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 Chuck Schaeffer How Financial Services Leaders Are Succeeding with Big Data


Big Data For Sales

Big data can aid sales goals such as increasing customer acquisitions, growing customer share and lowering customer churn.

To acquire more customers, insurance companies are using big data and the Internet of Things to create mobile connections between policies and both customers and non-customers. Using a sensor in the car or a mobile device app, driving data can be forwarded to the insurance provider. This telematics data may include driving habits such as number of trips per day, average vehicle speed, average miles driven, rate of acceleration or how sharply a driver brakes. This data is compared with other driver data, claims data, actuarial data, and policy and profile data to determine the best rate for each driver based on their driving behaviors, history and risk.

Insurance mobile apps also offer convenience features which contribute to the customer experience. These apps display the insureds proof of insurance card and policy, find parking spots, get roadside assistance, file claims, schedule vehicle inspections, reserve rental cars and monitor the claims and renewal processes.

Consumers benefit from good driving practices which lowers their insurance premiums. They may also receive discounts, rewards and personalized programs to improve their driving habits and further lower insurance expense. Insurers can use the data to offer more tailored products and premiums based on more accurate use and risk factors.

Insurance companies also benefit by using these apps as sales tools. For example, an insurance company in the Southeast allows mobile app downloads to non-customers who may be interested in features such as real-time traffic displays, cheap gas locations, parking finders or trip information such as distance, average speed and the like. However, mixed in the mobile app display is an estimated auto insurance rate from the app provider — essentially displaying the rate if the driver were to switch his or her auto insurance to the app provider. For this insurance carrier, these apps achieved a 7 percent conversion rate from non-customers. Not only has this delivered the single biggest boost to new customer acquisitions, but these new customers are considered the lowest risk and most profitable customers.

This auto insurance example is easily transferable to a life insurance scenario whereby insurers may monitor customer lifestyles using wearable technologies (i.e. watches or wristbands), even going so far as to be alerted to customers attending events or high adrenaline adventures from their Facebook updates or FourSquare check-ins.

Most consumers will not permit this type of monitoring but the ones that do opt-in and select the level of monitoring they are comfortable with are likely to be placed into actuarial segments which reduce their costs and increase their benefits. This will ultimately impact the underwriting of the remaining non-participating consumers in a negative way as their lower risk opt-in peers will no longer influence the underwriting calculations for the non-participating group.

Another financial services big data sales example is correlating online digital footprints with consumer profiles. Savvy FSIs are analyzing website and social media data to identify products and services frequently viewed (i.e. as measured by the volume of page reads, time spent on pages and low exit points) but not as frequently purchased in order to uncover the barriers standing in the way of purchase, and unlock otherwise hidden sales opportunities. When advisors or client account managers proactively bring up a financial solution which has been reviewed by a customer online multiple times or for a long duration, but not purchased, they are essentially retargeting and will achieve a significantly higher sales conversion rate as compared to offering a product that has not been previously investigated by the customer.

Increasing Customer Share

In the financial services industry FSIs are either growing or dying. There is no standing still in a fluid market. Therefore, continuously increasing customer share and market share are critical success factors and measures of viability.

Next Best Offer (NBO) technology combines big data and sophisticated algorithms in order to increase customer share. Bayesian algorithms examine customer segments and cohorts such as each consumer's existing products, products acquired by like consumers, household data, social data, third party aggregate data of financial solutions procured elsewhere and various types of other big data in order to apply predictive analytics and determine the most likely accepted cross-sell or up-sell. Applying a combination of data to determine the most relevant offer and delivering that offer during the next customer interaction is a financial services best practice.

The concept is similar to Amazon and its recommendation of books and other products based on people that purchased similar books or demonstrated similar interests or behaviors.

Amazon Recommendation

This technique also increases the bottom line as up-sell products are typically higher margin sales.

Recommendation engines also constantly refine their algorithms in real-time based on each customer interaction which increases the frequency of offers which convert and decreases offers which don't. Over time the technology develops predictive conversion confidence levels based largely on the combinations of customer profiles and product attributes. This sophistication actually makes up-sell and cross-sell predictable based on forecasted customer traffic and types of interactions.

Increasing Market Share

Growing market share is the result of acquiring new customers and retaining existing customers at a pace higher than competitors.

Correlating big data from social networks, data aggregators and websites with existing customer profiles can provide the information to save customer relationships and lower churn. Consider these examples.

  • If an insurance company learns from social data or third party aggregate data (such as an expensive jewelry store purchase) that a customer was recently engaged to be married, or perhaps incurred a similar life stage event, being the first to offer an insurance policy is certain to increase the offer conversion rate.
  • If social or house-holding data reveals an auto policy customer has a child turning 16 soon, an offer aimed at adding additional drivers to the policy may be well received. Similar offers related to first time credit cards or college tuition savings accounts further increase customer share.
  • If an existing customer queried your website and checked out 401K plans, upon the next call center inquiry it would be relevant to end the service call with an offer to speak to a 401K specialist.

Each of these big data correlations does more than increase margins and revenues. By growing the number of products within the customer account or household the FSI is also increasing the likelihood of increased customer tenure.

And related to customer tenure, should you take note and initiate a proactive action when a customer calls for information with an outside financial consultant on the line; updates their address, removes personal information, prints their online statements for the first time, registers a power of attorney or browses your website for forms? Absolutely, as these are predictive behaviors of customer churn. Recognizing these behaviors, calculating at-risk customer scores and sending alerts to financial advisors or client account managers can reduce customer defections by over 45 percent.

Next: Big Data for Financial Services Marketing >>

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Recognizing predictive behaviors, calculating at-risk customer scores and sending alerts to financial advisors or client account managers can reduce customer churn by over 45%.


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