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Recently, machine learning algorithms surpass humans intelligence in many areas (go, chess, poker). Operational optimization (logistic, back-office) and customer behaviour predictions (marketing, sales) are some of the top priorities in companies to digitize their business.
But only a few can remember that it all started in Bletchley Park with the need to break the Enigma Code. Without business analysis techniques they probably wouldn’t have succeeded. BA approaches that were used back in the day are still valuable today.
We will present two real (banking sector) cases and their results to demonstrate analytical phases of designing, developing and using predictive analytics models that process customer data daily and recommend actions, based on predefined business rules and decision points in workflows. From Stakeholder Needs aligned with their Value we will show how to build smart predictive algorithms to determine the “next best action” and “preferred channel” in the Context of better CX.
Defined KPI’s measure VALUE daily and enable BA to monitor effectiveness and efficiency continuously, detect potential issues and take necessary corrective actions. In 5 months we increased the VALUE to 450% and needed 16 days to achieve ROI.
- Examples of different approaches we have taken to implement valuable predictive analytics solution, including what works and what doesn't
- How BAs can balance between external Customer eXperience view and internal stakeholders need to maximize the value of the project
- Large quantities of data exist, but the value is in analytics, not only the right algorithms that will work, but that they improve CX and add value.
- Approaches to ensure that algorithms and procedures used within project are "good enough"