For decades, brick-and-mortar retail has operated on a painful information gap.
You spend $50,000 on a new end-cap display. You invest a significant portion of your marketing budget into a new in-store digital promotion. You A/B test two different window displays at two different locations.
And then… you wait.
You look at sales data a month later. Maybe sales went up. But was it the display? Or was it the weather, a competitor’s stockout, or the email campaign you sent last week?
This is the “black box” of physical retail. While your e-commerce counterparts track every click, scroll, and hover, you’re left to correlate lagging indicators (sales) with your expensive in-store efforts. You’re guessing.
At Retailr AI, we believe it’s time to stop guessing and start knowing. We provide the tools to bring e-commerce-level analytics into your physical space, allowing you to measure the true ROI of every single in-store action.
🤖 From Footsteps to First-Party Data
The foundation of “knowing” is data. But unlike shady online trackers, we capture this data anonymously and ethically to give you a clear, privacy-compliant picture of store performance.
Our AI-powered sensors use advanced computer vision—this is not facial recognition and no PII is ever stored—to translate real-world shopper behavior into actionable metrics on your dashboard.
Here’s the data we capture that e-commerce has always had, and you’ve always missed:
- Foot Traffic & Pathing: We generate real-time heatmaps. Where do shoppers actually go? Where do they stop? Which pathways are “dead zones”?
- Presence & Dwell Time: How many people stopped at your new promotional display? Did they just glance at it, or did they spend 30 seconds engaging with it?
- Content & Gaze Engagement: For digital screens, our sensors detect attention. When your ad plays, are shoppers looking at it? Or are they looking at their phones? We measure the “attention rate” of your content.
- Anonymous Demographics: Our AI can anonymously identify attributes like group size (e.g., individual, family), estimated age range, and gender, helping you confirm if your promotions are attracting your target audience.
📈 From Data to Decisions: How to Actually Prove ROI
This new layer of data transforms how you value your store. You can now move from “I think it worked” to “I know it worked, and here’s by how much.”
Case Study 1: Proving the Effectiveness of a New Display
- The “Guessing” Method: “We launched the new cosmetic display. Sales for that category went up 8% in that store. I guess the display is working.”
The “Knowing” Method (with Retailr AI): “The new cosmetic display increased foot traffic to that aisle by 22%. It had a 35% higher dwell time than the old display, with shoppers engaging for an average of 45 seconds. Our data shows this engagement—specifically with the interactive ‘try-on’ screen—led directly to the 8% sales lift. We are rolling it out to 50 more stores.”
Case Study 2: A/B Testing Your Digital Promotions
- The “Guessing” Method: “Let’s run Video A for two weeks and Video B for two weeks and see which one ‘feels’ better. The store manager liked Video A more.”
The “Knowing” Method (with Retailr AI): “We ran Video A and Video B on alternating days. Our dashboard shows that Video A captured attention from 40% of passersby, but the average gaze time was only 3 seconds. Video B only captured 25% of attention, but its average gaze time was 18 seconds. Insight: Video B is far more compelling. We will iterate on its creative and drop Video A, saving thousands in wasted ad spend.”
Case Study 3: Optimizing Store Layout and Staffing
- The “Guessing” Method: “It feels like we’re always understaffed at lunch. And nobody ever goes to the back-left corner.”
The “Knowing” Method (with Retailr AI): “Our traffic reports show a 40% surge in shoppers from 12:00 PM to 1:30 PM, but our associate schedules show a 30% staff reduction for breaks at that same time. By staggering lunches, we can increase coverage. Furthermore, our heatmaps confirm the back-left corner has 80% less traffic. We will move our high-margin ‘clearance’ section there to draw traffic and improve product discovery.”
💰 Pricing & Investment: What Does “Knowing” Cost?
Implementing an in-store analytics platform is a high-ROI strategic investment. Pricing is customized based on your store’s size, the number of data points you want to track, and the level of integration.
Here’s a transparent breakdown of how to budget for a Retailr AI solution:
1. Pricing Models
- One-Time Setup: This is the cost for the physical hardware, including our AI-powered optical sensors and the media players that connect them to the cloud.
- Monthly SaaS License (per device): This is a recurring software fee that gives you 24/7 access to your analytics dashboard, all the AI processing, software updates, and our full support.
2. Estimated Investment
- Initial Setup: Budget approximately $1,000 - $3,000 per sensor/data point. This cost varies based on the sensor type (e.g., a simple traffic counter vs. an advanced gaze-tracking sensor).
- Ongoing SaaS Fee: Plan for $50 - $200 per sensor, per month. This fee covers all data processing, dashboard access, and support, giving you a predictable operational expense.
3. Key Indicators: Is This Solution Right for You?
While all retailers benefit from data, our AI analytics solutions provide the fastest and highest ROI for:
- Multi-Location Retailers: Brands with 10+ stores who need to compare performance, A/B test, and scale winning strategies.
- High-Value Locations: Flagship stores, airport retail, and high-street locations where every square foot of real estate must be profitable.
- Marketing-Heavy Brands: If you invest heavily in merchandising, POP displays, and in-store promotions, you need this data to justify your budget.
- Operations-Focused Teams: Operations managers who are responsible for optimizing store layout, staffing levels, and preventing "dead zones."
Your Store is Talking. Are You Listening?
For the first time, you can get definitive, actionable answers to your most expensive questions.
- Is my new display working?
- Is my digital content engaging?
- Where is the single most valuable spot in my store?
Stop guessing. Start knowing.