Auto insurance company reduces overstaffing by 20%

Use Case

Demand forecasting

A North American auto insurance company with more than 2 million customers needed to improve the customer experience while reducing operational costs. They wanted a faster, automated solution to optimize customer request forecasting and staffing. Using Pecan, the team rapidly generates, implements, monitors, and refines dozens of models that predict customer call volume over short- and long-term intervals, as well as for specific regions and different types of service. Pecan’s predictions are used in their day-to-day operations to better allocate resources and build staff schedules.

Industry: Insurance

Company Size: Over 2 million customers and about $150M in revenue

Solution: Forecast customer requests to optimize resource allocation and staffing

Platform Use Case: Demand forecasting

Data Stack: Microsoft SQL Server

20%

reduction in support overstaffing

10%

reduction in customer wait time

2 week

and 1 year forecasts

What Our Customer Says About Pecan

The predictions were spot on … and that’s what our customers need at such a critical time. The business is extremely happy with the performance and results.
Anonymous
Data Analyst
The platform is very simple and easy to use. To experiment with models, all I have to do is check or uncheck specific variables I want to include in the modeling process. If I need to bring in more data and build more datasets and connections, I can do that easily.
Anonymous
Data Analyst

Challenge

Manual forecasting affected customer service quality and resource allocation

Before adopting Pecan, one team member created forecasts manually for customer request call volumes. Each set of forecasts required a week of work and, as a whole, only informed planning for only about 60% of the call volume. The team also wanted to reduce overstaffing and other operational costs.

The customer hoped to find a scalable solution that would optimize resource allocation across all its locations to improve service everywhere for its 2M customers.

It was essential to find a partner that offered a flexible yet automated approach to ingest hundreds of millions of rows of data from various data sources, refine models easily based on evolving combinations of variables, and implement top-performing models seamlessly into their workflows.

Solution

Predict customer assistance requests in advance

With Pecan, the team developed and implemented over two dozen models into production that generate short- and long-term forecasts. The short-term forecasts predict customer request volume and service type for 3-hour periods within 2 weeks. These forecasts are specific to hundreds of micro-regions in this company’s service area. These highly granular predictions ensure that staff and resources are correctly placed to best meet customer needs.

Additionally, the team retrains and runs another model quarterly to generate predictions for customer request volume a year in advance.

Pecan empowers the data science team with easy experimentation and data enrichment

The team has continuously experimented with and improved upon the predictive models using Pecan, creating hundreds of models over time — a fast and nearly effortless process with the platform. The team has also enriched their in-house data with external data, such as weather information, which has improved the models’ performance.

Equipped with these forecasts, the company uses business logic to translate predicted call volume into optimized staff and resource allocation.

Results

Improved service boosts customer satisfaction and retention

A customer needing assistance now receives quicker service and more straightforward resolution, thanks to Pecan’s forecasts and the data science team’s integration of predictions into their business systems. Of course, they won’t know that Pecan’s forecasts played a role in their service, but they’ll be more likely to maintain their coverage with this company — and to spread the word about their positive experiences.

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