For a limited time we are offering 25% off our footfall monitoring maintenance service! This comprises
For more details, or to take advantage of this offer, contact sales@retailsensing.com.
Do your shop window displays attract customers? Are you sure? Video recognition technology tells you exactly how many people looked at the shop window, for how long and what percentage of them actually entered the shop. You can see the path people take round the store and when browsers turn into customers.
Cameras inside and outside the shop, together with retail sensing units, provide essential business intelligence.
Video systems give over 98% accurate measurements of customer engagement and response, so retailers can improve displays and test exactly what converts browsers into buyers. Importantly, managers are also very quickly informed when shop window displays are failing to attract customers.
Video recognition technology does much more than merely counting people. A video sensing system:
If you would like more details on testing shop window displays and converting browsers into buyers, e-mail sales@retailsensing.com or call +44 (0)161 839 6437.
Installed across a busy retail chain, the Retail Sensing Occupancy Counter now tweets alerts when too many people are in any of the stores.
We introduced the occupancy tweeting feature at the request of the customer. We are happy now though, to offer the tweet option to all our customers.
The system also features reporting via a web-based dashboard for each store. According to the privileges of the person viewing the report, they can see information for just their store, for their area or for the entire chain.
The on-line occupancy meter shows at a glace how busy each store is.
Along with occupancy information, the on-line dashboard shows real-time retail performance metrics like queuing times, sales conversion and average shopping time.
As we design and manufacture the people counting equipment here in Manchester, we are always happy to modify our systems according to our clients’ specifications.
Our automated passenger counter system is saving a UK bus company thousands of pounds per bus.
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 sales@retailsensing.com. or call +44 (0)161 839 6437.
References
1Boyle, D. B. (1998). Passenger counting technologies and procedures. TCRP Synthesis of
Transit Practice 29. Washington, DC: Transportation Research Board.
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.
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
Measuring dwell time is the start of the path to enhanced customer experience, optimised store performance and higher profitability.
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 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.
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. http://online.fliphtml5.com/olpx/fgfr/
The Slower You Shop, the More You Spend, The Wall Street Journal, 2015.https://www.wsj.com/
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?
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.
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.
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.
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.
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.
