Centralized decisioning and it is use on real-time micro-operational decisions has become a must on today’s enterprise strategy. As the core of this technology is the business rules management system (BRMS) that allow business users to create, modify, deploy and optimize their business logic without or with a minimum intervention of an IT department.
Business rules are basically made of the logical if…then…else declarative statement where, on an enterprise-wide project, the outcome might be a large set of decisions. Decisions over decisions. A series of attributes must be needed to filter out the final choice. An example of these attributes are the business performance goals as increasing product margin, reducing customer churn, reducing customer fraud, etc. where one, or a weighted combination of all, drive the final result. All these ‘goals’ are a series of business traditional dimensions, that not always provide the most optimal result. Setting the margin or price as the main measurement to prioritize an offer might result in a low acceptance rate, and setting the lowest cost offer as a goal might result in offer cannibalization.
So real-time smart decisions are here, but where is the intelligence to support them? Here is where Predictive Analytics steps in, not only to look at the past and respond the why, but to project into the future and respond where. Adding predictive analytics to business rules improve business results by adding fact supported and educated insights to any decision. Of course, the GIGO paradigm applies here as on all data related process. Modeling the data to predict an expected behavior about a customer, product or transaction is an important service to be used to support a decisioning. The predictions are made in the form of propensity or likelihood scores of a customer accepting an offer, a transaction being a fraud, a customer churning, or a customer defaulting a loan to name some.
A model score can be returned from different sources:
- Offline scores: the models are executed in house or by a third-party provider and assigned as an static attribute that is fetched during an interaction.
- Inline scores: the models are pre-defined as declarative statements and scores calculated on real-time. Examples of inline scores are scorecards and decision trees
- Response-based adaptive models: The model score starts as a preset value and it is automatically fine-tuned overtime based on the historical recorded response at to the moment of the decision.
There is no prescriptive rule about which method to use during the decisioning process, and is more related about the enterprise sophistication and how are their resources prepared to handle Analytics as business as usual. Most of today’s analytics platforms have their business counterpart that take the complexity out from a basic analysis, and let the business user generate insights without a high degree knowledge of statistic. But again, the answers are always as good as the questions.
In summary, your real-time project can be optimized when the power of analytics is added as a support factor to other business goals, or as an standalone factor. In a future post, we will cover how all business attributes and goals can interact in real-time to drive a decision.
By Leo Guim, NeoFusion
