Case study topics:
- Conversion Rates – Conversion rates may vary by day of week within the same store. Stores within the same chain will have different conversion rates depending on where they are located, staffing and shopper traffic patterns outside of the stores. Retail chains cannot assume that all of their locations will have the same conversion rate.
- Sales forecasting methodology outline – Sales for a location may be forecast by knowing how many passersby there are for the store location and the “enter” rate. Those who are in the store are “shoppers.” By knowing the store’s conversion rate, the number of transactions can be estimated by multiplying the shopper traffic by conversion rate. Finally, by using the average dollars per transaction (average ticket or receipt) the number of transactions can be multiplied by the average ticket to forecast sales for any time period. “What if” scenarios can be generated by altering the shopper traffic, conversion rate or average ticket.
- Real-time occupancy – The case described was for the luncheon buffet for Sberbank. The buffet was experiencing long customer lines as customers were going through its buffet during the noon hour. CountBOX has a real time occupancy feature that allows bank employees to see how many people are in the buffet area at any time. Employees now can view the number of people in the buffet before they come to it by accessing the CountBOX occupancy on their computers or smartphones. Since implementing this service, lines in the buffet are not so long as employees are either coming early or eating their lunch at a later time during the day.
- Facial recognition case – Sberbank also uses CountBOX facial recognition technology in its customer service area. Customers’ moods are determined by the facial recognition software as the customers enter and exit the customer service area. Sberbank wants its customer service representatives to “delight” its customers. Therefore, the goal is to have happy customers leaving the customer service area. By using the CountBOX facial recognition technology, Sberbank can determine the percentage of customers leaving the customer service area “happy.” The data is also used to coach employees in how to improve the way they interact with customers so as to “delight” them. Discussion ensued as to how large retailers such as Walmart, Target, Best Buy, etc. could use the technology to improve their customer service areas.
- Employee “rating”/bonus splitting case – the case described how to split a bonus pot among employees using conversion rates attained for the day while employees were on the sales floor. Essentially, every employee who was on the sales floor for a given day was assigned the actual conversion rate for that day over the bonus pot time period (say a month). At the end of the time period the assigned conversion rates were averaged for each employee. The average conversion rate for the employees represented the overall conversion rate contribution for the employee. The bonus pot would then be split according to each employee’s contribution. Discussion ensued as to how this could be used in rating employees by HR and store managers. Also the metric could be refined by normalizing traffic and possibly by weighting by the number of days worked within the time period. Overall this is an excellent metric not normally used in retail.
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