Kimball University: Extreme Status Tracking For Real Time Customer Analysis
Customer interactions create a wealth of timely data that marketing departments are eager to exploit. The customer status fact table provides a central switchboard for using this fast-moving data.
The behavior dimension can be modeled in two ways. The simpler design is a wide dimension with a separate column for each behavior tag type. Perhaps these behavior tags are assigned by data mining applications that monitor the customer's behavior. If the number of behavior tag types is small (less than 100), this design works very well because query and report applications can discover and use the types at run time. New behavior tag types (and thus new columns in the behavior dimension) can be added occasionally without invalidating existing analysis applications.
A more complex behavior dimension design is needed when a very large and messy set of behavior descriptors is available. Perhaps you have access to a number of demographic data sources covering complicated overlapping subsets of your customer base. Or perhaps you have account application data containing financial asset information that is very interesting but can be described in many ways. In this case, you will need a dimensional bridge table. Kimball Group has described dimensional bridge table designs in previous articles. Search for "bridge tables" (in quotes) at www.kimballgroup.com.
The status dimension is similar to the behavior dimension but can probably always be a wide dimension with a separate column for each status type, simply because this dimension is more under your internal control than the behavior dimension.
The transaction dimension describes what provoked the creation of the new record in the customer status fact table. Transactions can run the gamut from conventional purchase transactions all the way to changes in any of the customer-oriented dimensions, including customer, behavior and status. The transaction dimension can also contain special priority or warning attributes that alert applications to highly significant changes somewhere in the overall customer profile.
The begin and end effective date/times are ultra-precise, full-time stamps for when the current transaction became effective and when the next transaction became effective (superseding the current one). Kimball Group has given a lot of thought to these ultra-precise time stamps and we recommend the following design:
The grain of the time stamps should be as precise as your DBMS allows, at least down to the individual second. Some day in the future, you may care about time stamping some behavioral change in such a precise way.
The end effective time stamp should be exactly equal to the begin time stamp of the next (superseding) transaction, not "one tick" less. You need to have a perfect unbroken set of records describing your customer without any possibility of miniscule gaps because of your choice of a "tick".
In order to find a customer profile at a specific point in time, you won’t be able to use BETWEEN syntax because of the preceding point. You will need something like
as your constraint, where Nov 2, 2009, 6:56am is the desired point in time.
The customer status fact table is the master source for the complete customer profile, gathering together standard customer information, behavior tags, and status indicators. This fact table should be the source for all other fact tables involving customer. For example, an orders fact table would benefit from such a complete customer profile, but the grain of the orders fact table is drastically sparser than the customer status fact table. Use the status fact table as the source of the proper keys when you create an orders fact record in the back room. Decide on the exact effective date/time of the orders record, and grab the customer, behavior, and status keys from the customer status fact table and insert them into the orders table. This ETL processing scenario can be used for any fact table in the EDW that has a customer dimension. In this way, you add considerable value to all these other fact tables.
This article has described a scalable approach for extreme customer status tracking. The move toward extreme status tracking has been coming on like an express train, driven both by customer facing processes that are capturing micro-behavior, and by marketing’s eagerness to use this data to make decisions. The customer status fact table is the central switchboard for capturing and exposing this exciting new data source.
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