After Sales Customer Loyalty Survey Data Analysis

Hawthorne Cat, the regional Caterpillar dealership in the San Diego and Pacific area, in collaboration with California State University – San Marcos, College of Business have agreed to conduct a customer loyalty study for the company’s Service and Parts departments in the San Diego and Hawaii stores. California State University current Masters of Business Administration student and Hawthorne Cat Corporation Marketing Manager, Mr. Christopher Giannaris, have identified three main objectives for the study: 1, Identify Hawthorne Cat stores that had the lowest percentage of customer loyalty during the month of June and rank them in order from the lowest to the highest; 2. identify the main causes that lead customers to give the company low ratings in their indicators for the Service and Parts departments; 3. Identify the main causes that lead customers to give the company low ratings in their indicators in each of the company’s stores. A spreadsheet containing the raw data resulted from the customer loyalty survey which was conducted within the first six months of 2018 was provided by the Hawthorne Cat team. The spreadsheet had 424 interviews inside 24 distinct worksheets, each one of them related to one or more Hawthorne Cat Stores. The worksheets were labeled as HMC Field Service, HMC Shop Service, HPS truck, HPS Commercial, HPS CSA, HMC Parts, HPC Service and HPC Parts; and, for each one of these labels, there was a worksheet for Loyal, Vulnerable and At-risk customers. The raw data was compiled by each one of the company’s stores and assigned to one of the three loyalty status (Loyal, vulnerable and at-risk). With the compiled data it was possible to determine and rank the stores by their loyalty percentage and by doing so determine the loyalty ranking of the stores. Then, for each store, the comments, complaints, and suggestion from customers were assigned to categories to determine which were the most common reasons why customers gave a low rate to indicators. It was possible, after assigning the comments to the categories, to identify the most common complaints by department (Service and Parts) and by Store. Based on the findings, there is a list of recommendations that are intended to help minimize the problems. As it is operationally very had to tackle all the problems at once, the recommendations focus on trying to solve the most frequent problems that occurred in the first six months of 2018; Since the problems from both service and Parts departments have distinct natures, the recommendations are also divided by department.