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Performance Management 2.0: Tech-enabled optimization of field forces

Businesses with large field forces have more reasons than ever to boost effectiveness and efficiency. New technology options can help shatter old barriers to higher performance.

In recent years, companies that manage large field forces have made great strides in improving productivity, safety, and customer satisfaction. However, despite multiple initiatives, including implementing lean management and process improvements, significant improvement opportunities remain untapped.

The root of the problem lies with how companies manage and use the significant amount of data they already have. They typically collect and analyze data streams in silos–developed at different times, by different people, and for different purposes. As a result, the data made available to frontline managers lacks sufficient granularity to enable data-driven performance discussions with field workers.

By building and rolling out a technology platform that integrates multiple data feeds, companies can enable granular performance management. We call this tech-enabled optimization of the field force “Performance Management 2.0.” By combining a data foundation with an agile approach, a company can develop a tech platform that provides detailed insights. More than just a dashboard, this type of platform can serve as a single operating system that replaces the multiple systems used by companies today.

At the same time, a company needs supporting initiatives and a strong change-management process to put the insights into action. The potential upside includes a productivity improvement of more than 10 percent as well as significant enhancements to safety, quality, and customer service.

These improvements are especially vital for enabling companies to meet the spike in services demand in the aftermath of the COVID-19 crisis. Higher levels of productivity also allow technicians to minimize exposure to the virus during on-site visits. To capture these benefits, managers need to better understand which tasks take the longest for which technicians and who needs training on specific topics. For companies that are still experiencing low demand for services due to the crisis, the pause provides a unique window of opportunity to accelerate digital initiatives and transform the operating model.

The need for granular performance management

Efforts to improve field-force productivity, safety, quality, and customer service are limited by how much transparency is available to frontline managers on a day-to-day basis. The lack of transparency not only makes it hard to identify problems, but also limits visibility into best practices that would eliminate bottlenecks and help field workers improve their performance and meet targets.

For example, high-level metrics, such as number of jobs per day or number of hours per job, tend to give managers only directional views on performance, rather than pinpointing specific productivity-improvement opportunities. Similarly, an aggregated safety scorecard for each technician provide little insight into the factors that may have led to an unsafe environment (such as speeding to arrive at a job on time). Shortcomings such as these prevent managers from supporting field workers with customized, practical guidance on improvement opportunities—for example, how to complete a job in a single site visit, or how to use GPS-powered apps to find the fastest route to a site.

Breaking data siloes

The problems arise because companies typically take a siloed approach to reviewing data. For example, frontline managers check overtime data and dispatch information, while the dispatch group reviews payroll data, and the fleet group reviews the GPS data. The siloed systems make it hard for managers to stitch together data from different sources to form a complete view of the root causes of performance issues. Frontline managers may require hours or even days to conduct a simple root-cause investigation. Consequently, they often have very little specific feedback to give their technicians when performance issues occur—except when they have directly observed the technician during a field visit.

A case in point is performance management at a Fortune 500 company with more than 1,000 field-service technicians who install and repair equipment in customers’ homes. The times for completing jobs were high, but managers had low visibility into the root causes. To perform an investigation, they needed to work across five or six different IT systems—a task that required half a day or more.

Acting on data insights

The solution is to give frontline managers easier access to information about specific productivity- and safety-improvement opportunities, enabling them to provide the field force with more granular coaching. Conversations between a manager and technician can focus on the root causes of issues and the specific actions to improve performance, rather than just reviewing high-level metrics that may not relate well to workers’ day-to-day activities.

To make it happen, companies can improve how they leverage the significant amounts of data they already collect. Companies are sitting on a trove of data—GPS location services, workforce-safety reports, timecards, equipment activation records, customer communications, and warehouse management. By integrating these time-series data together, companies can gain significant insights. The benefits range from answering basic questions, such as when the technician reached a customer site, to gathering information about specific safety or quality incidents, and communicating to the customer prior to arrival (Exhibit 1).

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Enhanced performance management generates significant value. Companies can capture productivity improvements of about 10 to 20 percent by reducing discretionary time, improving job efficiency, and reducing drive time. They can also improve customer service, both by improved communication to the customer prior to arrival and by closer adherence to on-time arrival at job sites. Additionally, they gain more visibility into safety incidents—such as speeding, tickets, falls, or sudden stops and acceleration of vehicles—and potential best practices for improving safety. Other impact areas include improved quality, higher employee morale, and better inventory management.

A flexible, responsive approach to implementation

To build a technology platform that brings together multiple data streams, companies can adopt a user-centric approach.

Set up a performance infrastructure. The first step is to identify what data is needed versus what is currently available, assessing where real-time, live data feeds are required instead of batch feeds. The types of interfaces needed to access the data will require consideration as well, along with measures supporting consistent data quality.

Analyze the opportunities. In identifying the most valuable improvement opportunities, companies can start by focusing on a few common themes, such as improving drive-time efficiency, increasing the use of multiple stops, or shortening the time required to close out a job. Bottom-up analysis, based on interviewing managers and observing technicians at job sites, helps focus efforts on the most important problems and improves the likelihood of successful implementation.

Define priority features. Starting with a hypothesis of the top features needed for the platform gives this stage of the process more structure. A user-centric approach will emphasize workshops with frontline managers to get their feedback and identify the list of features to prioritize. The end result is a one-year roadmap that details which features will be covered in each quarterly release of the platform, creating a steady cadence of platform development and rollout.

Build the minimum viable product (MVP). The team building the initial, MVP iteration of the platform should ideally include a data expert, an enterprise architect, a product owner, and a business lead, as well as a subteam responsible for development and testing. Including these profiles makes it easier for the organization to align the business and the IT function on how to develop the MVP and deploy it in the field.

Their work will be easier if they have complete access to high-quality data, which means paying attention to details such as the data’s reliability (Are the GPS coordinates accurate?) and accessibility (How machine-readable are our sources? Can we stream the data in real time?). Companies operating multiple legacy systems face the additional question of whether they can leverage their existing infrastructure, without major rewiring of IT systems.

Under a user-centric approach to building the MVP, the team may develop early mockups and conduct workshops with field managers to get feedback on each iteration. Their perspective can help teams avoid the inclination to add more and more features, as it’s the field managers who will use the system every day. Their needs will matter more to the system’s success than building in nice-to-have add-ons for the head of the field force or the area manager.

An easily understandable interface for frontline managers will often prove critical to the MVP’s acceptance. An individual-level dashboard provides managers with a granular view into a “day in the life” of a specific worker (Exhibit 2). A team-level dashboard gives frontline managers a quick view of which members of the field force to focus on for coaching and training (Exhibit 3).

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Pilot the MVP. Rolling out the MVP to a select few sites allows the development team to gather initial reactions for additional iteration on the user interface and functionality. To enable effective pilots, organizations can launch supporting field initiatives that leverage the platform to improve operational excellence over a three- to six-month period. Good results build positive word-of-mouth that can help in building scale across the enterprise.

For example, an initiative to improve job-execution time focuses on freeing up slices of time based on analyses of arrival and departure times from job sites, equipment activation times, and support center call times. It also would likely roll out skill mapping and upskilling to ensure supply flexibility. Through such initiatives, managers can use the platform to pinpoint opportunities to provide tailored support to field workers, rather than simply monitor their performance.

As part of the foundation for these pilot initiatives, managers can consider standardizing the operating model at each site. This entails ensuring compliance with basic guidelines, such as timecard entries, checking out vans every morning, and keeping the fleet GPS switched on. The standardized operating model provides a solid foundation that enables accurate and consistent data collection from the field.

Scale up. A carefully phased plan allows an enterprise-wide rollout to proceed over the course of three to four months. Successful deployment at scale depends in part on basic technology hygiene measures, such as strengthening the platform with appropriate privacy and access-control features. Stress tests help ensure that the system is resilient. At the same time, developers can start adding further tools to provide ready insights to frontline managers as well as senior management. As with pilots, strong initiatives to drive improvement can encourage use of the platform and help managers see that the benefits are worth the effort.

Change management provides the essential foundation for capturing performance improvement, starting with a compelling story to communicate the overall objectives to the organization, as well as the vision, goals, and milestones. The platform will likely require people to learn new skills, which capability-building programs can provide. To establish reinforcement mechanisms, companies can build new structures and processes designed so that people know what is expected of them day to day, and what the consequences will be for not meeting a commitment. Finally, the senior team’s role modeling of the new practices and behaviors will likely have the deepest impact in underscoring the importance of the transformation and the organization’s alignment on the new approach.

The Fortune 500 company referred to above developed a tech-enabled platform to give its frontline managers easy access to performance data and facilitate granular performance management. The company integrated data from multiple sources, matching time stamps and visually laying out the activities for each day. The platform provides comprehensive metrics by technician, peer group, and work site, creating a single operating system that eliminates the need for managers to navigate multiple systems that previously wasted time and effort. Metrics are summarized to provides digestible insights. To drive the rollout, company defined specific initiatives that leverage the platform, building on previous efforts around lean and process improvements. It has launched pilots at multiple sites and plans to eventually scale up the platform across its network.

The transition to the platform is expected to generate substantial impact for the company, helping sustain the 10 percent productivity increase that lean and process improvements had already achieved, while also driving an additional 5 to 10 percent productivity improvement for technicians. The company is on target to capture additional savings both in fleet usage, relating to reduced fuel consumption and avoided capex, and in equipment usage. Better on-time arrival rates and enhanced communication with customers before arrival will likely mean higher customer satisfaction, while personalized coaching and targeted skill-building provide new opportunities for boosting employee morale. And less driving, combined with faster escalation of safety issues, leaves fewer opportunities for safety problems.

A call-center operation provides an additional example of how these changes can improve results. A global services provider used a combination of technologies to develop an integrated platform that enabled granular performance management and coaching. The system used data from multiple sources, including from its customer relationship management system, its call routing records, and its interactive voice response system. By calculating performance metrics for each agent and presenting the results to team leaders, the platform highlighted the actions of concern and recommended specific coaching actions to improve performance. This resulted in a 15 percent increase in productivity.


To enable data-driven performance management of their field force, most companies do not need more data. In many cases, they are already sitting on all the data they need. What’s required instead is greater accessibility and visibility into this data and the analytical horsepower to derive actionable insights. A technology platform that integrates multiple data feeds provides a unified operating system that enables companies to capture the potential of previously untapped data assets. By deploying this new operating system at scale, companies can achieve step-change improvements in productivity, safety, quality, and customer service. These benefits will be especially valuable for companies as they seek to manage the spike in demand following the COVID-19 crisis while minimizing risks to field workers on site.

About the author(s)

Guy Benjamin is an associate partner in McKinsey’s New York office and Harrison Lung is a partner in the New Jersey office, where Raghu Murali is a consultant.

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