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Sisense analytics tools spur growth at Hastings Mutual

The insurance company is using the BI vendor's tools to improve data-driven decision-making efficiency and identify sales opportunities leading to revenue growth.

In just over a year since first starting to use the Sisense analytics platform, Hastings Mutual Insurance Company has improved efficiencies, identified new sales opportunities and increased revenues.

Hastings Mutual, founded in 1885 and based in Hastings, Mich., is a property and casualty insurance company that operates in the Midwestern states of Illinois, Indiana, Iowa, Michigan, Ohio and Wisconsin.

Sisense, meanwhile, is an analytics vendor founded in 2004 and based in New York, offering a platform that serves both trained data scientists and self-service business users.

Rising demand for data

By 2020, more and more Hastings Mutual employees wanted to use data to inform their decisions, whether they were part of the corporate office or agents out in the field working for affiliated agencies.

Hastings Mutual had begun building its data warehouse about seven years earlier, and the data had finally reached a level at which it was trusted by the data consumers and could be informative, according to Randy Sykes, IT director of data services at Hastings Mutual.

We were experiencing quite a backlog for the business to be able to look at the data we were collecting. The data was at a level where people could use it, they trusted the warehouse, and there was an explosion of people wanting to get at the data.
Randy SykesIT director of data services, Hastings Mutual Insurance Company

Hastings Mutual was using Pentaho for its business intelligence needs at the time, but using the platform required a level of technical knowledge most of those suddenly clamoring for data didn't possess, Sykes said.

It was a strong platform for those with Sykes' expertise who were the ones building dashboards and other data assets, and Hastings Mutual still uses Pentaho for its extract, load and transform needs. But if it was to enable self-service analytics, the company needed a platform that employees without backgrounds in coding, statistics and data science could use.

In addition, without self-service capabilities, those suddenly clamoring for data had to essentially wait in line for a small centralized data team to fill requests. By the time they finally got their hands on data that could inform their decisions, the data was 30 to 45 days old, according to Sykes.

"We were experiencing quite a backlog for the business to be able to look at the data we were collecting," he said. "The data was at a level where people could use it, they trusted the warehouse, and there was an explosion of people wanting to get at the data. Pentaho was designed for people with a technical background, and so we started looking for a tool that was much easier to use."

Choosing Sisense

When looking for a new analytics platform, Hastings Mutual tried out Qlik and Tableau in addition to Sisense. Ultimately, Sisense's data modeling capabilities made it the right analytics platform for the insurance company.

"One of the features Sisense had that the others did not have quite as much of was the ability for us to build data models that people could then pull their information from," Sykes said. "Rather than people having to be familiar with the raw underlying data, Sisense allowed us to build models that people could go in against, as opposed to having to learn all the technical details of the information."

Screenshot of a sample Sisense dashboard
A sample dashboard from Sisense displays an organization's revenue data.

Hastings Mutual deployed Sisense in October 2020 and since then has enabled its employees to work with data in a variety of ways.

Some data still gets parsed out via Hastings Mutual's centralized team. But Sisense has strong embedded analytics capabilities, so Sykes and his team now embed reports and dashboards in its agency website so that agents have data directly in their workflows.

In addition, for those who have some familiarity with analytics through working with reports and spreadsheets, Sisense is easy enough to use that some Hastings Mutual employees are taking advantage of the analytics platform's self-service capabilities.

When Hastings Mutual first began using Sisense, it had seven business users. Now, it has 20 designers and more than 100 people using about 200 dashboards that have been created and are automatically updated nightly, according to Sykes.

"My team is still responsible for collecting the data and making sure it's been conformed properly," he said. "But we wanted to make sure we got the data in the hands of our decision-makers."


Data that Hastings Mutual collects daily includes system-of-record data, which is policy, claims, accounting and financial data. In addition, each day it collects weather and climate data from the National Oceanic and Atmospheric Administration, weather data from Iowa State University and census data.

Weather data is important for insurance companies because weather risk insurance often covers unforeseen events, and by understanding weather patterns and trends, insurance companies can mitigate their own risk.

Meanwhile, one of the advantages of Sisense is that Hastings Mutual is able to combine capabilities developed on its own with those provided by Sisense to customize analytics assets such as geospatial maps. Hastings Mutual can layer in data such as weather information, census information, and information on a per-county or per-agency basis.

Ultimately, in about 16 months, the Sisense analytics platform has enabled Hastings Mutual to identify $50 million in upsell opportunities and increase revenues by 2%, according to the company.

"We talk about upsell opportunities, but what we're actually doing is presenting to our agents a list of policies that opportunities exist on, so it helps the agent do better business," Sykes said. "Our business was basically strangled [before adopting Sisense]. They didn't have enough information coming to them."

He added that conditions change quickly, and without current data, the decisions being made are not well informed.

"We really have to be able to provide information about what's going on with our business very fast," Sykes said. "We can't wait 30 days for a mainframe. The biggest thing is just being able to show our people the information they're looking for, and Sisense has helped us to do that in a faster fashion. Data is great, but if you can't get at it, it doesn't do you any good."

Future plans

Beyond data modeling, embedded analytics and self-service BI, Hastings Mutual plans to expand how it uses Sisense, according to Sykes.

It's not currently taking advantage of Sisense's natural language processing capabilities, but in an effort to enable more nontechnical users, it plans to implement natural language query features. In addition, Hastings Mutual plans to use Sisense BloX, a set of prebuilt templates that enable users to create custom applications.

"Right now, we're letting our customers digest what we've already presented them," Sykes said. "We see a lot of opportunities. It's just a matter of which one is the most important to start with."

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