1. Introduction to Video Analytics Benchmarking
Kyle Grottini – Research Scientist II - LPRC

2. Benchmarking
Trying to paint a picture of what an “average” day looks like
What can we expect to happen based on previous observations
Look at business trends over certain: times of day, holidays, days of the week
Provide insight into number of customers, where they are shopping and how long they are waiting
Is there enough staff on the sales floor during peak times?
Data is usually unappealing to the eye, graphical representation of data can help you see relationships that aren’t readily apparent in a spreadsheet

3. Our Data
This dataset looks at a 4 week period of simulated data from people counting and loitering time analytics
Each analytic is “binned” in an hour by hour timeframe and reports the count and average loitering times
Analytics can be broken down hour by hour, day by day, week by week. In most cases you can break the data down however you would like it.
This data was analyzed using JMP, but this type of analysis can be done in Excel.
Typically, most companies that have camera analytics will also have a dashboard to provide similar reports to the one I’ll be running through today.

5. People Counting
Can show you the flow of customers by: hour, day, week, during a particular sale on an item.
Pathway analysis on analytics systems can detect whether a customer is coming in or exiting though the front door
Even with those naughty customers that exit through the entryway or enter through the exitway
This helps us keep a physical count of # of people in the store.
Help us approximate conversion rates
Can see if a particular sale increased store foot traffic

6. People Counting By Time Of Day

7. People Counting By Time Of Day
Practical uses for this information
See a spike in business from 12-2 and 5
Send an announcement for associates to move from the back to the sales floor and checkout stands
Relative lull in business in early hours, 2-4, and after 6 may suggest this is a better time for associates to be restocking, tagging, spot cleaning, etc.

8. People Counting By Day of Week
Tells us when our busy days are.
Typically this will be pretty intuitive (less busy weekdays, more busy weekends)
See if we get more foot traffic on particular days or sales

9. People Counting By Day of Week

10. People Counting By Day of Week
Practical uses for this information
Can use relative customer volume across the week to improve scheduling practices.
For instance, if you see a 20% increase in business on the weekend, you may schedule 20% more employees than normal
A little more precise than “I know we will be doing more business, so I will schedule more people”
Can look at average customer volume compared to similar days to see if a sale made a noted increase in customer volume

11. Queue Timing by Day of Week
Line queueing has an impact on shopper experience
Line queueing analytics allows us to see how long customers are waiting to checkout
In this instance we are looking at how long the front-most person is waiting in line before being summoned by a cashier to checkout (think of waiting in a bank line before a banker summons you to their window)

12. Queue Timing by Day of Week

13. Queue Timing by Day of Week
Yikes – we have identified that waiting times in line seem to skyrocket on Saturday and Sunday
Tells us we need more people up front (but what times?)
Line Queueing by hour should be able to provide some more insight…

14. Queue Timing by Time Over All Days

15. Queue Timing by Time
Based on overall trends, we could likely correctly assume that lines are bottlenecking during the increased periods of customer foot traffic we saw with our People Counting analytics
For a further breakdown…

16. Queue Timing by Time and By Day of Week

17. Queue Timing by Time and By Day of Week
Confirms our logic that as number of customers increases, the more likely we will have customers checking out
Can also tie in analytics to determine conversion rates
By looking at the data by time of day and day of week, we can make more targeted actions to reduce queue time while having a minimal impact on labor.

18. Loitering Time
Detects the amount of time the customer is spending in a specified area.
The more the customer loiters in an area, the more likely they are considering buying something in that area.
This can be used to have targeted staff in certain areas during high traffic times.
Can be used to identify sales trends and ensure areas are stocked for the “rush”

19. Loitering Time – Product X
Analytics set up to determine the length of time a customer loiters in the product x areas
We can use the information for a “normal” amount of loitering time and use dwell time analytics to summon an associate to the area to assist in sales.

20. Loitering Time By Day – Product X

21. Loitering Time by Day – Product X
Not all analytics will be helpful
No clear pattern of loitering times by day
Hard to draw any conclusive evidence about staffing needs by day

22. Loitering Time By Hour – Product X

23. Loitering Time By Hour – Product Y
A similar analytic is placed over another product section – Product Y
Definite relationship between loitering times and conversion for the Product Y department (for example: cosmetics, clothing, shoes, certain sporting equipment)
A lot of theft also occurs in this area, but you can’t always dedicate one associate to a specific department

24. Loitering Time By Hour – Product Y

25. Loitering Time By Hour – Product Y
Fairly evenly distributed throughout the day
Hard to decipher a specific time that an associate “needs” to be in the area of Product Y.
In this case, shopping for product Y is typically done in leisure time though, so let’s see if looking at it by day of the week is a little more insightful.

26. Loitering Time By Day – Product Y

27. Loitering Time By Day – Product Y
We see that customers are much more likely to “loiter” in the Product Y department over the weekend.
May be beneficial to have several associates looking over the shoe area during the weekends, to help increase sales and decrease theft.
However, the store is open for 12 hours everyday, could we get a little more focused?

28. Loitering Time By Day and by Time – Shoes

29. Loitering Time By Day and by Time – Product Y
Now this is a lot more informative
Queue times seem to spike in the afternoon and evening times on Saturday and Sunday.
Instead of having associates around the Product Y department all day, they can make more targeted efforts to assist customers around noon and 5 pm

30. Developing “norms”
This data was “collected” over a one month period, which may not be representative of business cycles throughout the year
Benchmarking is a continuous process, should reevaluate each month or so to see if your insights hold true against: different seasons, holidays, sales.
Year over year, you should be able to build more insight into what activity is going on in your stores using various analytics
The more data the better!
Can use averages to determine what a normal customer loitering time or time in line looks like.
Customer insight can also be valuable when starting initial benchmarks
i.e. a customer likely knows how long they are willing to wait in line before they become agitated by the wait. You can use this as a rough estimate for line queueing analytics that will summon associates to the front

31. Cautionary tales
It’s important that your data is actually representative of what is going on
Sometimes an analytic can go on the fritz and say that someone has been loitering in an area for 2 + hours when really it is detecting a shopping cart
Delve into your data and look at any peculiar observations (one that is significantly larger or smaller than what you would expect)
Look at days with exceptionally high customer foot traffic and determine if there are any underlying motivators for them to come in.