From Metrics to Momentum

Pest management companies are collecting an enormous amount of data every day—from financial reports, service records, call-center logs, customer feedback, sensor data, and more. The challenge is deciding which metrics will lead to insights that suggest meaningful action.  

“Perhaps the biggest [data] challenge is knowing how to prioritize your next step based on this data that you have,” says Cindy Shupe, senior director of business intelligence for Ogden, Utah-based Hawx Smart Pest Control. “You need to be able to look at the data and use it within the context of your business and your industry.” 

The companies that get the most value from their data start by clarifying what questions they need that data to answer and pinpointing the information that reveals trends, flags risks, and points the way to next steps that improve operations, serve customers, and support growth. 

“Focus on the big picture and on what is actionable,” Shupe advises. “Ask, ‘What can I measure or what data can I collect that’s going to result in an action I can take?’ So, start with a specific business question, then measure it, and then move on to the next thing. Be adaptable and curious: ‘Why am I seeing this result?’ or ‘How can I keep this trend going—or reverse it?’ ”  

Finding Your Focus 

Jay Swann, owner of Appalachian Pest Control in Barboursville, West Virginia, focuses first on financials. “I look mostly at collections: who’s outstanding, who’s past due, and how far past due,” he says. “I also look at those customers we have skipped for some reason to see why, and I look at cancels to figure out why somebody cancels their service. I’m looking for any red flags, focusing on trends. I want to be able to address problems as soon as possible and look at where we may be headed in the future, so I can forecast more effectively.” 

On a larger scale, Hawx Smart Pest Control focuses on answering the questions directly tied to growth, profitability, and retention. “This helps us to limit distractions and weed out any metrics that might look good but don’t really tell you at an operational level what to do next,” says Shupe. 

Taking an even bigger-picture view, Mary Blanks, global vice president of marketing and business transformation for St. Paul, Minnesota-based Ecolab, reports that the company keys in on the data that best helps clients and their operations.  

“Our job is to keep them online and for pests to never become an issue,” Blanks says. “We’re asking: Where are our customers most vulnerable? Where are we seeing those earliest signs of risk?  

“Answering these questions helps us know how best to intervene before a high-impact issue occurs,” she adds. “We look at where pest pressure may be increasing, what species are coming in, and where there are hot spots.” 

It takes a large amount of data from a diverse range of sources to accurately answer those questions, and it typically requires a variety of software and human solutions to analyze that data to the point where it can effectively inform decision-making.  

Hawx relies on a field service management system to capture core customer life cycle and service history, complemented with call-center data, employee data, and metrics from marketing, sales, and service. Because Hawx does not expire its data, customer service and sales data go back to when the company began selling to customers in 2013. 

“We have built up a very complex, interconnected system for managing data,” Shupe says. “It’s definitely something that requires strong governance. For example, it’s very important that all offices are inputting data in a standard format, so that when you’re going from one system to another, you know exactly how to work with it and what information you’ll find.” 

Managing the Flood 

Pest management companies are collecting more data than ever across a range of sources, including field-​service management applications, digital service reports generated post-visit, geographic information system mapping and facility mapping, and on-site internet-connected devices. Managing this flood of data, regardless of how multitiered or straightforward your data systems may be, requires reliable partners, technology, and, above all else, human oversight. 

Swann notes that Appalachian Pest Control has about 12 years of historical data on hand, which is valuable for forecasting sales, production, and completion rates. He relies on third-party cloud-based providers to manage much of that data. However, that arrangement has led to some frustration, with identical report requests yielding inconsistent—and therefore unreliable—results.  

Swann also has lost data when switching cloud providers, since each one uses proprietary data storage systems that are often incapable of exporting that data into other formats. As a result, though a lot of its data comes from the POS system, Swann says the company also continues to manage some data manually in Excel spreadsheets.  

“The best data-management advice I could offer other NPMA members would be to print hard copies of everything you think you will ever need,” Swann says. “Because you’re always going to be beholden to that cloud company, whoever that is. And if you ever leave there without hard copies, you’re at their mercy. If I said, ‘I would like a PDF of every invoice we’ve ever done,’ some of these companies have no way to produce that.”  

With both digital and hard-copy data streams to learn from, human review remains an important part of Swann’s analytic process. New data gets reviewed about four times a month, and his staff members meet twice a week to stay abreast of anything urgent. 

“Meeting that frequently allows me to make some split-second decisions as to where we can tweak a system or a process to see if it moves the needle any,” says Swann.  

For many companies, however, the sheer volume of information flooding in means that artificial intelligence (AI) assistance for analysis, pattern recognition, and recommendations is essential. 

At Ecolab, whose digital service data goes back a decade, representing tens of thousands of customers globally, AI integration is a critical part of its processes.  

“We are looking at millions and millions of rows of data,” Blanks points out, “and then using AI and [proprietary] large-language models to help sort through all of that information.” 

The company is also exploring ways to broaden the scope of its AI analysis, making it even smarter. “We’re looking at other data we can bring into our AI model from third-party sources—health department data, weather data, construction data, things we know can create activity conducive for pests,” Blanks says. “That will allow us to create a broader picture of additional environmental situations that could lead to an issue.” 

At Hawx, Schupe says data analysis is both a continual, ongoing process aided by AI and a team-driven activity involving generating and reviewing reports. On the AI side, Hawx uses a cloud-based data warehouse, which serves as the company’s “single source of truth.” Schupe says the system makes the data easily accessible to everyone, eliminating data silos and allowing Hawx to create a holistic approach to reporting that highlights the complex patterns the company expects the data to reveal. 

Shupe believes that one of the most powerful tools any data team can implement is cohort analysis. “We monitor the entire customer journey by consistently tracking customers grouped by their shared starting point—generally the date their service started—and similar characteristics,” she says.  

Hawx believes this allows the company to gain unique insights into customer behavior, allowing for strategic agility. “We can make swift, deliberate adjustments and generate precise forecasts,” Shupe says. 

Growing Forward 

Regardless of the size and scope of your operation—whether you’re riding the cutting-edge of AI technology or relying solely on reports and team analysis—it is possible to turn your data into actions that benefit your clients and your business.  

Blanks recommends starting with processes. “Make sure you have a way to trust the source of the data you’re using,” she says. “Make sure your teams have a way of documenting and sharing what they’re seeing so that everyone is using the same language and the same measures. Focus on the trends, not one-off events.” 

Finally, it is important to remember why you are collecting all of this information in the first place.  

“You’re not collecting it for the sake of record keeping,” Blanks says. “It’s not about just being ready for the next audit. Think of your data as a decision-coaching system. The data should very clearly signal what needs to happen next and where intervention is required.”