RetailNext — Homepage https://retailnext.net RetailNext turns in-store data into actionable insights for retail leaders. From traffic and shopper behavior to asset protection and merchandising performance, the platform gives retailers a complete picture of what happens on their floor, and the tools to act on it. RetailNext was founded in 2007 with the goal of bringing e-commerce-style analytics to physical retail. The company is headquartered in Campbell, California, and serves more than 600 retail customers across more than 100 countries, with roughly 240 employees and an average customer partnership length of over 10 years. The platform is built around four core products. Traffic Analytics delivers 95 to 99 percent accurate people counting and converts that data into staffing recommendations and true conversion measurement. Insights lets merchandising and store design teams pilot a new layout, display, or concept in a handful of stores and validate the results before committing to a fleet-wide rollout. Asset Protection combines AI-powered behavioral analytics with point-of-sale integration to identify loss-prevention threats before they become losses and to shorten investigation time. All three are powered by Aurora®, RetailNext's own ceiling-mounted IoT sensor, which counts people, captures behavioral data, and records high-resolution video from a single device. Beyond the core products, RetailNext publishes sector-specific solutions for retail categories such as apparel and footwear, department stores, convenience stores, jewelry, restaurants, retail banking, shopping malls, and facilities, as well as use-case-specific solutions for business intelligence, marketing and customer experience, merchandising, security, store design, and store operations teams. RetailNext also operates a Benchmarks Hub with monthly-updated foot traffic data across thousands of retail locations in North America, EMEA, and APAC, giving retailers an external reference point for their own performance. --- Traffic Analytics https://retailnext.net/platform/traffic-analytics Traffic Analytics is RetailNext's foot traffic measurement and staffing product. Most retailers make their most expensive operational decisions, including staffing, scheduling, and store hours, based on guesswork rather than data. Traffic Analytics is built to replace that guesswork. Powered by RetailNext's AI-driven Aurora® sensor, it delivers 95 to 99 percent accurate foot traffic counts and converts that raw count data into actionable intelligence: recommendations for staffing the right number of people at the right times, true conversion-rate measurement that ties store visits to actual sales, and comparative performance data across an entire store network. Because the underlying sensor hardware is the same across RetailNext's product line, Traffic Analytics data can be combined with the company's other products, including Insights for layout and merchandising analysis and Asset Protection for loss-prevention use cases, without deploying additional hardware. The product is aimed at retail operations, store planning, and workforce management teams who need to move from anecdotal observation of store performance to a quantified, benchmarkable measure of shopper volume and conversion at every location. RetailNext frames this as a way to stop treating staffing and scheduling decisions as bets and start treating them as data-backed decisions, backed by a sensor network the company describes as delivering industry-leading counting accuracy across a large, multi-country retail customer base. Matt Evans, Senior Director of Business Insights and Analytics at The Container Store, is quoted on the page describing the shift this created: "Traffic counting has long been considered an ancillary metric at The Container Store due to its unreliability, however, through our partnership with RetailNext it has become a critical metric for labor planning and understanding our true customer conversion." --- Insights https://retailnext.net/platform/insights Insights is RetailNext's test-and-learn retail analytics product for merchandising and store design teams. Every store change is normally a bet until it is tested, and Insights is built to remove that guesswork by letting a retailer pilot a new layout, display, or merchandising concept in a small set of stores, observe exactly how shoppers respond using data from RetailNext's Aurora® sensor, and validate the outcome before committing to a fleet-wide rollout. Insights goes beyond basic traffic counting to map the complete customer journey, revealing which zones engage shoppers, which layouts convert, and where shoppers disengage before they reach the register, rather than reporting a single aggregate traffic or conversion number for the whole store. The same underlying product also supports RetailNext's Business Intelligence use case, described as going beyond a basic traffic dashboard by combining shopper-behavior data with AI-powered analysis, predictive modeling, and a benchmark dataset built from RetailNext's broader retail customer base, so a retailer can understand not just what happened in a store but what it means and what to test next. Insights runs on the same Aurora sensor hardware as Traffic Analytics and Asset Protection, so a retailer already using either of those products can add Insights without installing new hardware. It is aimed at merchandising, store design, and retail analytics or business-intelligence teams who need to test a concept in a handful of locations, measure the actual shopper response, and only then decide whether to roll it out network-wide, replacing what RetailNext frames as guesswork-based store planning with an evidence-based test-and-validate process before capital or labor is committed to a change across an entire store fleet. Adolfo Rodríguez of The Estée Lauder Companies is quoted on the page: "RetailNext's advanced suite of shopper journey analytics, covering the full path of a shopper in-store, delivers deep insights into how customers shop and interact with our product, fixtures, and in-store technology." --- Asset Protection https://retailnext.net/platform/asset-protection Asset Protection is RetailNext's loss-prevention and store security product. Most loss prevention teams still work reactively, reviewing camera footage after an incident has already cost the business money rather than catching risk in progress. Asset Protection is designed to change that by combining AI-powered behavioral analytics with point-of-sale data integration. The product identifies suspicious behavior and transaction patterns that indicate a threat before it turns into an actual loss, and it is built to cut the time investigators spend searching through footage by surfacing the relevant video and transaction exceptions directly. The stated goal is to let loss-prevention teams spend less time on after-the-fact investigation and more time on prevention, while also being able to demonstrate a measurable return on their security investment. Asset Protection runs on the same Aurora® sensor hardware used across RetailNext's platform, so retailers already using Traffic Analytics or Insights do not need separate hardware to add asset-protection capability. The product is aimed at loss prevention, risk management, and store operations teams responsible for reducing shrink and protecting both inventory and employees, and it is positioned as part of a broader shift in retail security from reactive footage review toward proactive, data-driven threat detection integrated with point-of-sale systems. Brent Paulsen of UNTUCKit is quoted on the page: "When looking for a solution that would help us run our stores and protect our teams and assets, it became clear that RetailNext was the best choice on the market. We are able to save ~40% versus using separate systems for traffic counting and loss prevention. During the pandemic, the system has also been incredibly helpful for remote compliance monitoring." --- Aurora® Sensor https://retailnext.net/platform/aurora-sensor Aurora® is RetailNext's own IoT sensor hardware: a single ceiling-mounted device, designed and manufactured by RetailNext rather than sourced from a third party, that powers every other product in the platform. One Aurora unit counts people with a stated accuracy of 95 to 99 percent, captures behavioral analytics data, and records high-resolution video simultaneously, replacing what would otherwise require separate counting, analytics, and camera hardware installed side by side. Because Traffic Analytics, Insights, and Asset Protection all read from the same Aurora sensor feed, a retailer can add any of those products to an existing Aurora deployment without installing new hardware, and can start with one use case and expand to others later without a second site visit. RetailNext positions Aurora as built for global deployment across large, multi-country store networks, which the company connects to its broader customer base of 600+ retailers across 100+ countries. The product page frames Aurora as the physical foundation the rest of the platform depends on: the sensor generates the underlying count, behavior, and video data, while Traffic Analytics, Insights, and Asset Protection are the software layers that turn that raw sensor data into staffing, merchandising, and loss-prevention decisions respectively. For a retailer evaluating RetailNext, Aurora is effectively the one hardware decision behind an otherwise software-driven product lineup, and it is the common thread that lets a retailer add products later without re-instrumenting stores. Jacky Leung of Bluebell Group is quoted on the page: "Our retail brands in Taiwan have benefited tremendously from the deployment of RetailNext. The data is extremely accurate and very easy for our teams to use." --- Our Advantage https://retailnext.net/platform/our-advantage Our Advantage is RetailNext's positioning page explaining why retailers choose RetailNext over other approaches to retail analytics. Its core argument is that a retail analytics platform is a long-term commitment: the staffing, layout, campaign, and asset-protection decisions a retailer makes will only be as good as the underlying data platform, so the choice of vendor compounds in value or cost over years, not months. RetailNext presents itself as the leading retail intelligence platform for physical retail, citing 600+ retailers across 100+ countries as customers, over 19 years of purpose-built retail analytics development since its 2007 founding, and what it describes as the industry's most accurate underlying sensor data as its foundation. The page organizes its case around four themes: industry-leading counting accuracy from the Aurora® sensor network, a single integrated platform covering traffic, behavior, and asset protection rather than separate point solutions, proven deployment at enterprise scale across large multi-country store networks, and what RetailNext describes as the industry's largest retail benchmark dataset, built from its own customer base's aggregated traffic data. The page includes testimonials from several named enterprise retail customers describing their results with the platform: Nilesh Khalkho, CEO of Sharaf DG, is quoted saying "We have combined the full suite of data from RetailNext with our operational data from other systems to provide actionable insights to our brand partners at every shop in shop. Not only are we able to provide insights to brands, but the many brands we work with use the data to make more informed decisions about their businesses to improve performance." Marc Mastronardi, Chief Store Officer at Macy's, is quoted saying "As we continue to invest in data-driven decision making, having visibility into the in-store customer shopping journey is critical. RetailNext has been a great partner to us, and we believe this level of insight is key to beginning in understanding traffic and shopping patterns in our locations." And Matt Evans, Senior Director of Business Insights and Analytics at The Container Store, is quoted saying "Traffic counting has long been considered an ancillary metric at The Container Store due to its unreliability, however, through our partnership with RetailNext it has become a critical metric for labor planning and understanding our true customer conversion." Our Advantage functions as RetailNext's central "why us" argument, distinct from the individual product pages, which describe what the platform does, and the Solution Comparison page, which lays out a more structured framework for evaluating RetailNext against alternative approaches to retail analytics. --- Customer Stories https://retailnext.net/platform/customer-stories Customer Stories is RetailNext's case-study index page, aggregating outcome-based case studies from its retail customer base. The page states that more than 600 retailers across more than 100 countries use RetailNext, ranging from global luxury brands to regional specialty chains, and frames its value proposition around measured outcomes rather than features: better conversion rates, smarter labor scheduling, stronger loss prevention, and store designs that work for shoppers, each tied to a specific customer example rather than a general claim. Case studies linked from this page include named customers such as Boggi Milano, The Vitamin Shoppe, Daniel's Jewelers, Dave & Buster's, Hurley, Camper, UNTUCKit, Sharaf DG, and Megane No Tanaka, spanning apparel, specialty retail, jewelry, entertainment and dining, footwear, and multi-brand retail. Each case study describes a named customer's specific use of RetailNext, for example Traffic Analytics for staffing or Asset Protection for loss prevention, and the measured result they achieved, rather than a generic product description. Customer Stories functions as the evidentiary counterpart to the Our Advantage and Solution Comparison pages: where those pages make RetailNext's general case for itself, Customer Stories backs that case with individually attributed, named-customer outcomes across a range of retail sectors and geographies, intended for a buyer who wants proof from a comparable retailer before evaluating the platform further. --- Solution Comparison https://retailnext.net/platform/solution-comparison Solution Comparison is RetailNext's page for retailers evaluating how to approach retail analytics before choosing a vendor or method. The page frames the decision as a choice among four broad approaches: adopting a single integrated platform covering traffic, behavioral analytics, and asset protection from one vendor; assembling point solutions from separate vendors for counting, heat mapping, loss prevention, and analytics individually; building custom analytics in-house with internal engineering resources; or continuing with the status quo of point-of-sale data, manual counts, and assumptions with no dedicated in-store analytics investment. For each approach, the page lays out tradeoffs a retailer should weigh: for point solutions, the burden of managing multiple vendors and reconciling data that was never designed to work together; for building in-house, the timeline, ongoing engineering cost, and risk of an internally built system compared with a proven vendor platform; and for the status quo, the hidden costs of misaligned labor, missed conversion opportunities, unvalidated merchandising decisions, and uncontrolled shrink that come from operating without dedicated analytics. The page also presents a comparison table evaluating integrated retail-analytics platforms against a set of named criteria: people-counting accuracy, where RetailNext states 95-99% against a stated 75-85% for the alternatives the table compares against; staff-exclusion capability, described as advanced AI-based on RetailNext's side versus manual or basic methods elsewhere; breadth of behavioral analytics, full suite versus basic or requiring a separate vendor; whether asset protection is unified into the same platform or requires a separate vendor; availability of predictive analytics and AI, included versus unavailable or limited; the size of the underlying benchmark dataset, described as billions of interactions versus limited or unavailable; enterprise scalability, citing RetailNext's 600+ retailers across 100+ countries; IT deployment complexity, a stated 4-6 weeks for RetailNext versus variable or complex elsewhere; SOC 2 Type II compliance; and average customer tenure, cited at 10+ years. The alternatives in this table are not named competitors — the comparison is presented in general terms against categories of alternative approaches. The page is aimed at buyers still deciding between building, buying a single platform, or piecing together point solutions. --- Apparel & Footwear https://retailnext.net/solutions/apparel-footwear Apparel & Footwear is RetailNext's sector solution for fashion retailers, a category the page describes as facing extended pre-purchase browsing, rapid trend shifts, dramatic seasonal traffic swings, and competition from both online and offline channels. RetailNext positions its shopper-behavior and traffic data as the tool for converting browsers into buyers, optimizing merchandising layouts and displays for maximum impact, and planning inventory and staffing precisely for each season's traffic pattern rather than reacting to it after the fact. The solution is built for retailers across the format spectrum the page names explicitly: luxury boutiques, fast-fashion chains, and footwear specialty stores, using the same underlying Aurora® sensor data that powers RetailNext's core products, including Traffic Analytics for counting and staffing, Insights for testing merchandising and layout changes, and Asset Protection for loss prevention. The page's stated capabilities center on three areas: turning browsing behavior into measurable conversion rather than treating a browse as a missed opportunity, quantifying the return on specific merchandising decisions instead of relying on visual judgment alone, and building season-by-season traffic forecasts instead of using flat historical assumptions. It is aimed at apparel and footwear retail leaders, whether at the merchandising, store operations, or executive level, who need shopper-behavior evidence to make decisions in a category the page characterizes as unusually volatile and fast-moving relative to other retail formats. Darren Bowden, VP of Store Finance at Calvin Klein, is quoted on the page: "RetailNext has helped evolve how we think about our stores at Calvin Klein and we think of them as an extension of our own team. Their robust shopper journey data gives our analytics team a much clearer view into what is happening in our stores, giving us e-commerce style analytics across store layout, marketing, product assortment, and more." --- Convenience Stores https://retailnext.net/solutions/convenience-stores Convenience Stores is RetailNext's sector solution for convenience retail and fuel-plus-retail operators. The page frames convenience retail as a tight-margin, high-volume business where every transaction matters and lean staffing models leave no room for inefficiency or guesswork, particularly around lost sales caused by slow service or poor merchandising. RetailNext's traffic and behavioral data is positioned to help c-store operators maximize revenue per visit, convert more fuel customers into in-store purchasers, optimize staffing and merchandising for peak periods such as morning and evening rushes, and run leaner, more consistent operations across every location in a network rather than store by store. As with RetailNext's other sector pages, the underlying data comes from the Aurora® sensor network shared across the platform, so a convenience retailer can add Traffic Analytics for counting and staffing, Insights for testing merchandising changes, or Asset Protection for loss prevention without new hardware for each capability. The stated capabilities on this page emphasize converting existing foot traffic already inside the store, from fuel purchases or quick errands, into additional purchases; managing the sharp swings in volume that occur during rush periods; and improving basket size through better in-store merchandising decisions backed by actual shopper behavior data rather than assumption. The solution is aimed at convenience-retail operations and merchandising leaders managing large multi-site networks where small per-location efficiency gains compound significantly across the fleet. Ayman Beydoun of BFL Group is quoted on the page: "RetailNext is a critical partner for our stores across the GCC. Their accurate measurement of in-store shopper behavior helps us deliver the best service by giving us the data we need to staff our stores accordingly and to measure performance across all of our locations. This data is critical to driving the best operational and marketing programs for our business." --- Department Stores https://retailnext.net/solutions/department-stores Department Stores is RetailNext's sector solution for large-format, multi-department retail operators. The page frames department stores as facing a challenge specific to the format: rather than managing one store, an operator is effectively managing dozens of specialty departments under a single roof, each with different customer behavior, traffic patterns, and performance dynamics. RetailNext's traffic and behavioral data is positioned to give department-store operators department-level visibility rather than a single storewide traffic number, so performance can be optimized category by category. Named capabilities include department-level performance visibility across a large-format floor, mapping the customer journey through a store size and layout more complex than a typical specialty retailer, and optimizing entrance and traffic-flow strategy for stores with multiple access points. As with RetailNext's other sector solutions, this runs on the shared Aurora® sensor data platform, meaning a department-store operator can combine Traffic Analytics for department-level counting and staffing, Insights for testing layout or merchandising changes department by department, and Asset Protection for loss prevention across a large floor, without separate hardware for each capability. The solution is aimed at operations and merchandising leaders at large-format retailers who need visibility at the department or category level rather than only storewide, since aggregate store traffic numbers can mask very different performance dynamics between, for example, a store's cosmetics department and its home goods department. Marc Mastronardi, Chief Store Officer at Macy's, is quoted on the page: "As we continue to invest in data-driven decision making, having visibility into the in-store customer shopping journey is critical. RetailNext has been a great partner to us, and we believe this level of insight is key to beginning in understanding traffic and shopping patterns in our locations." --- Facilities https://retailnext.net/solutions/facilities Facilities is RetailNext's solution for facilities and workplace management teams, a use case positioned somewhat differently from RetailNext's core retail sector pages. The page frames facilities and workplace teams as being asked to reduce real estate costs, support hybrid-work strategies, ensure safety and occupancy compliance, and improve occupant experience, often all at once. RetailNext's occupancy and space-utilization data, drawn from the same Aurora® sensor platform used across its retail products, is positioned to answer those demands with measured occupancy rather than assumption: understanding how spaces are actually used rather than how they were designed to be used, maintaining continuous occupancy compliance rather than relying on periodic manual checks, and supporting a hybrid workplace strategy with actual attendance data rather than badge-swipe estimates alone. The page states this solution applies to a corporate campus, an office building, or a mixed-use facility, extending RetailNext's underlying people-counting and behavioral-analytics technology beyond a traditional retail-sales use case into a broader occupancy-intelligence use case. It is aimed at facilities, workplace strategy, and real-estate management teams who need continuous, accurate space-utilization data to justify real-estate decisions, prove compliance, or design a hybrid-work policy, using the same sensor hardware and underlying counting accuracy, stated at 95 to 99 percent, that the company's retail-focused products rely on. Prama Bhatt of Ulta Beauty is quoted on the page: "While COVID-19 impacted many parts of our business, one of our top priorities was determining how to best manage in-store capacity. RetailNext was able to quickly deliver an occupancy API to provide our guests a real-time view into how busy our stores were at any given time. This innovative approach reinforced why we value their proactive, nimble expertise as Ulta Beauty's in-store analytics partner." --- Jewelry https://retailnext.net/solutions/jewelry Jewelry is RetailNext's sector solution for jewelry retailers, a category the page describes as operating differently from other retail formats because purchases are high-value, highly considered, and often consultation-driven, with customer journeys that can span multiple visits over weeks or months. The page also notes a specific operational tension in jewelry retail: balancing security needs with maintaining an inviting, unhurried in-store atmosphere, which the page says standard retail analytics don't address. RetailNext's behavioral and traffic data is positioned to help jewelry retailers optimize consultation-area usage and staffing for appointment-driven demand, understand and support the multi-visit customer journey rather than treating each visit as an independent event, and manage security without disrupting the deliberately unhurried shopping experience the category depends on. Named capabilities on the page include understanding the multi-visit journey specifically, recognizing that the same shopper may return several times before a high-value purchase; optimizing consultation areas where staff-assisted, considered selling happens; and maintaining security in a way that doesn't compromise the customer experience. As with RetailNext's other sector solutions, this runs on the shared Aurora® sensor platform, so a jewelry retailer can combine Traffic Analytics, Insights, and Asset Protection capabilities using the same underlying hardware. The solution is aimed at jewelry retail operators managing a purchase category defined by long consideration cycles, high average transaction values, and a need to balance loss prevention against a service experience that depends on not feeling surveilled. David Sherwood of Daniel's Jewelers is quoted on the page: "The RetailNext solution has been central to our store operations, marketing and loss prevention strategies. Our team regularly utilizes the analytics on their portal and the real-time mobile-app to monitor store compliance on a variety of metrics - being able to see live video from any device has been a critical tool for us during COVID." --- Restaurants https://retailnext.net/solutions/restaurants Restaurants is RetailNext's sector solution for restaurant and food-service operators. The page frames restaurant performance as depending on getting three things right at the same time: correct staffing levels at the right times, a consistently good guest experience in every interaction, and adequate protection for people and assets. RetailNext's traffic intelligence is positioned to address all three simultaneously rather than as separate initiatives: accurate guest-traffic forecasting and labor scheduling so staffing matches actual measured demand instead of a fixed schedule, and video security and promotional measurement so loss prevention and marketing-effectiveness questions can be answered from the same underlying data. The page states this applies whether an operator runs a single location or hundreds, positioning RetailNext's traffic and behavioral data, drawn from its Aurora® sensor platform, as the operational intelligence layer separating high-performing restaurant operations from average ones. Named capabilities include staffing to measured demand rather than to a fixed roster or guesswork, reducing guest wait times and improving service throughput during peak periods, and combining people-protection and asset-protection functions with the same sensor data used for traffic counting, so a restaurant operator does not need separate systems for staffing analytics and security. The solution is aimed at restaurant operations leaders, whether at a single-location or multi-location enterprise scale, who need to balance labor cost, guest experience, and security using one shared source of in-store data. Damian Hall, CEO of Tanaka Megane, is quoted on the page: "RetailNext has been instrumental in helping me drive a performance-based culture throughout my organization." --- Retail Banking https://retailnext.net/solutions/retail-banking Retail Banking is RetailNext's sector solution for bank branch operators. The page frames branch performance as under pressure from several directions at once: rising customer expectations, digital-channel adoption shifting in-branch visit patterns, and continuous pressure to reduce operational costs. RetailNext's branch traffic intelligence, drawn from its Aurora® sensor platform, is positioned to help retail banking leaders optimize teller and platform, or advisory-desk, staffing to match actual measured visit patterns rather than fixed schedules; reduce customer wait times during branch visits; and make data-driven decisions about branch design and broader branch-network transformation, including which branches to redesign, resize, or consolidate. Named capabilities on the page include matching staffing to actual customer demand rather than a static schedule, designing branch layouts that are validated against real visit and traffic-flow data rather than assumption, and optimizing the bank's overall branch network using comparable traffic data across every location. As with RetailNext's other sector pages, this solution shares the same underlying Aurora® sensor hardware and data platform as RetailNext's core products, so a bank can combine branch-level Traffic Analytics, Insights for testing branch layout changes, and Asset Protection for branch security using one shared sensor deployment. It is aimed at retail banking operations and branch-network strategy leaders managing a branch network under pressure to modernize the in-branch experience while reducing the cost of operating that network. Ayman Beydoun of BFL Group is quoted on the page: "RetailNext is a critical partner for our stores across the GCC. Their accurate measurement of in-store shopper behavior helps us deliver the best service by giving us the data we need to staff our stores accordingly and to measure performance across all of our locations. This data is critical to driving the best operational and marketing programs for our business." --- Shopping Malls https://retailnext.net/solutions/shopping-malls Shopping Malls is RetailNext's sector solution for mall and shopping-center operators and property managers. The page frames shopping-center performance as under pressure from changing consumer behavior, an evolving tenant mix, and a constant need to prove property value with data rather than anecdote. RetailNext's visitor-traffic intelligence, drawn from its Aurora® sensor platform, is positioned to help mall operators make more informed leasing decisions using actual visitor and dwell-time data rather than estimated foot traffic, prove marketing ROI by connecting mall-level marketing activity to measured visitor volume, and create new tenant-facing revenue streams by offering tenants access to traffic and visitor analytics as a service. The page also covers property-management use cases beyond leasing and marketing: measuring traffic flow across corridors and anchor-store zones specifically, and managing real-time occupancy for safety and regulatory compliance purposes. Named capabilities include making leasing decisions with quantified visitor and dwell-time data, proving the return on mall-level marketing spend, and turning the mall's own traffic data into a monetizable service offered to tenants. The solution is aimed at shopping-center property managers, leasing teams, and marketing teams who need a single, comprehensive, corridor-and-zone-level view of visitor behavior across an entire property rather than storefront-level data from individual tenants alone. Ziad AlMalki of AlMalki Group is quoted on the page: "As an expert in brand development in the luxury retail and distribution market for over 65 years, our clients' success is at the core of what we do. We are excited to introduce the RetailNext platform to augment our capabilities in helping over 100 international brands grow their market share in the Middle East." --- Business Intelligence https://retailnext.net/solutions/business-intelligence Business Intelligence is RetailNext's use-case solution for retail analytics and BI teams. The page opens with a direct contrast: most retail analytics platforms tell a team what happened, while RetailNext Insights, the product underlying this use case, is positioned to tell a team what that data means and what is likely to happen next. The solution is built for BI and analytics teams who need more than a basic traffic dashboard, combining shopper-behavior data captured through RetailNext's Aurora® sensor platform with AI-powered analysis, predictive modeling, and what RetailNext describes as the industry's largest retail benchmark dataset, built from aggregated data across its retail customer base. The stated goal is intelligence that supports strategic decisions at every organizational level, from an analyst building a custom report to an executive checking a quick metric on a mobile device, with every insight described as accessible and every decision as data-backed rather than dependent on a dedicated analyst pulling a report on request. Named capabilities on the page include asking questions of the data in plain English rather than through a fixed report format, surfacing what is coming before it becomes visible in lagging metrics like sales, and benchmarking a retailer's own performance against what RetailNext describes as the industry's largest comparable dataset. The solution is aimed at business intelligence, data, and analytics teams inside a retail organization who need predictive and benchmarked retail data rather than only descriptive, after-the-fact reporting. Damian Hall, CEO of Tanaka Megane, is quoted on the page: "RetailNext has been instrumental in helping me, as CEO, drive a performance-based culture throughout my organization." --- Marketing & CX https://retailnext.net/solutions/marketing-cx Marketing & CX is RetailNext's use-case solution for marketing and customer-experience teams. The page opens with a specific problem: marketing teams spend significant budget driving customers to physical stores, but most cannot prove that the traffic they paid for actually arrived at a store, let alone whether it converted into a sale. RetailNext positions its traffic and behavioral data, drawn from its Aurora® sensor platform, as the way to close that loop, connecting marketing spend directly to measured in-store traffic and, from there, to measured conversion. Named capabilities on the page include connecting specific marketing spend or campaigns to measured store traffic rather than relying on general lift assumptions, mapping the in-store customer journey once a shopper has arrived, and optimizing marketing budget allocation based on which campaigns and channels are shown to actually drive measured store visits rather than only digital engagement metrics. The solution shares the same underlying Aurora sensor hardware and data as RetailNext's core products, so a marketing or CX team can use the same in-store traffic data that operations and merchandising teams at the same retailer already rely on, rather than a separate marketing-only measurement system. It is aimed at marketing and customer-experience leaders who need to justify and optimize spend intended to drive physical store visits, in a category where digital-attribution tools do not extend into the physical store. Nilesh Khalkho, CEO of Sharaf DG, is quoted on the page: "We have combined the full suite of data from RetailNext with our operational data from other systems to provide actionable insights to our brand partners at every shop in shop." --- Merchandising https://retailnext.net/solutions/merchandising Merchandising is RetailNext's use-case solution for visual merchandising and customer-experience teams. The page frames merchandising decisions as having traditionally been guided by experience and instinct rather than measured evidence, and positions RetailNext as a way to replace that instinct-based approach with direct behavioral data. By mapping how shoppers actually interact with the store environment, including which displays draw attention, which zones shoppers linger in, and which layouts convert browsing into a purchase, RetailNext gives merchandising and CX teams evidence for decisions that were previously judgment calls. Named capabilities on the page include understanding which specific elements of the store environment are actually working, drawn from RetailNext's Aurora® sensor-based behavioral data; testing new merchandising concepts in a small number of stores before a wider rollout, using the Insights product; and proving the return on investment of a specific merchandising or display decision after it has been made, rather than relying only on overall sales trends that could be explained by other factors. The solution is aimed at visual merchandising, store design, and customer-experience teams who need to test and validate merchandising decisions with shopper-behavior evidence, and who currently have to rely on subjective in-store observation or after-the-fact sales results that don't isolate the effect of a specific display or layout change. Darren Bowden, VP of Store Finance at Calvin Klein, is quoted on the page: "RetailNext has helped evolve how we think about our stores at Calvin Klein and we think of them as an extension of our own team. Their robust shopper journey data gives our analytics team a much clearer view into what is happening in our stores, giving us e-commerce style analytics across store layout, marketing, product assortment, and more." --- Security https://retailnext.net/solutions/security Security is RetailNext's use-case solution for retail loss-prevention and physical security teams, built on the same Asset Protection product described on RetailNext's dedicated Asset Protection product page. The page frames most loss-prevention teams as still working reactively, reviewing camera footage after an incident has already cost the business money rather than identifying risk while it is developing. RetailNext positions its combination of AI-powered behavioral analytics and point-of-sale data integration as a way to move from that reactive model to a proactive one, identifying threats before they turn into confirmed losses and substantially cutting the time investigators spend searching through footage after the fact. Named capabilities include moving from a reactive to a proactive security posture, closing cases in hours rather than days by surfacing relevant footage and transaction exceptions automatically instead of requiring manual review, and working with a retailer's existing camera infrastructure rather than requiring a full camera-system replacement. The intended outcome, per the page, is that security teams spend less time pulling and reviewing footage and more time actually preventing incidents, while also being able to demonstrate a measurable return on their security investment to leadership. The solution is aimed at loss-prevention, risk-management, and store-operations teams responsible for reducing shrink and protecting both inventory and employees, using data from the same Aurora® sensor platform that underlies RetailNext's other products. Brent Paulsen of UNTUCKit is quoted on the page: "When looking for a solution that would help us run our stores and protect our teams and assets, it became clear that RetailNext was the best choice on the market. We are able to save ~40% versus using separate systems for traffic counting and loss prevention." --- Store Design https://retailnext.net/solutions/store-design Store Design is RetailNext's use-case solution for store planning and design teams. The page frames store-design decisions as carrying significant capital risk: a concept that looks compelling in a design brief can underperform in reality, and by the time that becomes clear across a rollout, it can cost a retailer millions of dollars. RetailNext's behavioral data, drawn from its Aurora® sensor platform, is positioned to let store planning and design teams validate a new concept, test a layout change, and measure its actual effect on shopper journeys and sales performance in a small number of stores before committing the decision across an entire fleet. Named capabilities include validating new store concepts before scaling them fleet-wide, seeing how shoppers actually navigate a store rather than how the floor plan was designed to be navigated, and proving the return on investment of a specific design decision using measured shopper behavior rather than only post-launch sales figures that could be explained by other factors. The solution is aimed at store planning, design, and real-estate teams who need evidence before committing capital to a design change across many locations, and who currently have limited ways to test a concept's real-world performance before a full rollout, using RetailNext's Insights product for exactly this kind of small-scale pilot testing. Ben Jackson of Kendra Scott is quoted on the page: "Partnering with RetailNext has allowed us to gain deep insights into our in-store customer. The data has enabled us to reshape the retail experience around how people want to shop. Listening to our customers is the top priority at Kendra Scott, and RetailNext has supplied a strong foundation for us to enhance our retail locations as we continue to grow." --- Store Operations https://retailnext.net/solutions/store-operations Store Operations is RetailNext's solution for retail operations teams managing labor, conversion, and performance across many locations at once. The core challenge the page addresses is that operations teams are responsible for reducing labor costs while simultaneously improving the customer experience, across dozens or hundreds of stores at the same time. RetailNext's traffic and behavioral data, measured at 95 to 99 percent counting accuracy through the Aurora® sensor network, is used to schedule staff to match actual measured demand rather than fixed schedules, to see performance across an entire store fleet at once instead of location by location, and to turn raw traffic counts into a measurable performance metric rather than a simple visitor tally. The page cites specific customer outcomes as evidence: Boggi Milano achieved a 40 percent increase in shopper yield, The Vitamin Shoppe generated six million dollars in additional sales over a seven-month period, and Camper saw a 10 percent increase in conversion rate. These figures are presented as results from retailers using RetailNext's platform for store operations use cases. The solution is aimed at operations leaders who need to move from location-by-location guesswork to network-wide, data-backed decisions about staffing levels, scheduling, and comparative store performance, using the same underlying sensor data that feeds RetailNext's other products. Matt Evans, Senior Director of Business Insights and Analytics at The Container Store, is quoted on the page: "Traffic counting has long been considered an ancillary metric at The Container Store due to its unreliability, however, through our partnership with RetailNext it has become a critical metric for labor planning and understanding our true customer conversion." --- Benchmarks Hub https://retailnext.net/benchmarks-hub The Benchmarks Hub is RetailNext's public retail foot traffic data resource. Every month, RetailNext tracks shopper traffic across thousands of retail locations in North America, EMEA, and APAC, producing what the company describes as one of the most comprehensive views of real-world retail performance available anywhere. The underlying data comes from RetailNext's own Aurora® sensor network, which the company states counts people with 95 to 99 percent accuracy, meaning the benchmarks are built from actual measured foot traffic rather than surveys or modeled estimates, updated monthly with data from the USA, Canada, EMEA, and APAC markets specifically. The Hub provides access to a live Benchmarks Dashboard, built on Power BI, that lets users view real-time trend data broken out by region, retail sector, and time period. Coverage spans multiple retail formats, including apparel, footwear, convenience stores, and department stores, among others. The intent of the Hub is to give retailers, analysts, and industry observers an external reference point they can use to compare their own store or portfolio performance against a broader, multi-region dataset, rather than relying solely on their own historical numbers. Because the data is refreshed monthly, the Hub also functions as a running indicator of macro-level shifts in physical retail traffic across the three regions it covers, which is useful context for anyone trying to understand how in-store shopping behavior is trending independent of any single retailer's own results. --- Pricing https://retailnext.net/pricing Pricing is RetailNext's interactive pricing-estimation page, used in place of a public price list. RetailNext uses a subscription-based pricing model covering software and Aurora® sensor hardware, and rather than publishing fixed prices, the page provides a three-step interactive estimator: a retailer enters store details, including number of locations, average entrances per store, store type or industry category, region or country, and current technology setup; the tool generates an instant estimate in local currency; and the results are also sent to the retailer's email. The subscription itself covers Aurora sensor hardware, platform software access, software updates, warranty coverage, and access to RetailNext's benchmark data. The page is explicit that the subscription price covers software and hardware only, and that additional services vary by site: installation and site surveys, professional services such as mounting, cabling, and network setup, hardware shipping, sales tax and import duties, specialized mounting hardware for unusual ceiling heights or store layouts, and any custom integrations a retailer needs. The page frames the subscription structure as a lower-operating-expense alternative to a large upfront capital purchase, describing it as preserving cash flow through budget-friendly monthly payments, and notes that retailers can contact RetailNext's sales team about discounted or enterprise-level rates, implying that pricing is negotiable at scale rather than fixed even within the estimator's output. The page is aimed at a retailer in the early evaluation stage who wants a fast, personalized cost estimate before engaging directly with RetailNext's sales team. --- Partners https://retailnext.net/partners Partners is RetailNext's page for its partner ecosystem, aimed at organizations that want to resell, integrate with, or build on top of RetailNext's platform rather than at end-customer retailers directly. The page invites technology providers, consulting firms, resellers, and deployment specialists to join the RetailNext partner ecosystem, stating that there is a partnership model built for each of those categories rather than a single one-size-fits-all program. The stated value proposition for a partner is access to new revenue streams by offering RetailNext's platform to their own customers, industry-leading support from RetailNext during that process, and the ability to go further by working together rather than as a standalone vendor competing in the same space. The page positions RetailNext's own retail-analytics platform, meaning Traffic Analytics, Insights, Asset Protection, and the underlying Aurora® sensor, as the product a partner would resell, integrate with, or deploy on behalf of their own client base, depending on the partner category. This page is distinct from RetailNext's customer-facing pages: it is written for a prospective business partner evaluating whether to build a commercial relationship with RetailNext, rather than for a retailer evaluating whether to buy and deploy RetailNext's platform directly. Because RetailNext names several distinct partner categories rather than a single generic reseller program, the page functions as an entry point for a range of organizations, from a systems integrator deploying RetailNext hardware on site to a software vendor building an integration against RetailNext's platform data, each of which would engage with RetailNext on different commercial terms. --- Who We Are https://retailnext.net/about/who-we-are Who We Are is RetailNext's company-overview page. RetailNext describes itself as the global leader in retail analytics, empowering physical retailers worldwide to make data-driven decisions, and states that it was founded in 2007 with the specific goal of bringing e-commerce-style analytics to brick-and-mortar retail. The stated company facts on this page are: more than 600 retail customers globally, in more than 100 countries, roughly 240 employees worldwide, headquarters in Campbell, California, and an average customer partnership duration of more than 10 years. The page frames RetailNext's founding premise as a specific gap it set out to close: e-commerce retailers have long had detailed, page-by-page behavioral analytics about their online customers, while physical retailers historically had comparatively little equivalent visibility into how shoppers actually behaved once inside a store, and RetailNext was built to close that visibility gap for physical retail specifically. This page functions as the canonical source for RetailNext's company-level facts, including founding year, customer count, country count, employee count, headquarters location, and average customer tenure, which are also referenced, sometimes with slightly different or older figures, on other pages across the site such as Our Advantage and individual product pages; where this page's figures differ from an older figure elsewhere, this page should be treated as the more current source since it is the dedicated company-facts page.