Knowledge Base
Footfall & passers-by

Retail analytics

Retail analytics is the practice of collecting, measuring, and analysing data from retail operations to understand performance, customer behaviour, and market trends — enabling better decision-making across merchandising, marketing, staffing, and store design.

What is retail analytics?

Retail analytics encompasses all the methods, technologies, and processes used to turn raw retail data into actionable insights. It spans the entire retail operation: from understanding who walks into the store and what they do, to evaluating supply chain efficiency and forecasting demand.

At its core, retail analytics answers questions like:

The evolution of retail analytics

Phase 1: Transaction data (1990s–2000s)

The first era of retail analytics focused almost exclusively on point-of-sale (POS) data. Retailers could see what sold, when, and at what price — but had no visibility into the behaviour that led to (or didn’t lead to) a purchase.

Phase 2: Online analytics (2000s–2010s)

E-commerce introduced sophisticated analytics capabilities. Web platforms could track every interaction — page views, click paths, cart abandonment, A/B test results — giving online retailers an enormous competitive advantage in understanding their customers.

Phase 3: In-store analytics (2010s–present)

Physical retailers began adopting technologies to close the “data gap” with online. Wi-Fi tracking, Bluetooth beacons, footfall sensors, and eventually computer vision brought web-analytics-style insight to physical spaces — measuring the customer journey from entrance to checkout.

Phase 4: AI-powered analytics (2020s–present)

The current frontier combines computer vision, deep learning, and natural language interfaces to deliver real-time, actionable insights. AI can automatically detect patterns, generate recommendations, and allow users to query data conversationally (e.g., “What was the busiest hour last Saturday?”).

Key areas of retail analytics

Traffic and footfall analytics

Measuring visitor volume is the foundation. Without knowing how many people entered, no other metric (conversion, dwell, engagement) can be accurately calculated.

Customer journey analytics

Understanding how shoppers navigate the store — which zones they visit, in what order, and for how long — reveals whether the store layout is working or whether key departments are being missed.

Conversion analytics

Conversion rate — the percentage of visitors who make a purchase — is arguably the most important metric in retail. A store with declining sales may not have a sales problem; it may have a traffic problem, or a conversion problem. Analytics separates the two.

Demographic analytics

Understanding the age and gender profile of your customer base helps inform merchandising, marketing, and even music and scent choices. Modern analytics does this anonymously using computer vision — no loyalty cards or surveys required.

Operational analytics

Staffing levels, queue wait times, checkout utilisation, and service desk performance all fall under operational analytics. The goal is to align resources with actual customer demand.

Campaign and merchandising analytics

Measuring the impact of promotions, window displays, fixture moves, and product placements. This is the in-store equivalent of A/B testing and is essential for proving marketing ROI.

In-store analytics: the data gap

The fundamental challenge for physical retailers is that they’ve historically had far less data than their online competitors. An e-commerce site knows:

A physical store, by contrast, traditionally only knew what happened at the till. Everything before that — the browsing, the consideration, the comparison — was invisible.

Modern in-store analytics, powered by computer vision, closes this gap by providing the physical-world equivalents of web metrics: visits (footfall), page views (zone visits), time on site (dwell time), and conversion (purchases ÷ visitors).

How Aura Vision enables retail analytics

Aura Vision provides the in-store data layer that retailers need to match the depth of their online analytics. By connecting to existing CCTV cameras, the platform delivers footfall counts, customer journey maps, heatmaps, demographic insights, queue analytics, and staff scheduling data — all from a single integration.

The Ask Aura AI assistant allows retail teams to query their data in natural language, making insights accessible to everyone from store managers to board members without requiring data science expertise.

Frequently asked questions

What is the difference between retail analytics and business intelligence?

Business intelligence (BI) is a broad term covering data analysis across any industry. Retail analytics is the discipline-specific application of BI to retail, using metrics like footfall, conversion rate, basket size, and dwell time that are unique to the sector.

What types of data are used in retail analytics?

Retail analytics draws on point-of-sale (POS) transaction data, footfall and traffic counts, customer journey data (path maps, heatmaps), demographic profiles, inventory levels, staffing schedules, and external data like weather and local events.

Can small retailers benefit from analytics?

Absolutely. While enterprise retailers may deploy analytics across hundreds of stores, even a single-location retailer benefits from understanding peak hours, conversion rates, and which areas of the store perform best. Modern cloud-based analytics platforms have made these tools accessible at any scale.

What is the difference between online and in-store analytics?

Online analytics tracks digital interactions (clicks, page views, cart additions). In-store analytics tracks physical behaviour (footfall, movement paths, dwell time, queue waits). The challenge for physical retail has been achieving the same depth of insight as e-commerce — which is now possible with computer-vision-based analytics.