May 4, 2021

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.

Organizations around the world strive to achieve profitability in their business. To become more profitable, it is essential to satisfy the needs of customers. But, when variations exist between individual customers how they can effectively do that. The answer is- by recognizing these differences and differentiating the customers into different segments. But how do organizations segment their customers? And in this article we’ll help you understand this from a data science perspective. What is customer segmentation? Customer segmentation is the process of dividing the customer base into different segments where Each segment represents a group of customers who have common characteristics and similar interests. As explained above, the exercise of customer segmentation is done to better understand the needs of the customer and deliver targeted products/services/content. With time, all sorts of organizations from e-commerce to pharmaceutical to digital marketing have recognized the importance of customer segmentation and are using it improve customer profitability. Customer segmentation can be carried out on the basis of various traits. These include : How to perform customer segmentation? Start with – Identifying the problem statement One of the foremost steps is to identify the need for the segmentation exercise. The problem statement and the output expectation will guide the process of segmentation. Example: In both the cases, the intent or need to perform customer segmentation is different. This will further determine the approach taken to achieve desired outcome. Gathering data Next step is to have the right data for the analysis. Data can come from different sources- internal database of the company or surveys and other campaigns. Other third party platforms like Google, Facebook, Instagram have advanced analytics capabilities to allow capture of behavioral and psychographic data of customers. Creating the customer segments Once you have defined problem statement, and gathered all the required data for it, the next step is to carry out the segmentation exercise. Key steps here will be: Data science and statistical analysis with the help of machine learning tools help organizations deal with large customer databases and apply segmentation techniques. Clustering, a data science method, is a good fit for customer segmentation in most of the cases. Usage of the right clustering algorithm depends on which type of clustering you want. Many algorithms use similarity or distance measures between data points in the feature space in an effort to discover dense regions of observations. Some of the widely used machine learning clustering algorithms are : Segmentation backed by data science helps organisations to forge a deeper relation with their customers.  It helps them to take informed retention decisions, build new features, and strategically positioning their product in the market.

“Analytics is a journey and not a destination!! It takes considerable effort to frame that journey and execute it with a sense of purpose. You will encounter stumbling blocks that may threaten your initiative but you need to find a way out and keep marching ahead.” How is it like to build a data analytics strategy? We did a data analytics exercise for a US client recently in education domain that had all the flavors of roadblocks one can encounter on venturing into analytics territory. I intend to summarize those here along with solutions we found in collaboration with all stakeholders Takeaways This was just a month’s exercise. Surely we will hit many such scenarios ahead.