When William Shakespeare wrote “To be or not to be” for Prince Hamlet to speak and express his contemplation for embracing the universal truth; little did he know that he would be quoted in various different contexts for different types of effects. A coward soldier saying; “to flee or not to flee”; a conniving trader evaluating an unsuspecting customer; “to fleece or not to fleece”; the colonial masters strategizing their exit; “to free or not to free”. And as guessed by you; a data scientist upon stumbling on a couple of interesting variables; “to correlate or not to correlate”.

The first step in predictive modeling is defining the problem. Once done, historical data is identified, and the analytics team can now begin the actual work of model development. In this blog, we touch on the business factors that influence model development. If you find this interesting and want a deeper dive, you’ll have the opportunity to download our whitepaper that goes into more detail on this topic.

Most insurers’ view of their policyholders is in isolation - one policyholder at a time, with the possibility that more than one individual in a household may have different or multiple policies from the same insurer. As such, the premium impact of the household is larger than that of the individuals.

Today, customers expect a personalized, unique experience. Millennials not only expect a superior experience but also expect their service provider to know in advance about the kind of treatment they prefer to receive. A critical step in delivering a unique experience is to know what your existing customers think about you and your services.

Virtual assistants like Siri, Cortana and Alexa as well as other speech synthesis techniques have solved many customer use cases by offloading repetitive and mundane searches or activities. Customer-oriented businesses leverage this technique to provide better operational efficiency and improve customer experience. They can then run analytics over the voice/audio content to derive predictions.

Insurers have a near-constant stream of unstructured data at their disposal that can be used to drive growth by improving policyholder retention and identifying cross-sell and upsell opportunities. One of the challenges for insurers is sorting through this mountain of unstructured data quickly to gain an accurate understanding of the sentiment of their customers in real time.

According to the Coalition Against Insurance Fraud, at least $80 billion in fraudulent claims are made annually in the U.S across all lines of insurance. This translates more than $400-$700 per year in increased premiums for each American family.

Bajaj Allianz Life Insurance Company recently hosted a unique insurance summit focused on putting customer experience first. The event titled Future Perfect-Customer First Insurance Industry Summit 2018 was a full-day event that was attended by 21 of the 24 insurance companies that operate in India. While the focus was life insurance, the summit also saw participation from a few general insurance carriers.

Cross selling is a natural extension of trying to get a larger share of the customer's wallet. After acquiring a new customer, the sales and account management teams put their entire effort into: 1. Ensuring that the customer spends more with them; and 2. The customer buys another product or a service from them.

'Retention' refers to the ability of a company to retain its customers over some specified period. High customer retention means customers of the product or business tend to return to, continue to buy or in some other way not defect to another product or business ‘Persistency’, during a period may be defined as the proportion of policies remaining in force at the end of the period out of the total policies in force at the beginning of the period. In other words, persistency is the percentage of business retained without lapsing or being surrendered. Low lapsation means high persistency and vice versa.

Big data analytics find immense application across the entire business. One area which can have direct, measurable and visible impact is the area of customer service. Data has been used since the beginning of time to improve customer service, but it is only recently that the full power of predictive analytics is being applied in this function. Organizations, both large and small, are using big data analytics to deliver superior customer service and build strong customer loyalties. See below as we look at 6 ways big data analytics is driving smarter customer service.