忠诚度管理.ppt

* This represents a more detailed examination of loyalty within a specific Ford. Incorporating their data, as well as Experian data, to analyze loyalty in terms of variables such as: Vehicle owned Purchase history with corporation Warranty / Service data Captive financing Leasing/Purchasing etc, etc. * * We will adjust category labels based on specific OEM usage (brand vs make, etc). * AHM: OEM and Acura vehicle sales data Dealer master file AHFC: Vehicle financing data Service contract data Experian: National Vehicle Database (competitive vehicle sales) INSOURCE (household demographics, lifestyles, etc) * We will adjust category labels based on specific OEM usage (brand vs make, etc). * We will adjust category labels based on specific OEM usage (brand vs make, etc). * Drivetimes – MMg3 uses a component from SRC to create drivetimes called ‘Guzzler’– it will create a drivetime polygon from a start point by using a dataset built at the local street level. Speeds are assigned to each class of road segment and are then used to calculate the travel time at different rush hour periods.? The output is simply a polygon that covers the area reachable within the time.? Note that MMg3 allows you to create drivetimes in batch, so you could create polygons for 15,10 5 minutes around every store in one simple operation. MMg3 can also provide point-to-point travel times or distance.? A new feature was added with the latest release that allows you to find the distance or time between a) a list of customers to the store they visited b) the nearest N stores to each customer and c) every store to every customer.? This would allow you to calculate the average distance travelled to dealers, and you could do the analysis separately for each of urban, rural and metro. The ability to create a distance decay chart is not currently available, although this is being reviewed for the future. ?????????? Stage 1 - Data collation *?????????? This may be market data such as