Business leaders and stakeholders often think about right time to start looking at analytics and sometimes fall shy due to concerns surrounding data availability, quality of data, lack of resources and value of the overall exercise. We have been asked quite a few questions ourselves in last couple of months by decision makers across Insurance industry. Frequent ones are quoted below with response
Assumption 1: We just have few thousand records, i am not sure if this is enough for any kind of predictive analytics.
That’s a valid observation, for any predictive model to be successful we need to build and validate it on sufficient dataset. Generally you can have a fairly good model for 1000 records and atleast 100 events. Example 100 lapse in 1000 observed customers. As a thumb rule in addition to above point for each variable used for prediction there should be at least 20 records.Ex if 10 variables are used for prediction, minimum no of records expected are 10*20 i.e. 200. This whole process can help you identify deficiencies in data collection process, like missing values, invalid data or some additional variable should have been collected. Such interventions at early stage can be very helpful and can go a long way in improving data quality.
Assumption 2: Our data quality is too bad, I don’t think we can do it right now
Addressing data quality is core to the process of modeling. Data once imported is processed to bring it into meaningful shape to proceed for any further analytics. Availability of high computing power at lesser cost makes sure any size of data is small nowadays and can be processed in lower time and cost.
Assumption 3: I am not too sure on the Return On Analytics
The real fruit of analytics is not just in the scorecards or numbers but also in the way it is integrated and implemented within organization. Having an list of customers in excel scored on basis of lapsation might not be much useful but if it’s real time and integrated across IT ecosystem of web or mobile giving your agents, Customer Service team insights into consumer behavior every time he interacts with your firm, it becomes much more actionable. Think about product affinity ratings for customer integrated with Tablet app agents carry these days. Not only your agent will be able to push right product to the customer based on his needs but importantly build a long term relationship.
Assumption 4: I already have basic predictive modeling initiatives running but not very effective. What more can I do!
Basic premise of any analytics initiative is framing the right question, having the right data at hand and finally a strong actionable strategy. Doing this right will definitely result in good show. Once you have considered looking at internal data sources, you can also try adding external data sources like CIBIL, Social Media and economic indicators like Inflation, Exchange rate etc to glean information about financial behavior, consumer life style and events. Frame hypothesis which you would want to validate against external data sources and test them.