We help retail property investors and occupiers identify opportunities and maximise income by understanding and shaping customer behaviour across complex retail landscapes.

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INVESTORS

LoculInvestor

Strategically acquire, develop and grow retail assets.

LoculInvestor links shopping centre underwriting to the operational turnaround plan—by quantifying upside in retail sales and identifying the actions needed to close the gap.

Potential: quantify and benchmark the sales potential of an asset either greenfield or existing, owned or targeted.
Playbook: identify the retail mix opportunities and strategic necessities that will win share from competitors.
Positioning: Execute your strategy with a toolkit that helps secure the right tenants and driving customer engagement.
Performance: assess opportunity and direct investment efficiently by measuring performance against potential by suburb and customer segment.

LoculAgent

Find Tenants. Lease faster. Achieve higher rents.

LoculAgent provides retail leasing agents with data-driven insights to find the right tenants, create narratives, handle objections and justify value.

Show the “why” behind rent and mix so you control the narrative.

Benchmark each site to justify premium pricing and lift achieved rents.

Answer tough questions with facts, not guesswork, to cut time on market.

Find the right tenants based on fit, competition, demand and nearest store.

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Our Difference

Our team has over 50-years experience in location analytics and pioneered Westfield’s first customer-analytics engines. They then pushed further into mobile-signal maths and AI. That mix of frontline retail property expertise and advanced modelling now powers Prospector, uncovering customer insights other solutions miss.

Modelling the “Should”: How Loculyze Finds Hidden Retail Upside

Opportunity exists in the space between what is and what could be . If you're relying solely on performance insights then you're only getting half the story. Most analytics stops at describing what happened. How many visits did we get? Where did they come from? Which centres grew, which fell? Useful, but incomplete. It tells you what did happen , not what should have happened . If you don’t know what a centre should be doing given its catchment, competition and format, you

Created by AI, Curated by Experts: The Loculyze Way of Analysing Mobile Signal Data

Why We Use Mobile Signal Data Mobile signal data has been around for more than a decade, and it promises something retail property has always wanted: a consistent way to understand how people move, where they spend time, and how places compete. But in many solutions, the outputs don’t pass the pub test. You’ll often see impressive visualisations and very precise-sounding numbers that fall apart the moment a property professional asks the obvious follow-up: “Does this reflect

Why Retail Property Needs a Location-AI Disruptor

Stop Paying Monopoly Prices for Blunt Retail Analytics Retail property analytics hasn’t kept up with the market it’s meant to serve. If you’re an industry professional you probably know the current options: Premium banking-data products with genuinely strong signal, but heavy price tags and usage restrictions making it out -of-reach or unavailable for many. “Trade area” analysis where a consultant draws a neat shape on a map and calls it insight. Cheap data feeds and self-ser

Our Research

Does brand matter? Yes—but in retail terrain matters more.

From struggle to strategy: how shopping centres win the retail game with customer analytics

The impact of the Bondi Junction attacks on customer behaviour

Our Platform

loculAI is our living model of customer interactions with retail places. It captures, organises and transforms big and small data sets to provide a unique view of how customers interact across complex competitive landscapes.

LoculAI

Created with AI, Curated by Experts

Client Solutions: We create interactive SaaS solutions that deliver clear, concise, and prescriptive insights—vetted by industry experts and focused on helping retail property investors and occupiers identify opportunities and maximise income.

Models: We have developed four model layers to serve our client solutions:
1. People (retail spend by customer segment),
2. Places (retail destinations and tenants),
3. Observed interactions between people and places (i.e. mobile signal data) , and the
4. Expected interactions between people and places (predictive spatial interaction models).

Datasets: We feed and train our models using proprietary datasets built and enriched using third-party providers, open-source resources, and proprietary AI agents to collect and organise datasets as needed.

Collection: We consider the entire information universe as our dataset—enabled by our ability to rapidly source, construct, and integrate data from anywhere.