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$44 Billion Spent on CRM in 2000 and Customer Relationship Management is Worse than Ever! Consider these examples:
Why do these disconnects occur? The answer is actually quite simple: lack of true enterprise integration. As today's corporate environment becomes more competitive, companies cannot afford to simply accept the limitations and disadvantages of offline data integration. They must find ways to build the intelligence they garner about customers into their operational environments, enabling them to fulfill customer demands at every touchpoint in a coordinated fashion. By operationalizing business intelligence (BI) not just data forward-thinking companies can transform themselves into true customer-focused enterprises with distinct competitive advantages. Operationalized Business Intelligence Incorporating BI into the operational environment is the critical step for improving customer interactions. Through this process, a company can synchronize its enterprise around the customer rather than just synchronizing data around the customer. The key to deploying BI is a flexible business rules management system that can house and deliver the rule results. This information hub becomes the marketing coordination system across the enterprise, housing all activities related to customers. Because the scores, rules and other intelligence are housed within the system and applied to the real-time data streams as they occur, the system does not rely on batch scoring or batch data feeds. These business rules management systems have a variety of requirements, including:
Integrated Architecture Load of Analysis Environment Deployment of Knowledge
For the purposes of this article, we have proposed additional definitions of the rules to illustrate typical real-time triggers in a marketing context.
The rules will be created either by using the editor or importing rules from integrated analysis applications. The business rules editor provides two very important features: verification that all rules created are valid and validation of existing business rules in the event there is a change in any of the data stores used by the business rules. To achieve this type of verification and validation, a meta data layer is housed within the business rules repository and used by both the rule triggering engine and the business rules editor. Operationally Optimized Systems The architecture depicted in Figure 2 facilitates the three critical processes that provide an adequate framework to transition corporate data into actionable knowledge to "operationalize" the business intelligence. Architecture Components Business Rules Repository: Repository where all business rules are stored. The business rules editor allows rules to be "published" into the business rules repository. From there, the rule triggering engine, initialization database and ETL processes can reference the rules and integrate them into their processing. Included in the business rules repository is a meta data layer housing information about all data access methods and data models within the information architecture. In this way, the processes utilizing the business rules repository can properly access and utilize information from the entire information architecture, thereby removing the need for redundant data except for performance and availability requirements. Rule Initialization Database: The initialization database contains a rolling horizon of detail data about all customers with a data model the same as the sample database in the analysis environment. The purpose of this data store is to provide a way to initialize rules that reference detail behavior across a time horizon. For example, if a rule is deployed that says, "Trigger campaign A when event A, event B and event C occur within 20 days," the initialization data store allows the behavior over the last 20 days to be summarized and counted rather than waiting 20 days from initial deployment for the rule to be fully populated. The option exists to create triggers if the event criteria were met in the time horizon covered by the initialization database or to just summarize events without triggering. Following are two examples using this rule to help better describe this behavior:
The purpose of having these two options is to provide the ability to make new rules retroactive for the period of the rolling horizon in this case, 20 days. The initialization process can task many hardware platforms. Based on Daman's experience, the solution could take many forms, including near-line or offline storage systems, distributed parallel processing, etc. By using the meta data layer within the business rules repository, data may be distributed in a cost-effective manner allowing timely deployment of rules to address any business needs that arise. Raw Event Logs: Raw data from the rule triggering engine is provided from triggered events. During this process, it is transformed and loaded into the relational environment of the analysis database either in real time or on a batch basis. Extraction, Transformation and Load (ETL): Takes the incoming data from the daily load files, applies the business rules and loads it into the relational database supporting the analysis environment. It is important that rules on derived attributes used in the real-time engine are the same as those used in the ETL process. This provides a single point of management for computation of derived attributes and/or data transformations. Analysis Database: Relational database system housing three major categories of data: detail data on a sample of customers, summarized historical data for all customers and campaign data. The analysis database provides the input to the analysis tools to derive knowledge about the customers. The business rules repository feeding the ETL process provides derived attributes for the analysis database. This process also assures consistency between offline analysis and rules used to trigger real-time events. Addition of derived attributes after initial implementation is handled through the dynamic creation of tables and updates to appropriate meta data. This can all be handled as an automated process. Analysis Environment: The analysis environment is a set of tools used to mine information from the analysis database. Typically there are three classes of tools in this environment: reporting, modeling and rule deployment. The reporting and modeling tools are those off-the-shelf applications such as MicroStrategy, Brio, SAS, SPSS, etc. The business rules editor is used to create the business rule in the rules repository either by publishing objects created by the mining tools or allowing analysts to create combinations of rules based on coded action-enablers and core rules. Rule Triggering Engine (RTE): The RTE manages the pre-calculation of business rules and the incorporation of additional data as it comes in from messaging or detail data updates. The RTE maintains its own data store of pre-computed values and handles the application of the business rules. It maintains "copies" of the business rules so it can immediately apply the rules to selected incoming data. On a scheduled basis, it will update the pre-computed information using the daily log files, the initialization database and other data stores as appropriate. Behavior Trigger Interface (BTI): The BTI provides the interface into triggered rules. The BTI can range from a simple service to a service integrated into transaction and load-balancing software. Fulfillment Database: The BTI can provide a trigger to a fulfillment database that contains the full information on the marketing promotion that is to be delivered. This may be another network, Web site, legacy system, etc. By keeping this system separate from the underlying architecture, it is more easily maintained by third parties or other marketing personnel without impacting the underlying triggering mechanisms. Other Marketing Stakeholders: In the most generic sense, the BTI is a messaging hub that can have information requested or delivered through any network or API. The interfaces are typically for those who are either managing the customer interface or providing fulfillment of the message. Enterprise Integration All of this takes on even greater importance during an economic downturn. In such a climate, enterprise efficiency provides an avenue for retaining profitability without shedding personnel en masse or shuttering whole operations. The bottom line is this: Corporations that take advantage of today's integration technologies and tactics will probably survive; those that don't will likely falter. |
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