Being data-driven can help organizations improve their processes, drive better decision-making, provide better customer experience and eventually enabling improved business outcomes. Data-driven strategies use data to allow an outcome-driven collection process, and the insights are used for decision-making purposes. But many companies struggle with managing large volumes of data that they collect from various touchpoints and often fail to use them for meaningful insights or fail to use them to improve operational processes and better business outcomes. A data-driven strategy can be used in many areas across the operations like arriving at proper segmentation, modelling user behaviours, modelling various treatment options for recovery, modelling agent performance, arriving at the right customer contact strategy etc. Data-driven strategies use both historical and real-time data. Historical data includes data like customer data, transaction history, credit history etc. Real-time data includes real-time customer consumption patterns and usage data. This data can be used to build different prediction models and to enable decision making. Examples include building models to predict customer delinquency, using data to create a customer’s risk profile, using data to understand user behaviour, and accordingly creating a personalized contact strategy, etc.
Some of the key challenges in implementing a data-driven strategy:
Challenges in implementing a data-driven strategy could be multiple, and it can span across technology and process.
So, this means implementing a data-driven strategy is both a technological and cultural change. This change needs to be appropriately managed at both levels for the desired outcomes.
Below are some of the key challenges that organizations encounter in implementing a data- driven strategy.