Determining the Customer Arrivals at Starbucks Following the Closure of Nearby Stores

Poisson Distribution
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Poisson Distribution
A Poisson distribution tool is used in predicting the probability of occurrence of particular events when the frequency of occurrence is known from previous data (Consul & Jain, 2015). This essay addresses a case study of determining the customer arrivals at the Starbucks store following the closure of the nearby stores. Anne, the store manager, is concerned about the staff-customer numbers with this change. She, therefore, has to determine the number of customers visiting the store in a given time to know the number of staff to employ. Understanding the possible customer arrivals is necessary before adopting any further statistical inference.
Anne believes that the typical Starbucks customer averages 18 visits to the store over a 30-day month. The following questions need to be addressed;
1 How many visits should Anne expect in a 5-day period from a typical Starbucks customer?
It is required that the number of successes in the space interval or relevant time is determined first. The Poisson process condition that the probability of success occurs in any interval is similar for all the intervals of equal size and is proportional to the size of the interval is used. In this case, the relevant mean is based on the rate of 18 visits over the 30-day month.
With a rate of 18 visits in a 30-day month, the mean for the 30-day period is written as µ30 = 18. To compute the proportional mean for a 5-day period;
18 visits/ 30 days = 3 visits/ 5 days.