AWS Business Intelligence Blog

Traeger Grills’s Customer Experience team drives customer satisfaction significantly using Amazon QuickSight

This is a guest post authored by Traeger Grills.

Traeger Grills invented the Original Wood-Fired Grill over 30 years ago in Mt. Angel, Oregon. We make it easy for home cooks to create delicious wood-fire flavored meals, and have enhanced our grill products by selling a variety of merchandise, from wood pellets and accessories to meals, seasonings, and T-shirts.

For retail brands, top-notch customer service can be a big driver of loyalty—and recurring revenue. However, it’s a costly, resource-intensive function, and managers must drive efficiency and satisfaction goals. That requires a lot of data and the ability to use it to create and measure action plans.

In addition to creating brand love, customer service can be a wellspring of insight to guide impactful product and marketing decisions. Success in mining insights from a large mass of data also requires great processes and tools.

Traeger Grills’s Customer Experience team is responsible for order management, customer service, and technical support for customers and commercial partners like The Home Depot, ACE Hardware, and Amazon.

In this post, we discuss how Traeger Grills’s Customer Experience team uses Amazon QuickSight for data insights, helping drive customer satisfaction score from approximately 72% to 93%, and improved our first call resolution number from about 40% to 75%.

Prohibitive costs and time-to-value with legacy business intelligence tools

Previously, we were only getting self-reported data from the business process outsourcer (BPO) that manages our call center. We wanted to own our own data and reporting so we could drive improvements as well as get better insight into things like order forecasting.

In addition, we wanted to help over 500 call center agents see how they were measuring up to key performance indicators (KPIs) and where they needed to improve.

We approached our business intelligence (BI) team with these requests—and they were shocked by the scale of our use cases and number of people we wanted to equip with data access.

At the time, we were using another enterprise data visualization tool. Our BI team investigated and balked at the astronomical costs for licensing all 500 agents. They suggested they create some Excel-based reports and we could download them and send them to call centers daily.

Those reports would take months to build, and they wouldn’t accomplish our goals.

So we took matters into our own hands. We stood up Amazon Connect, an omnichannel cloud contact center, and added QuickSight—in a matter of days.

We started with the usual KPIs for establishing a baseline and understanding performance, including customer satisfaction, first call resolution, average handle time, and warranty spend reporting. We then built on that to create more actionable and strategic insights and as well as day-to-day reporting for outlier management.

How we did it: Self-serving data insights with ease

We knew getting internal IT and BI resources would be difficult with the prioritization against all areas of our business, so we needed a product we could stand up without help or reliance on other resources. Once we decided to use Amazon Connect to manage agents’ work, QuickSight was the clear winner. The two products work together seamlessly without scaling issues, and at minimal cost. Critically, because our timeline necessitated building a solution as quickly as possible, they could be deployed without internal developers.

Deployment was quick and painless. Our entire data infrastructure, including Amazon Redshift, Amazon DynamoDB, and Amazon Simple Storage Service (Amazon S3), already exists in AWS. This made creating datasets, analyses, and dashboards effortless. We began automating reports and deriving insights that supported decisions across our organization.

As an example, we use Amazon Forecast to generate order forecasts for our global network of warehouse partners—this gives warehouses a heads-up when ordering is about to spike, not just with seasonality, but down to days of the week.

The following diagram illustrates our solution architecture. QuickSight pulls data from Amazon Redshift, raw JSON payloads in Amazon S3 (using Amazon Athena), DynamoDB, APIs like the Amazon Connect GetCurrentMetricData, and CSV files.

Democratizing data has created big wins for us

Knowledge is power. Using QuickSight helped us move our customer satisfaction score from approximately 72% to 93%, and improved our first call resolution number from about 40% to 75%—saving us a lot of time and money. Our team members have the insights they need to dig deep into areas of opportunity, and we can measure the effectiveness of action plans. We stood up Amazon Connect and then QuickSight in a matter of days and, for the first time, had full control of our own data and reporting.

We also provide the data to our partners, like our third-party warehouses, so they can plan staffing to meet 1-day service-level agreements. Before this, our warehouses didn’t have insight into expected order volume and were often overwhelmed. This helps us please our customers, and also reduces costs because customers aren’t calling to find out where their order is.

The Amazon Connect real-time analytics dashboard shown in the following screenshot captures our real-time reporting. It shows what skills we have and how many active contacts are in progress for each skill. It also shows agent AUX states and performance in past intervals for ASA (Average Speed of Answer).

We are also using Amazon Q in QuickSight for our product information and are testing Amazon Q generative AI right now.

What’s next

Using QuickSight allowed us to completely transform our customer experience data operation within a matter of days, giving us tools to drive large improvements in efficiency and satisfaction.

Using the data, we created action plans around specific goals, like increasing customer satisfaction and making sure most customers got what they needed on the first call. We were able to measure success so we could double down on what worked and adjust what didn’t.

Sharing each service agent’s data with them was key—when they could see how they measured up to KPIs, they knew what to work on and were more motivated to do so. Their team leads also had this information and could manage accordingly.

Finally, we made our data available to third-party logistics partners, sharing order forecasting that helped them understand when they would need to add more staff to meet spikes in demand. This helped them get our products shipped out to customers sooner—saving us from having to talk to frustrated customers calling to check on the status of their orders, as well as driving an 81% reduction in expedited shipping costs.

The result? We were able to reduce headcount while making improvements in customer satisfaction and sales. In the next year, we’re expanding the QuickSight footprint across our enterprise.

Get started with QuickSight

QuickSight allows organizations to take control of their data. It’s a powerful tool for querying databases, and it’s straightforward to get set up. The intuitive visualizations and usage-based pricing model of QuickSight allows organizations to democratize data access to everyone in the organization who will benefit.

If you’re looking to gain a self-service BI tool for creating and sharing BI across your organization, visit Amazon QuickSight.

About the Author

Corey L. Savory-Venzke is a Customer Service and Customer Experience leader at the head of end-consumer and dealer/retail customer service for Traeger Pellet Grills and their MEATER brand by Apption Labs. Corey has driven significant cost reductions while driving improvement in customer satisfaction and service KPIs to best-in-class performance levels. Corey is originally from the Boston area and will always consider herself to be a New Englander and die-hard New England sports fan. She currently resides in Salt Lake City.