WHY RETAILNEXT

Solution Comparison

Choosing how to approach retail analytics is one of the most consequential technology decisions a retailer makes. The right platform will underpin your operational decisions for years. The wrong one will cost more — in budget, in internal resource, and in missed opportunity — than you expect.

This page is designed to help you make that decision clearly. We've laid out the four approaches retailers consider, what each one actually costs and delivers, and where RetailNext fits relative to the alternatives.

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Four Ways to Approach RETAIL ANALYTICS.

Most retailers evaluate one or more of these approaches. Each has genuine trade-offs across cost, capability, time to value, and long-term risk.

Integrated Platform

One vendor. One dataset. Complete intelligence across traffic, behavioral analytics, and asset protection. Faster time to value, lower total cost of ownership, and a single support relationship.

Point Solutions

Separate vendors for traffic counting, heat mapping, loss prevention, and analytics. More control over individual components — but significantly more complexity, cost, and data reconciliation.

Build In-House

Custom analytics developed by your engineering team using commodity hardware. Maximum control and IP ownership — but 12-24 months to basic functionality, ongoing engineering cost, and no benchmark data.

Status Quo

POS data, manual counts, and assumptions. No investment — but the hidden cost of poor decisions, missed optimization, and competitive disadvantage compounds over time.

Not All Analytics ARE EQUAL.

Accuracy, completeness, time to value, and total cost of ownership vary dramatically across these approaches. Here's the honest comparison.

RetailNext vs COMPETITIVE PLATFORMS.

When evaluating integrated retail analytics platforms, these are the dimensions that matter most. RetailNext's advantages are grounded in purpose-built hardware, a unified platform, and 19+ years of retail-specific innovation.
Capability RetailNext Typical Competitors
People counting accuracy 95-99% — AI-powered Aurora® sensor, manually audited 75-85% — repurposed security cameras or generic IoT
Staff exclusion checkmark Advanced AI-powered, customer-only metrics cross Manual or basic only
Behavioral analytics (heat mapping, path analysis) checkmark Full Insights product suite partial Basic or separate vendor
Asset Protection integration checkmark Unified platform — no additional vendor cross Separate vendor required
Predictive analytics & AI checkmark Pulse AI chatbot + AI-powered staffing predictions cross Not available or limited
Benchmarking dataset checkmark Industry's largest — billions of interactions annually partial Limited or unavailable
Enterprise scalability checkmark 600+ retailers, 100+ countries, proven at global scale partial Variable — limited large-scale deployment track record
IT deployment complexity checkmark Cloud-native, one outbound port, 4-6 week deployment partial Variable — often more complex
SOC2 Type II compliance checkmark Compliant partial Variable
Average customer tenure 10+ years Unknown / variable
Capability People counting accuracy
RetailNext 95-99% — AI-powered Aurora® sensor, manually audited
Typical Competitors 75-85% — repurposed security cameras or generic IoT
Capability Staff exclusion
RetailNext checkmark Advanced AI-powered, customer-only metrics
Typical Competitors cross Manual or basic only
Capability Behavioral analytics (heat mapping, path analysis)
RetailNext checkmark Full Insights product suite
Typical Competitors partial Basic or separate vendor
Capability Asset Protection integration
RetailNext checkmark Unified platform — no additional vendor
Typical Competitors cross Separate vendor required
Capability Predictive analytics & AI
RetailNext checkmark Pulse AI chatbot + AI-powered staffing predictions
Typical Competitors cross Not available or limited
Capability Benchmarking dataset
RetailNext checkmark Industry's largest — billions of interactions annually
Typical Competitors partial Limited or unavailable
Capability Enterprise scalability
RetailNext checkmark 600+ retailers, 100+ countries, proven at global scale
Typical Competitors partial Variable — limited large-scale deployment track record
Capability IT deployment complexity
RetailNext checkmark Cloud-native, one outbound port, 4-6 week deployment
Typical Competitors partial Variable — often more complex
Capability SOC2 Type II compliance
RetailNext checkmark Compliant
Typical Competitors partial Variable
Capability Average customer tenure
RetailNext 10+ years
Typical Competitors Unknown / variable

One Platform vs MULTIPLE POINT SOLUTIONS.

Many retailers start by assembling point solutions — a traffic counter from one vendor, heat mapping from another, loss prevention from a third. The logic seems sound: best-of-breed for each function. The reality is more complicated.

The Point Solution Scenario

  • Vendor A for traffic counting
  • Vendor B for heat mapping and behavioral analytics
  • Vendor C for loss prevention and video
  • Internal or third-party BI tool to aggregate it all
  • 3-5 vendor contracts, support relationships, and renewal negotiations
  • Data that never fully reconciles across sources
  • Internal resources consumed managing vendor complexity

The RetailNext Reality

  • Traffic Analytics, Insights, and Asset Protection in one platform
  • One unified dataset — no reconciliation required
  • One vendor relationship, one contract, one support team
  • API included for BI tool integration at no extra cost
  • Combined total cost of point solutions typically exceeds the platform price
  • Single deployment, single onboarding, unified dashboards

"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."


— Brent Paulsen, Managing Director and Head of Retail

UNTUCKit logo

Buy Proven OR BUILD FROM SCRATCH.

For some retailers, building in-house feels like the right strategic move — control, IP ownership, and a custom fit to your specific data architecture. Here's what that actually looks like in practice for most retail organisations.
Consideration RetailNext Build In-House
Time to basic functionality 4-6 weeks 12-24+ months
People counting accuracy checkmark 95-99% from day one cross Months or years to achieve comparable accuracy
Ongoing engineering cost checkmark Included in platform fee cross 2-4 FTE engineers minimum, ongoing
AI / ML improvements checkmark Automatic platform updates cross Requires dedicated ML capability
Benchmark data checkmark Access to industry's largest dataset cross No access — must build own
Risk checkmark Proven technology, established deployment cross Unproven development, high failure rate
Opportunity cost checkmark Engineering team focused on core business cross Engineering resources diverted for years
Vendor stability checkmark 19+ years, 600+ customers cross Internal team turnover risk
Consideration Time to basic functionality
RetailNext 4-6 weeks
Build In-House 12-24+ months
Consideration People counting accuracy
RetailNext checkmark 95-99% from day one
Build In-House cross Months or years to achieve comparable accuracy
Consideration Ongoing engineering cost
RetailNext checkmark Included in platform fee
Build In-House cross 2-4 FTE engineers minimum, ongoing
Consideration AI / ML improvements
RetailNext checkmark Automatic platform updates
Build In-House cross Requires dedicated ML capability
Consideration Benchmark data
RetailNext checkmark Access to industry's largest dataset
Build In-House cross No access — must build own
Consideration Risk
RetailNext checkmark Proven technology, established deployment
Build In-House cross Unproven development, high failure rate
Consideration Opportunity cost
RetailNext checkmark Engineering team focused on core business
Build In-House cross Engineering resources diverted for years
Consideration Vendor stability
RetailNext checkmark 19+ years, 600+ customers
Build In-House cross Internal team turnover risk

The Hidden Cost OF STANDING STILL.

The status quo always feels like the low-risk option. No budget, no implementation, no disruption. But for multi-location retailers, the cost of making decisions on incomplete data compounds across every store, every week.

Labor Misalignment

Scheduling to assumption rather than actual and predicted traffic typically costs 5-10% in unnecessary labor spend or missed service levels. Across a 100-store network, that's a significant and recurring cost.

Missed Conversion

Without accurate traffic data, you can't calculate a true conversion rate — which means you can't identify which stores are underperforming or why. Industry data shows 15-20% conversion improvement is achievable with data-driven optimization.

Unvalidated Merchandising

Layout and fixture decisions made without behavioral data carry high risk. A failed fleet-wide rollout can cost millions. Test-and-learn methodology reduces that risk to near zero — but only if you have the data to run it.

Uncontrolled Shrink

Without integrated asset protection and POS exception reporting, shrink is difficult to detect proactively and even harder to investigate efficiently. Integrated platforms reduce investigation time by up to 75%.

The Case for RETAILNEXT.

The numbers that matter most when choosing a retail analytics partner.

95-99

People counting accuracy

Manually audited at every installation

4-6

Average deployment timeline

vs 12-24 months to build in-house

10

Year average customer partnership

RetailNext customer base

600

Retailers globally

100+ countries

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Ready to Make A CONFIDENT DECISION?

Talk to our team. We'll walk you through the comparison in detail and help you build the business case for your specific situation.

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Common QUESTIONS.

About the comparison

About choosing RetailNext