How we measure real-world consumer behaviour 

This blog is about how Accurat measures real-world consumer behaviour beyond surveys, internal sales data and declared intentions. It explains how movement data helps reveal where consumers go, how often they return and how markets evolve over time. By looking at actual behaviour, businesses gain a stronger basis for decisions in retail, media and location strategy.

Retail performance is often evaluated through internal sales data, loyalty data, or surveys. Each of these sources explains part of the picture, but none of them fully show how consumers behave across the wider market. Internal data explains what happens inside one business. Surveys add context, but they depend on declared answers and often arrive with a delay.

Real-world consumer behavior requires another perspective: actual movement.

At Accurat, we start by analyzing where people go, how often they return, how they combine brands, and how these patterns evolve over time. By turning anonymized location signals into structured insights, powered by AI, market dynamics become measurable on a daily basis. 

 

From movement to behavioural insight

Every day, we collect millions of anonymized location signals from consenting users through partner apps. These signals are cleaned, validated, and matched with points of interest with the help of AI. These POIs consist of supermarkets, retail parks, city centers, and competing store locations. 

This makes it possible to identify the following:

• where visits take place
• how often consumers return
• how long visits last
• how far visitors travel
• which competing locations are combined

What starts as raw movement becomes behavioral structure through market research. This matters because observed behavior often differs from declared behavior. Consumers may describe themselves as loyal, while their actual visit patterns reveal frequent switching between brands

 

 

Measuring loyalty beyond transactions

Loyalty is often defined as purchase repetition. In reality, loyalty depends on context. A consumer may visit the same supermarket weekly but still combine that store with several competitors depending on convenience, promotions, or location.

This is why loyalty measurement at Accurat focuses on multiple dimensions:

• visit frequency

• unique visitors

• overlap with competitors

This creates a more realistic understanding of customer loyalty than transaction data alone. Because loyalty is always relative: it's not only how often consumers return but also where else they go.

 

Behaviour also explains audience differences

Two locations can attract the same number of visitors but perform very differently because the audience behind those visits is different. That is why behavioral analysis also looks at who visits.

We connect visit patterns with recurring movement profiles; segments become visible:

• families

• commuters

• loyal local visitors

• cross-shopping consumers

This reveals which audiences drive performance and which segments are under pressure. For retailers, such analysis often explains why similar stores evolve differently under identical pricing conditions. 

Why daily updates matter 

Consumer behavior changes continuously. Fuel prices, promotions, weather, local events, and competitor actions all influence movement patterns faster than many reporting systems can capture. That is why we work with continuously refreshed  behavioral data rather than delayed snapshots. 

This allows businesses to detect early signals:

• market share pressure

• unusual switching

• campaign response 

 

How we measure campaign impact in the real world

Linking media exposure to actual store visits makes campaign impact measurable. Where traditional reporting mainly shows reach, behavioral data shows whether exposure leads to action.

By comparing exposed and non-exposed audiences, we make it possible to measure the following: 

• uplift in visits during and after campaign periods

• changes in visit frequency

• local differences in campaign response

• impact compared to control regions

This is particularly valuable for offline channels such as out-of-home and folder distribution, where direct attribution has traditionally been limited. The result is a clearer view of what media contributes to actual behavior: not only who could have seen a campaign but also who visited afterwards. The central question is straightforward: did exposure generate additional visits?

 

 

 

Measuring what consumers actually do

The biggest difference between traditional research and behavioral intelligence is simple: One measures answers; the other measures actions.

By analyzing anonymized movement at scale, businesses gain access to how consumers actually behave across markets, brands, and environments. That creates a stronger basis for decisions in retail, media, and location strategy. Because consumer behavior is not static, it is visible in motion.