A1 PEOPLE COUNTING

The Importance Of Accuracy

The importance of having accurate people counts, or traffic counts, is well understood in any industry that relies on consumer impressions to attract and provide value to customers. In the retail environment, merchants want to be where customers go, advertisers want to advertise in the busiest locations, and sponsors want to interact with as many people as possible.

Intelligent, accurate, people counting systems, such as the ones that A1 provides, allow landlords to confidently share shopper habits with their partners. Data such as when customers arrive, what entrance they use, what stores they visit and how long they stay provide the information that the data users need to make decisions about where to place a store, how many people will see an ad, and when to host an event.

People counting data can also be used to help building operators plan and measure their operating efficiency to ensure optimization in spending for staffing levels, cleaning schedules, utilities, and supply usage.

Looking further ahead, seasonal trends in traffic variations and occupancy help to identify when scheduling needs to be adjusted, and extra staff need to be on-boarded to meet increased capacity requirements. Likewise, taking advantage of embedded weather and event tracking, short term adjustments based on local weather forecasts and planned events can be scheduled as little as a week or a few days in advance. This reduces the need for same-day, last-minute adjustments as much as possible.

OCCUPANCY-FLOOR-HOUR MODEL

Oxford Properties Group

Robert Horst, VP Real Estate Management with Oxford Properties Group explains how his team at Scarborough Town Centre used their traffic counts to build an occupancy-floor-hour model to optimize staffing levels to ensure their expectations for mall cleanliness and overall guest experience are met at all times of day.

Very easily one can determine the average ratio of mall customers to mall employees on the floor each hour, and ensure that the ratio remains balanced throughout the day by looking for shortfalls or overbooking of floor hours. With a simple visual check of this ratio, we were able to identify that a peak traffic hour in the mall coincided with the same hour that our employees were changing shifts; meaning less employees were available to service the Centre at it’s busiest time of the day. Adjusting our shift change time resolved this issue, and ensured our team was at full capacity when it needed to be to maintain our guest experience expectations.Robert Horst, VP Real Estate Management, Oxford Properties Group

OCCUPANCY-FLOOR-HOUR MODEL

Oxford Properties Group

Robert Horst, VP Real Estate Management with Oxford Properties Group explains how his team at Scarborough Town Centre used their traffic counts to build an occupancy-floor-hour model to optimize staffing levels to ensure their expectations for mall cleanliness and overall guest experience are met at all times of day.

Very easily one can determine the average ratio of mall customers to mall employees on the floor each hour, and ensure that the ratio remains balanced throughout the day by looking for shortfalls or overbooking of floor hours. With a simple visual check of this ratio, we were able to identify that a peak traffic hour in the mall coincided with the same hour that our employees were changing shifts; meaning less employees were available to service the Centre at it’s busiest time of the day. Adjusting our shift change time resolved this issue, and ensured our team was at full capacity when it needed to be to maintain our guest experience expectations.Robert Horst, VP Real Estate Management, Oxford Properties Group

MORE EFFICIENT PLANNING

Sophisticated KPI Model

A more sophisticated KPI model can be developed by using the floor hour benchmark referenced above.

  1. Identify a list of KPI’s against which a score will be made, such as washroom cleanliness, waste bin levels, and food court tray clean up
  2. Cross-reference the floor hour benchmark to hourly scores on the rubric created in step 1; the number of floor hours scheduled at the point at which the KPI scores start to decline should be used as the trigger point to make adjustments
  3. Analyze historical traffic trends to understand how each day of the week differs on a “normal” week, and to identify how anomalies, such as weather, special events, and holidays affect that pattern; resulting in either higher or lower occupancy and required floor hours to serve them
  4. Allocate the minimum number of floor hours to each operating hour based on forecasted occupancy, and develop an employee schedule that ensures the minimum floor hour number is met at all times.

This comparison helps to identify opportunities for adjusted shift lengths, staggered start times, and optimal overlap shifts, ultimately “right-sizing” floor hours and reducing overhead costs through efficient staff planning.

By identifying that one-day is overstaffed, while another day is understaffed, a site is able to redistribute staffing and reduce man-hours. A savings of 2 hours per day overall in floor-hours could result in savings of almost $20,000 per year assuming an average cost of $19 per man-hour, without impacting the experience provided to customers in the site. This same process applied to security, guest services, valet parking and more can lead to significant operator cost savings, and represent just one piece of the value to be found.

WHITEPAPER
People Counting From A-Z

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