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Ongoing Decision Improvement Drives Decision Excellence

by | Mar 11, 2020 | AI, Decision Automation, Decision Management, Strategy

Following on from last week’s post on The Customer Journey to Decision Optimization, I wanted to talk about decision improvement. Let’s face it, any organization would want to make excellent decisions. Excellence in customer treatment decisions makes customers feel more valued, be more loyal and promote your brand more. Excellence in fraud detection reduces losses, while excellence in pricing maximizes profits. Customer journeys and operational processes are full of decisions that should be excellent.

Leading organizations around the world are using Decision Management to deliver excellence in these operational decisions. Developing automated decision-making, digital decisioning, makes these decisions more precise, more consistent and more agile. It delivers decision excellence.

Experience with helping dozens of companies improve thousands of decisions suggests that one particular aspect of digital decisioning is critical in delivering excellence. The organizations that succeed are those that recognize the importance of continuous improvement and that realize managing decisions requires an iterative lifecycle.

When an organization first automates a decision, there is often pressure to go for the highest initial degree of automation, shortest time to decide and most precise decision that can be delivered. Surely, the argument goes, we should invest in the best possible decision out of the gate. In practice, the changeable nature of consumers, the complexity of markets and human responses, and the wide range of possible approaches reduce the impact of this. Better, it turns out, to focus on the ability to rapidly, systematically and continuously improve the decisions. It’s not the initial rate that matters, but the speed of improvement.

Decision Improvement CycleThis means that understanding the structure of your decisions, tracking how you made them, mapping your decisions to business metrics and key performance indicators is essential. With this information you can see what works and what does not. With a strong decision management platform in place, you can use what you learn to rapidly change and update your decision-making approach. This more dynamic view of decision excellence is more realistic, more robust in the face of change and more successful. It shows you where the most opportunity lies, and gives you a strong foundation to take advantage of techniques such as Machine Learning & Optimization.

In this recently published paper I outline an effective lifecycle – review decision performance using real decision outcomes, identify opportunities to improve the data or approach, experiment to see if new data or approaches really add value, promote the changes that work and then run the new approach to gather more data about decision outcomes. Once you are efficiently managing the decision improvement process, you can see the changes that will ensure you meet your objectives and anticipate changes in the business environment.

Remember, to deliver decision excellence, focus on the journey.


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