Leveraging Customer Analytics for Business Success – Decision Point
Article Synopsis :
In the era of Big Data, businesses must be smart about how they deploy analytics tools in pursuit of customer insights.
“Leveraging Customer Analytics for Business Success” from Knowledge @ Wharton is an interview transcript with Wharton professor Peter Fader and WNS executives Raj SivaKumar and Mike Nemeth.
Key takeaways from the interview include:
1. Analytics are of three types:
- Descriptive Analytics: simple data collection and visualization.
- Predictive Analytics: trend identification and building stories.
- Prescriptive Analytics: future actions based on collected insights, useful in planning resources.
2. Data sources include demographics, media habits, Net Promoter Score (NPS), social media and wearable technology.
3. Understand that not all data collected and stored will be useful. First, get to know the data. Then ask how the data can be used for your purpose.
4. The data the insurance industry has been collecting is not customer data. Rather, it’s more risk data – and these aren’t the same thing. Insurers need to understand the importance and value of a customer-centric data gathering approach.
5. The future of customer analytics lies in the quality of data collected. Focus on data usability first, and then the science of using it to your advantage.
6. One of the keys to success is adding domain expertise to analytics project teams. Analysts, tools, and data aren’t enough. Domain expertise is required to inform the entire process.
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Digital Insurer's CommentsInsurers across regions are striving to enhance customer experience via customer insights mined from the vast amounts of data they possess. The problem is most of the data isn’t customer data—it’s more about the risk, i.e., the house, the boat, the car. Collecting information specific to the customer is a relatively new thing in the insurance industry.
Carriers are learning how to leverage new sources of information and synthesize it with existing data sources for good analytics results. In our view success is not just a matter of collecting more data, but changing the type of data you collect and the questions you ask of it in a very transformational way.
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