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5 Ways Data Analytics Improves Customer Experience and Increases Staff Productivity

July 25, 2021

Happy employees make happy customers. According to a Harvard Business Review article, even a slight improvement in employee satisfaction increases a company’s value by at least 7%. And, productive employees are happy employees. People want their efforts to have meaning. They want to contribute to the growth of an organization. One way for businesses to ensure ongoing productivity is through data reporting and metrics. Using predictive analytics, businesses can improve employee experience, which leads to a better customer experience.

What is Predictive Analytics?

Predictive analytics uses statistical algorithms to predict future outcomes. By analyzing historical and current data, models are created to forecast how likely a specific action or behavior will occur in the future. It encompasses models that search for the subtle relationships in the data to deliver actionable insights to give an organization a competitive edge. The models can be used in real-time to make more informed decisions.

Why Predictive Analytics Needs Big Data

Predictive modeling needs data — lots of data. Without data, statistical models are of little use. If the data is suspect, the results are called into question. To ensure data integrity, companies need to clean and prepare information for data analysis. That can be a massive undertaking as the volume of data continues to grow.

The latest estimates suggest that every person generates 1.7 million megabytes of data each second. Harnessing the power in that volume of data requires the data mining capabilities of Big Data. Predictive analytics is the only cost-effective way to produce timely results.

Here are 5 ways data reporting and metrics can improve customer experience and increase employee productivity.

1. Products and Pricing

Predictive algorithms let insurers adjust products and pricing to address customer expectations. Companies can create products that fit with prevailing market conditions and risk patterns. Using historical data such as costs, risks, and claims, predictive models can project behaviors into the future.

The customer data can be used to offer more relevant insurance products based on location, weather, and economic growth. Continually analyzing consumer behavior means insurers can customize policies to fit market needs. If the weather service is projecting a more active tornado season, insurance companies can tailor products to fit the market’s price points.

2. Insurance Claims 

The insurance industry still lags in its digital transformation; however, most insurers have automated some, if not all, of their claims processing. After all, claims are part of every insurance lifecycle. They are also prone to error. Automation, says McKinsey in a recent study, can reduce the cost of the claims journey by 30%.

Depending on the level of automation, some insurers may have employees entering data in multiple locations, which increases the chance of a data entry error. A simple error such as reversed digits of a zip code or incorrect data entries on underwriting insurance can delay a claim until the error is found and corrected. Setting key performance indicators (KPIs) during the claims processing enables analytics to look at where in the process errors occur so that improvements can be made.

3. Customer Risk and Fraud

Predictive analytics can look at historical data to determine relationships between customer patterns and potential fraud. Given that insurance fraud costs the industry as much as $40 billion per year, finding ways to identify potential risks directly impacts the bottom line. Whether it’s a stolen identity or false information, having a tool that flags unusual activity can save insurers millions.

4. Agent Fraud 

As many organizations have learned, the biggest threats come from inside. For the most part, consumers are not as knowledgeable as agents in how to steal funds from an insurer. The agent may attempt to embezzle funds through claim payment schemes, or they may manipulate an application to their advantage.

Using predictive analysis, organizations can identify patterns that suggest fraudulent activity. Humans may not see a repeated behavior if the agent deals with multiple individuals, but a predictive model can detect a pattern across the enterprise.  Stopping illegal activity before it becomes obvious avoids potential damage to an insurer’s reputation.

5. Customer Experience

When consumers can search the internet for insurance coverage, customer loyalty is hard to achieve. However, insurers with a customer-centered approach that addresses the public’s preference for digital solutions are more likely to retain their customers. The best way to keep customers is to ensure they have a positive customer experience.

Data analytics can provide insight into why customers abandon online shopping carts or how customer service is perceived. With up-to-date data, predictive models can detect changes in consumer behavior that might indicate dissatisfaction with a carrier. Armed with that information, insurers can proactively contact the customer.

OZ Intelligent Automation Consulting works with insurers to develop predictive models that help improve the customer experience and increase employee productivity. Whether it’s automating internal processes or personalizing customer interactions, OZ’s suite of accelerators, products, and services can help insurers move towards digital transformation.

 

Sources:

https://www.businessnewsdaily.com/6084-employee-happiness-without-raise.html

https://www.gartner.com/en/information-technology/glossary/predictive-modeling#

https://hbr.org/2019/08/the-key-to-happy-customers-happy-employees

 

 

 

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