fell, by store and daypart, and staffing already matched the forecast. It could pilot the extra cashier in a few stores and compare conversion against control stores. The weak point is upstream: nothing in a traffic count says why the registers struggled, so the pilot tests the first idea in the room.

With Super Vision, through the improvement loop:

Monitor. Evidence from the 12 stores shows the second register was scheduled and staffed, but during most Saturday line peaks its associate was at the counter handing over buy-online, pick-up-in-store orders, which peak at the same hours.
Simulate. Using each store's own Saturday history, Super Vision models two options: the extra cashier, or staging pickup orders before 1 p.m. and moving handover to the service desk. The handover change projects most of the coverage gain without added hours. That is a projection, not a result.
Experiment. The handover change runs at 5 of the 12 stores, with 5 comparable stores as controls, for six Saturdays.
Benchmark. Results, adjusted for traffic and day of week, show second-register coverage during lines and wait times at test stores against controls. The chain's existing traffic counts and POS data stay the source for conversion.
Scale. If the result holds, the handover change goes to the stores where pickup volume collides with the Saturday peak. The extra cashier is funded where evidence still shows a gap, not across 45 stores.

Treat any saving in this scenario as a possibility, not a forecast: it excludes Super Vision fees and rollout costs, and it counts only if the improvement is verified, attributable to the change and still there months later.

When RetailNext is the better choice

Your reporting depends on an audited entrance count, for conversion, landlord or board figures. Super Vision does not claim exact traffic or occupancy counts.
The change you want to test is a layout, fixture, display or merchandising concept, read through shopper paths and dwell. That is what RetailNext⁴ describes Insights for.
You want market traffic benchmarks by region and sector (RetailNext⁷).
You want counting and loss-prevention video from one vendor, which RetailNext⁶ describes with Asset Protection.
Aurora sensors are already installed and trusted by your finance team.

When Super Vision is the better choice

The proposal on the table changes how stores run, such as when a second lane opens, who covers the floor at peak, how a shift handover works or where a recurring task happens.
You want a staffing or process change tried at a handful of stores, with comparable stores as the yardstick, before regional budgets move. If you have one store, you can still try the change there; expect a before-and-after reading, since there is no control group.
Your district managers visit each store a few times a quarter and need to see what the floor looked like on the days they were not there.
Installing a new device at every entrance is a barrier, and you would rather begin with the cameras on the ceiling today.

What neither does

Neither produces revenue figures from its own data. Conversion and sales need transactions from POS; both tools depend on that join.
Neither settles cause from a single store's before-and-after. That reading is an observed change. Cause needs a comparison group that did not get the change over the same weeks.
Neither covers what it cannot see. Off-camera tasks and areas outside a sensor's view are missing from the data, whichever tool you use.
Neither is a fair way to rate individual associates. Super Vision reports by zone, position and hour, and a staffing test result describes a process, not a person.

Using them together

A chain with RetailNext counting does not need to replace it to use Super Vision. A workable split: the sensor count stays the traffic denominator and the conversion number the business reports; Super Vision explains what happened on the floor during the hours that number moved and runs the operational tests; merchandising tests stay where they are. Read every test against the same traffic source, so the results line up.

For the measures behind this kind of test, see how to measure checkout wait time and scheduled versus deployed labor, or how Super Vision approaches checkout wait times and staffing coverage. The category view is in people counters vs video analytics, and more comparisons are on the comparisons hub.

Product names are trademarks of their respective owners. This page is not affiliated with or endorsed by them. Corrections: team@supervision.work.

Frequently asked questions

What is the main difference between RetailNext and Super Vision?

RetailNext's pages describe a platform built on its own Aurora sensor for traffic counting, shopper paths and conversion, with an Insights product for piloting layouts, displays and merchandising concepts against control stores. Super Vision uses compatible existing cameras to record how stores operate, such as lines, register coverage and zone activity, and is built for testing operational changes like checkout staffing before rollout.

Does RetailNext work with existing cameras?

It depends on the product. RetailNext's Aurora page says the sensor does not depend on existing cameras, because Aurora combines counting, behavioral analytics and video in one ceiling-mounted unit. Its Asset Protection page says that product integrates with existing analog and IP cameras. Super Vision works with compatible existing camera systems through authorized access; some camera systems need help from the camera vendor or IT team.

How much does RetailNext cost?

RetailNext's pricing page does not list a price. It offers an online estimate based on the number of store locations, entrances per store, store type and region, shows the result as a cost per store per year, and lists installation and site survey among costs that may be extra. Super Vision's published pricing starts at $499 per location per month ($449 a month on an annual plan), with a $799 Core plan for 6 to 10 camera feeds.

Is RetailNext Insights the same as Super Vision Experiments?

Both compare test stores with control stores before a wider rollout. RetailNext describes Insights as a way for merchandising and store design teams to pilot layouts, displays and merchandising concepts and read shopper dwell, engagement and path data. Super Vision's Experiments are aimed mainly at operational changes, such as staffing, register coverage and store processes, and are read from evidence of what happened on the floor.

Can Super Vision replace a RetailNext traffic counter?

Not if you depend on an exact traffic count. Super Vision measures visitor traffic and peak periods where cameras cover the zone, but it does not claim exact foot traffic or occupancy counts. Teams that report conversion from audited entrance counts should keep their counter and use Super Vision for the operational questions the count raises.

Can a chain use RetailNext and Super Vision together?

Yes, where the questions differ. A traffic count from a dedicated sensor can stay the denominator for conversion, while camera evidence explains what happened on the floor during the hours that conversion moved and supports tests of staffing or process changes. Keep the two sources labeled separately rather than blending them into one score.

Sources

RetailNext homepage: AI Retail Analytics Platform for Physical Stores. RetailNext, 2026. Vendor's own page. Read 2026-10-30; re-read 2026-11-04.
Aurora sensor. RetailNext, 2026. Vendor's own page, including its FAQ. Read 2026-10-30; re-read 2026-11-04.
Traffic Analytics. RetailNext, 2026. Vendor's own page, including its FAQ. Read 2026-10-30; re-read 2026-11-04.
Insights: test-and-learn retail analytics. RetailNext, 2026. Vendor's own page. Read 2026-10-30; re-read 2026-11-04.
Pricing. RetailNext, 2026. Vendor's own page (estimate form; no list price shown). Read 2026-10-30; re-read 2026-11-04.
Asset Protection. RetailNext, 2026. Vendor's own page. Read 2026-10-30; re-read 2026-11-04.
Benchmarks Hub. RetailNext, 2026. Vendor's own page. Read 2026-10-30; re-read 2026-11-04.
Super Vision pricing. Super Vision, 2026. Read 2026-10-30; re-read 2026-11-04.