Most business areas will contain more decisions that can be addressed in a single Digital Decisioning system project or even a set of such projects. This means that the decisions identified will need to be prioritized so that incremental progress can be made. In general decisions can be ranked in various ways for prioritization, and a “hotspot” analysis can be conducted.
The enterprise-level purpose of the project—whether it is to increase customer retention, increase profit per customer, reduce cost per unit, or some other target—is a critical driver for prioritization. Use this to identify the measures and KPIs that most closely align to the project’s overall purpose.
By analyzing the decisions that support these measures and KPIs, you identify the decisions that relate most closely to the project purpose and to set aside, at least temporarily, decisions that relate to this goal only loosely. Part of the association between a decision and a measure, or KPI, is an assessment of how direct the association is and of the contribution the decision makes. Those that make a larger and more direct contribution to the critical measures should be put in a preliminary list of prioritized decisions.
To further rank the decisions from highest to lowest priority, you can consider these factors:
- How measurable is the decision’s impact on the measures it impacts?
In general, it is not helpful to target decisions that are not going to be easy to measure, as it will be difficult to show that the project made a difference.
- How big is the difference between good and bad results in terms of revenue, risk, or loyalty?
The bigger the gap, the larger the potential payoff is for improving the quality of decisions.
- How often do you make the decision?
More decisions act as a multiplier, and this means more impact from a Digital Decisioning system.
- How much spend is committed as a result of the decision?
Decisions that commit the organization to large amounts of spend such as expensive investigations or purchases can be worthwhile even if the improvement is quite modest.
- How much does it cost to make a decision?
If a decision currently costs a great deal in terms of staff or expert time, then a Digital Decisioning system may offer a high payoff.
- How hard will it be to develop a Digital Decisioning system for the decision?
A quick and dirty assessment of the technology and process change required to implement a Digital Decisioning system can help prioritize those decisions that will be easier to implement.
In addition, the candidate decisions can be plotted to try and show the “hotspots.” This is an analysis showing which decisions should be the focus of immediate projects, given organizational and technical realities. For instance, the value of each decision could be ranked into high, medium, and low. The difficulty of implementation and time to market could be similarly assessed. Each decision can then be plotted on a bubble chart such as the one shown below.
In this example, the technical difficulty is on the horizontal axis from most difficult to least difficult, the time to market is on the horizontal axis from slowest to fastest, and the bubble represents the value of the decision, with larger bubbles for higher value. With this approach, the larger bubbles that are closer to the top right the ones to prioritize.
The exact prioritization approach taken should be varied based on a number of factors:
- Organizations developing their first Digital Decisioning system should prioritize based on which decisions seem most readily implemented and least controversial. Early success is important, and this should drive the prioritization.
- Organizations with extensive portfolios of Digital Decisioning systems should include the ability to reuse and exploit existing development assets and infrastructure.
- Organizations that struggle with organizational change should prioritize those decisions that are already implemented, if poorly, as there will be less organizational disruption involved in replacing one automated decision with another.