Using Video Analytics to Improve Customer Experience Kyle Grottini – Research Scientist II - LPRC

Last time… We looked at the data “collected” from video analytics to look at customer volume, loitering times and line queueing. Have a better idea of what customer flow looks like solely from an analytics standpoint. Identified “problematic” times for line queueing where there are increases in wait time. Determined when specific departments experience more customer traffic at specific times and days Looked at average number of customers throughout the day and identified opportunities to have more associates on the floor

Improving the Customer Experience Trying to turn video analytics into extra eyes for the associates Linking analytics systems to provide real time alerts for management and public announcements to associates Meant to enhance awareness of all associates within the store built environment Looking to enhance associate availability for customers making a purchase and ensure there is an appropriate amount of associates on the floor and checkout, while not impacting their efficiency of other areas (restocking, cycle counts, tagging items, etc.) Improved associate/customer interactions and decreased line queueing time is thought to improve customer experience in the store.

The Problems Need to set alerts such that: They don’t constantly alert managers and associates Reduces confidence that the manager/associate is actually required in an area Can turn into “background noise” that associates ignore Alerts will engage the associates in a manner that will benefit the customer Line queue – customer experience likely isn’t affected if you decrease the line queue from 45 seconds to 30 seconds. How long will customers wait in line before it starts to adversely affect their shopping experience? Loitering – if a customer is in an area for 60 seconds, it may not be necessary to alert the associate as the customer can be checking out the whole aisle. How long do customers look at items before deciding to purchase?

Looking back - Queue Timing by Day of Week These waiting times are certainly higher than average, but is this unacceptable to customers?

Benchmarking for alerts Video analytics are present to assist all members of the store To get a better idea of how these analytics can improve customer experience, it is often beneficial to talk with customers regarding what their expectations are within your store. By asking targeted questions on topics that video analytics can directly impact, you can get a better idea of appropriate times to set alerts

Our Data To get a better idea of what customers are expecting, 30 customers were interviewed in an LPRC StoreLab to determine: How long does the customer “loiter” in front of an item before deciding to purchase it. Affects loitering time alerts in certain departments How long on average does the customer wait in the checkout line? Assists us in looking at average customer experience How long is a customer willing to wait before the line queue time adversely affects their shopping experience. Provides a good baseline for when to set an announcement to bring more associates to the checkout area

Analysis Simple descriptive statistics can provide us with a general idea of what the “average” customer preference looks like. Look at measures of central tendency: Mean – can be affected by outliers, but considers all pieces of data collected Median – not affected by outliers, but may not be reflective of overall trends. Imagine 2 sets of 5 customers are asked how many minutes they are willing to wait in a checkout line. Group 1 responses are 2,3,5,8, and 10 and group 2 says 0, 1, 5, 30, 45. Both groups median values are 5, but the data looks significantly different Mode – not affected by outliers, reflects the most “common” answer i.e. customers most frequently reported they wait 5 minutes in the checkout line May not be helpful if your answers take a wide range of values.

Analysis - continued No “golden rule” as to what measure of central tendency will provide the most accurate reflection of customer attitudes. Up to the researcher to look at the data and try to make sense of it. Outliers to remove Different alert times for different departments i.e. customers may spend more time considering what type of shoes to buy than deciding to purchase a piece of apparel It’s going to take some time and some trial and error before you get to the point where associates don’t feel overwhelmed by announcements and are improving the customer experience

Do Customers Like Associate Interaction? Before we send waves of employees to the customer, it’s important to determine if the customer views this as a positive thing. Most customers don’t look for associate input while they are buying condoms and underwear. Customers tend to appreciate associate interaction in most other settings though.

Do Customers Like Associate Interaction?

Do Customers Like Associate Interaction? Of the 30 customers interviewed, 90% indicated that the associates have a neutral to positive effect on their shopping experience. Customers who rated as them having a negative impact mentioned that their prior interactions with employees have been bad or not very helpful. By and large, the majority of the customers view associate interaction as a positive thing

Do Customers Like Associate Interaction? Of the 30 customers interviewed, 90% indicated that the associates have a neutral to positive effect on their shopping experience. Customers who rated as them having a negative impact mentioned that their prior interactions with employees have been bad or not very helpful. By and large, the majority of the customers view associate interaction as a positive thing

Loitering Times Now that we know customer actually want to speak with associates, we can now start developing alarms to send associates over to shopping customers. Our interest here is how long does a customer look at a particular product before deciding to purchase or try on a particular item. Want to have the associate engage the customer while they are still looking at the product. Send associates over too early and the customer may not have had enough time to contemplate if they want to purchase a certain item, but send them over too late and they won’t be able to affect a customer’s purchasing decision

Loitering Times

Loitering Times Yes, I had interviewed a customer who said, on average, they look at a product for 30 minutes or more before purchasing. We see the data is fairly heavily skewed to the right, ensuring that our average time likely doesn’t represent the opinions of the average customer. Few things you can do in this situation: Use the median or mode Delete abnormal responses and re-run the analysis and look at the average

Loitering Times

Loitering Times By eliminating respondents who indicated they spend more than 15 minutes looking at items before deciding to buy, we have an average which is more reflective of “average” customer behavior. Looks like customers look at an item for about 3 minutes before deciding to purchase. The analytic can be set to notify employees to report to an area if the person has been loitering for 2 ½ minutes. Want to capture the customer while they are in the decision making process, not after they have made a decision

Line Queueing – Average Customer Wait We want to find out how long a customer is willing to wait in line before it starts to negatively affect their experience. This can be shaped by personal preference as well as their relative experience at the location. i.e. compare waiting times at a store vs. the DMV. A person is relatively thrilled if they can get out of a DMV line in 30 minutes but infuriated if it takes 30 minutes to checkout with their groceries Line queueing times can vary heavily depending on: average number of lanes open, the way in which the line forms, the basket size of other customers.

Looking back - Queue Timing by Day of Week These waiting times are certainly higher than average, but is this unacceptable to customers? Let’s see what they have to say!

Line Queueing

Line Queueing – Average Customer Wait This question had a peculiar distribution; even though the question was open ended (customer could have answered 10 seconds, 326 seconds, 238 seconds) but the majority of the customers indicated 5 minutes. We see that mean, median and mode are all pretty close to 5 minutes here, suggesting that this could be a good benchmark for average customer wait time. Now that we know how long the “average” customer waits 5 minutes in the line, we want to see how long they can wait before it adversely affects their experience This average wait time may be unacceptable to customers, so we may have to make some major changes to our checkout process if we find that customers don’t want to wait that long.

Line Queueing – Average Customer Wait Tolerance Customers were then asked how long are they willing to wait before their shopping experience is negatively affected. Again, this is fairly subjective feedback but may provide a good benchmark for alerting associates to go to the front

Line Queueing

Line Queueing – Average Customer Wait Tolerance Nearly half of the respondents indicated that any amount of time over 10 minutes starts to effect their in store experience. 4 of 30 people said that no amount of time would really impact their experience. Most said if they really wanted an item, then they’d wait to purchase it. We now know that the majority customers are used to waiting 5 minutes in the checkout line but would not want to spend any more time than 10 minutes We could then set our line queueing alert to send a message to the manager or call associates to the front over the PA system. This can be set after a customer has been in line for4-6 minutes.

A word of caution Customer feedback is subjective and may not accurately reflect their actual in store experiences. Several customers indicated “no amount of wait time” would affect their shopping experience Customer feedback also suffers from rounding error Over 50% of the customers indicated they spend an average of 5 minutes in a checkout line. Customers vary significantly throughout the country Anyone who has checked out of a store in New York knows it is typically much faster than checking out in South Carolina. “Average” customer experience can vary due to a multitude of reasons. Previous experiences in the store Their personal life Things that happened earlier in the day