All posts by Retail Sensing

Retail Sensing manufacture the Video Turnstile people counting, vehicle sensing and smart city equipment. Our systems not only measure footfall and traffic, but monitor queues, display occupancy, track shoppers around stores, show heat maps of most visited areas, record passenger numbers, count pedestrians and provide retail intelligence and key performance indicators.

Bus passenger counter saves transport company thousands of pounds per bus

Our automated passenger counter system is saving a UK bus company thousands of pounds per bus.

Automated passenger counting (APC) on buses

Automatically counting people on and off their buses enabled the company to cross check passenger numbers with ticket machine transactions. With proof that they had more passengers than transactions, the company took steps to remedy the missing fare revenue.

A video counting system is 98% accurate – much more so than manual counting which is only around 85% accurate1. It is also cheaper.

With video counting you can easily verify the counts, simply by watching the video back.

People are counted on and off the bus in, for example, five minute intervals. Additionally, the system can record times at each stop so is well suited for service planning. The length of time a bus waits at a stop – the dwell time – represents a significant portion of bus operation time.

With automated passenger counts (APCs), bus companies can easily generate reports for external funding agencies like regional authorities. They can also monitor ridership trends over time.

An accurate passenger count is a significant factor in scheduling and forecasting. It is also important in analysing performance, giving measures like passengers per mile, cost per passenger and number of passengers per driver.

The system works using CCTV cameras linked to intelligent passenger counters. People are logged getting on and off a bus. The system regularly sends data back to a central database.

Our passenger counting systems are currently providing key data on many bus routes around the UK, Europe and Singapore.

To discuss passenger counting please get in touch – e-mail or call +44 (0)161 839 6437.


1Boyle, D. B. (1998). Passenger counting technologies and procedures. TCRP Synthesis of
Transit Practice 29. Washington, DC: Transportation Research Board.

More on passenger counting

Retail Dwell Time – the Route to Higher Spending

Dwell Time – the length of time a person spends looking at a display or remains a specific area. An essential retail metric for analysing shopping behaviour and increasing customer spending.

The longer a person stands looking at a display, the more likely they are to buy something. Measuring this dwell time, and acting to improve it, will increase sales.

Using Dwell Time Metrics to Increase Sales

A study by Pathintelligence showed that “there is a significant and positive relationship between dwell time and sales“. They found that a 1% increase in dwell time resulted in a 1.3% increase in sales.

What does this mean in practice? Suppose a person looked at a shelf of wine for 5 seconds. If the retailer could get them to stay there for 10 seconds, according to the study that would result in a 130% increase in sales. The retailer can test what measures increase the dwell time at the wine department and, with the dwell time linked to the Point-of-Sale (POS) system, whether this increased the sales conversion and by how much. The actual figures for their store would accurately show the improvement in dwell time and sales.

Of course, just making someone wait longer to be served or helped by a member of staff is not going to increase sales. But measuring dwell time around a store also lets you pinpoint any bottlenecks and take remedies to fix them (see our case study: Cracking the case of the missing grocery sales).

As well as evaluating and improving the effectiveness of displays, knowing where customers go and stay within a store enables managers to optimise store layout for increased sales.

Acting on dwell time lets retailers

  1. Learn how customers move through the store and interact with displays
  2. Learn where to place their most profitable items
  3. Test and learn which display layouts and signage work best to encourage people to buy more items
  4. Reduce unprofitable waiting times around a store
  5. Optimise store layout

Measuring dwell time is the start of the path to enhanced customer experience, optimised store performance and higher profitability.

How to measure Dwell Time?

A video sensing system tracks the path people take through a store and their dwell times at different locations. Intelligent people-sensing units connect with overhead cameras to monitor and time customers’ progress. The customer isn’t identified so privacy is protected. Data is logged by the units and regularly sent back to a central computer, over wi-fi or Ethernet for example. The system also measures footfall and integrates with POS information to produce real-time sales conversion figures.

The dwell time and path data is often displayed as a “heat map” – where different colours are overlayed on a map of a store or department to show the most popular areas and routes.

Heat map of dwell times in a store
Heat map of where people linger in a children’s clothing store, showing where improvements might be made

The system can cope with changing light levels, cluttered backgrounds and crowded scenes. It is infinitely scaleable – local video analytics minimises bandwidth use. The return on investment can be as little as a few months.

Dwell Time is not Just for Stores

Many different types of business benefit from measuring dwell time. Museums and art galleries use dwell time to measure the popularity of the exhibits. Shopping centres can act on dwell times to optimise their layouts. The system enables many different types of organisations to analyse visitor behaviour.


Time is Money, Shoppers buy more when they stay longer, David McAdams and Sharon Biggar, 2007.
The Slower You Shop, the More You Spend, The Wall Street Journal, 2015.

Cracking the case of the missing grocery sales – changing a weakness into a strength

One store in a chain of supermarkets was under-performing. The point-of-sale (POS) figures showed that people using this supermarket bought less items than shoppers elsewhere. Why was this? And what could be done to bring the store in-line with the others?

The Investigation – Data Collection

To find out what was going on the store chain directors installed a Smart Retail Sensing System and started amassing retail intelligence.

They knew the store was busy – the POS system told them how many shoppers there were. But they needed more than just people counts. The Retail Sensing system told them, amongst other things, the Average Shopping Time for the entire store and different departments within the store. It also showed where queues were forming and for how long, plus the path people took through the store.

The Analysis

When Retail Sensing analysed the data the “problem” was clear: the supermarket butcher was fantastic.

People were going straight to the fresh meat counter. As the butcher was so good they were prepared to queue for his advice and attention, spending an average of 10 minutes doing so.

The queuing didn’t stop there. Customers also waited ten minutes at the checkout. The average shopping time for the whole store was around 30 minutes which meant that customers gave the same time to buying fresh meat as they did to all their other shopping combined.

When compared with other stores in the goup this showed in the basket makeup. The average meat share at this supermarket was 18%, in other stores it was just 12%. If the store could get the ratio to a similar level, without sacrificing meat sales, profits would rise.

The Solution: Acting on the Data

How to do this? On the advice of the Smart Retail Sensing consultants they experimented with two complimentary measures. Firstly the store set up a ticket-based system for the meat counter. As soon as people entered the store, signs invited them to take a numbered ticket to see the butcher. When their number was called the butcher was ready for them. This meant that they could do other shopping whilst waiting for their turn.

The second change was in the layout of the shop. Moving different departments – fruit and vegetables, dairy, cleaning products and so on – so customers had to walk down these aisles to make their way to the butcher. Different arrangements were tried on different weeks and data analysis revealed the optimum layout for increased sales.

The Result

Changing the path people took through the store and reducing their queuing time is resulting in many more items being bought on each visit. The people still come in to buy their meat, but now also have time to buy their other groceries as well.

The key to solving the case of the missing grocery sales was monitoring the queuing times, the shopping path, how long people spent in key areas and, above all, the Average Shopping Time.

Can we Help?

If you would like to know more about this story, or learn if the Smart Retail Sensing Service can help your business, call Ran on +44(0)161 394 0827.

Reseller in Hong Kong for Smart City Systems Sought

We are currently inviting technology companies in Hong Kong to investigate becoming our sole resellers in the area.

To find out more call David Collins on +44 (0)161 839 6437, email or fill in the form below.

Briefly, our systems:

  • Count people, providing both real-time and historical data for big data analytics with accurate footfall figures by location and time.
  • Count bicycles to helps monitor and support their use.
  • Count vehicles to identify and reduce traffic congestion.
  • Count passengers on buses, trains, trams etc. They let city managers know the numbers of people arriving by public transport at various points in the city, by time of day.
  • Identify and reduce traffic congestion
  • Quantify the use of footpaths and cycle ways
  • Let retail units discover vital analytics like sales conversion rate, average queuing time, average shopping time, most popular area of store and footfall past the store
  • Assess the impact of development initiatives and use the data to inform future decisions

About Retail Sensing and the IoT Smart City Sensors

CCTV camera built into IoT smart city system for counting pedestrians, bicycles and vehicles.

Retail Sensing are a UK-based technology manufacturing company. Our head office is in Manchester.

Our Smart City devices use our own video analytics algorithm to detect pedestrians, bicycles and vehicles. They include data logging hardware and Internet-of-Things connectivity.

The ISO9001 quality approved system is implemented around the city and sends real time data to “Brokers”. The brokers process the data and make it available to all end users.

The Smart Counters are attached to lampposts around the city. They can also be used inside public buildings and shopping centres

We’re currently involved in smart city projects around the world, either directly or with our resellers.

Find out more about becoming a Reseller

To find out more about becoming a reseller of our smart city systems, call David Collins on +44 (0)161 839 6437, email or fill in the form below.

Counting mobile library users in the Arctic Circle

In the far north of Finland, within the Arctic Circle, travels the mobile library of the Inari region. This library bus brings books and magazines to the small villages of the largest municipality in the country.

The library wanted to record how many people got on and off the bus at each stop. To achieve this accurately and cost-effectively they partnered with Retail Sensing.

Children on the mobile library bus
Children on the mobile library bus

The bus runs Monday to Friday, following a different route each day. In the middle of the bus is one door which the library patrons use to board and alight. Technicians installed a CCTV camera above the door and connected it to one of our people loggers. Our software analyses the images from the camera, detecting whenever a person enters or exits. The logger saves the counts of people using the library at each village – recording the time, date, number of library visitors and location.

The data collected is providing a measure of the library’s value – proving that the library bus is well-used and worthwhile.

Read more about automated passenger counting or contact Retail Sensing for more information on counting in a mobile library bus.

Google local search drives consumers to high street stores

Research from Google investigates the impact of internet searches on high street shopping. According to their report, the retail industry is undergoing a dramatic shift: footfall is down whilst online research is up. But search results are also a powerful way to drive consumers to stores.

Google local search drives high street footfall

Three out of four shoppers who find local information in search results helpful are more likely to visit stores. Shoppers are actually inspired to visit after successfully finding out information such as the in-store availability of an item, store location, hours and pricing at a nearby shop.

Local search drives footfall

The report says that digital is a powerful way to connect consumers with stores and increase footfall (or “in-store foot traffic” as they put it).

Another statistic unearthed by the researchers is that many consumers now spend more than 15 hours per week researching on their smartphones.

This change in consumer behavior is creating dramatic new realities in the world of local retail. It’s not only changing the mind-set of consumers as they walk into the store, but it’s also changing actual footfall patterns. Stores are seeing less visitors, but the people who enter are buying much more. Consumers visited less, but they were better informed about what they wanted when entering the store. Each trip was more purposeful and the stores’ sales conversions are increasing.


Sales conversion is simply the number of people who make a purchase divided by the number of people who enter the store. If 100 people visit a store, and 5 of them buy something, the conversion rate is 5%.

Obviously the first step in calculating sales conversion is to count how many people visit the store in a given time. Or, to put it another way, how many opportunities for a sale are there? Integrating people-counts with the point-of-sale (POS) system produces sales conversion figures in real-time.

Stores can further increase sales conversion rates by

  1. Using people count patterns to accurately schedule more staff at busy periods and generate more sales.
  2. Tracking the path consumers take through the store: if someone cannot find something they cannot buy it. Seeing where people go shows whether a store needs re-organising in order to increase conversion rate.
  3. Seeing the length of time people pause at displays and kiosks. If the display doesn’t catch their interest: change it.
  4. Monitoring queues: long waiting times discourage people from buying
  5. Finding how many people leave without buying anything and why: long queueing time, no staff available to convert the sale, poor store layout, etc.

A video people counting system gives high street retailers the tools to maximise profits.

Further Reading:
Video People Counting, Retail Sensing
The 3 New Realities of LOCAL RETAIL by Sameer Samat, October 2014
Understanding Consumers’ Local Search Behavior, Google