Inside an Auto Chain’s Pursuit of Modern Data Management

By any definition, Monro Inc. is a success. Since 1962, the Rochester, NY-based auto service and tire company has grown steadily, reaching more than 1,300 locations and representing 10 brands across much of the U.S. And it’s not done yet. With a strategy that strives to put customers first, Monro aims to push the boundaries of customer service through technology transformation.

Data modernization has stood at the heart of the publicly traded company’s technology transformation journey. Since its earliest efforts, Monro looked to overcome barriers impeding greater growth and customer service. The overall goal for transforming data management practices was to easily pull data from all service locations and use that data to support stores’ operations.

“It’s about having the information you need to make quick decisions to win every day,” said Cindy Donovan, the company’s senior vice president of IT. “Information is key to being able to quickly course-correct in every area – pricing decisions and staffing at low-performing stores, for example.”

Yet Monro was struggling to meet those objectives due to a variety of challenges. Among those challenges, the company lacked the range of data it wanted. The company also needed to make the data available for different user needs, lower the cost of data management, and overcome performance limitations of existing technology. In addition, Monro struggled to shorten how long it took to implement a new key performance indicator (KPI), understand data lineage, and achieve valuable analytics. It could take up to three months to create new dashboards, for example.

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Furthermore, data extraction, transformation and loading (ETL) processes weren’t executed in a consistent design manner, creating major support issues. Finally, there lacked a standard for how dashboards were defined. Every department consumed data slightly differently, creating multiple versions of the truth for the same KPIs.

Many of the issues stemmed from antiquated back-office transactional and point of sale (POS) technologies. Since every retail store had its own local database, a customer who visits more than one location could be recognized as different customers with different service histories.

To overcome these challenges, Monro’s IT team would need to create a data lake, enrich master and transactional data, and replace aging or inadequate technology.

Out With the Old

As the first step toward data management transformation, Monro’s IT team evaluated its current technology and determined which systems to replace.

At the time, transactions occurred through the VAST Retail POS system running through an IBM AS/400, which pushed the transaction into a structured PostgreSQL database. ETL functions were handled by Informatica. The data eventually ended up in a Yellowfin business intelligence system. One of the problems was that Postgres wasn’t built to support business intelligence functionality, noted Monro senior director of IT Ram Sunkara.

It was time to make some real changes. With support from business intelligence and analytics consultant Cause+Effect Strategy, the Monro IT team added Sisense API-based data analytics and visualization software, essentially to replace Yellowfin. The main reason for this move, Sunkara said, was that Monro’s CEO at the time wanted to have reporting dashboards in PDF format available to every store, district manager, and other leader each morning.

The team then moved everything from the PostgreSQL database into an AWS Snowflake cloud data warehouse, with storage replaced by an AWS S3 data lake. At the same time, the team replaced ETL tool Informatica with Talend and implemented the Kong API management platform for the real-time interactions the team has with external systems, such as the RingCentral phone system and DialogTech for customer call recordings.

As the data management transformation project progressed, the team began to focus more and more on integrating tools to give users faster access to data, regardless of the data source.

Burning Rubber

With new systems in place and everything connected to the data lake, Monro achieved a flow of real-time data between stores and websites to enable online appointments; real-time call analytics to understand call volumes; and up-to-date information to improve store personnel scheduling, time tracking, tire pricing, and tire sales forecasting.

In addition to better data visibility and the ability to perform “what if” analyses in real time, Monro saw significant cost and time savings. Data acquisition is about 50% faster, enhancements to reports and dashboards are also about 50% faster, and technology costs have dropped by about half.

These advances and insights have also directly improved decision-making processes and, ultimately, customer service. For example, the company can now correlate customer phone call data, walk-in traffic, and online appointments with sales.

“The appointments requested at our stores weren’t based on the real-time availability [of appointments] at those stores,” Sunkara said. “Now they are based on availability before appointments are confirmed, so we can guarantee that no guest will be turned away because of availability.”

Another project in the works involves enriching master data by consolidating the customer service history databases of every Monroe store. By combining that data, the company will know exactly who customers are, what stores they have visited, the service history of their vehicles, and which groups of customers constitute a household. This will make big differences in areas such as warranty coverage. Instead of a store manager having to call a helpdesk to get the service history of a customer from a different store, the store manager can simply pull up the consolidated service history and confirm warranty coverage.

Over time, Donovan and Sunkara said they aim to achieve further goals, including the following: