The potential for increasing productivity in customer-facing services has been transformed in the past decade by user-centered design, data science, modern digital-build techniques, and enterprise-level agility. These capabilities have fueled the tech industry and radically changed retail, financial services, and other industries. However, the success rate among government organizations adopting these practices has been uneven. While these organizations face a unique set of challenges, the high-performing ones manage to achieve significant productivity gains by deploying the four critical practices outlined in this article.
The challenges government organizations encounter in executing customer-centric transformations are, to some degree, like those of any large organization. They include dismantling silos, getting management and staff buy-in, funding investments, keeping business as usual running, upgrading technology, and competing for talent with the necessary skills, to name just a few.
However, public-sector organizations contend with additional difficulties. Most importantly, they must frequently reassess their priorities in response to policy decisions and leadership changes. They also need to navigate fragmented control with multiple stakeholders, a highly regulated data environment, complex processes of raising capital for improvements, and additional oversight. And they often struggle to attract and retain the digital talent needed and to upgrade legacy systems, complicating their efforts to modernize.1
Meanwhile, the pressure is increasing on governments to deal with urgent social and economic needs while controlling spending. What is more, as the private sector keeps raising the bar on great customer experience, citizens’ expectations of government rise too.2 The prize for upgrading is significant: according to the OECD, the most digitally advanced organizations are anywhere from 7 to 15 percent more productive than their industry peers.3
How can government organizations overcome these challenges and increase their productivity?
What it takes to improve
Our research indicates that the successful ones focus on four critical practices (exhibit). Organizations that think like their customers, use data to its full potential, respond quickly to citizens’ needs, and define clear outcomes with accountability for results can achieve a significant improvement in performance (see sidebar, “Getting started”).
Think like your customers
A truly customer-centric government organization builds its strategy around customer needs as a source of value. This means learning about the customers—in this case, citizens using the services in question—and then using the insights gained to identify the customer journeys that drive largest share of value.4 That, in turn, helps prioritize the entire work program to improve the customer experience. Customer focus should also influence the way an agency is organized by removing silos, reforming governance, introducing new objectives and key performance indicators, and so on.
Design thinking can help public-sector organizations see the world as their customers do. For government, it is also a powerful tool for making services more inclusive.5 At its simplest, design thinking is about learning deeply from users by spending time with them, in their context—shadowing them, interviewing them, and trying out the service first-hand—and then improving the service by working with customers to test and iterate solutions that work in the real world. Design thinking assumes that customer needs change over time, so it is a process that never stops. There is no “done.”
We know true customer centricity can drive immense benefits in the private sector. Our 2018 study of 300 private-sector organizations showed that those scoring highest in design thinking increased their revenues and total returns to shareholders substantially faster than their industry counterparts did over a five-year period—by 32 and 56 percentage points, respectively.6 Comparable data does not exist for design thinking in government agencies, but case studies indicate the effect would be similar.
One North American government agency used design thinking to understand the experience and expectations of the millions of customers it serves. Customer-satisfaction surveys revealed that half of the users found navigating the agency’s services a fractured, frustrating experience. If, for example, customers needed to apply for more than one service, they had to go to different websites and fill out often redundant forms. And because each service collected its own data and did not share it, the agency could not present customers with a personalized view of the services they used.
The agency’s digital team researched several thousand customers to understand how they wanted to interact with the agency. They used these insights to inform the agency’s aspirations and priorities and plan how to rebuild the services around redefined customer journeys. The overarching goal was to make accessing the service intuitive, providing one place where customers could access the information and transactions they needed in a personalized, user-friendly way.
The team prioritized redesigning the journeys that customers valued most, conducting hundreds of sessions with end users to test concepts, iterate designs, and refine language. In the process, they uncovered—and removed—silos that prevented the agency from sharing data and coordinating delivery. By rebuilding its digitally delivered services around customer needs, the agency boosted customer satisfaction by 25 percent.
Use your data to its full potential
High-performing government agencies think about customer, financial, and operational data as a product that delivers value to the organization and its customers. An analytics team can create and distribute data products that can be applied to various use cases and incorporated into business processes. Advances in analytics mean that insights generated from these tools can be predictive and easy for individual teams to act on.7
In this way, public-sector organizations can extract more value from their data in both the near and long term. It becomes a scalable asset that can guide advanced solutions for individual service delivery while addressing issues on a societal scale.8
A European labor agency sought to understand the effects of automation and future-of-work trends on the labor market. It prioritized use cases and deployed machine learning to build six core analytics engines that allowed the agency to predict how these trends would affect segments of the population, and to make the data available to users across the public sector. The data products were embedded in various processes and are being used for automated career counseling, profession forecasting, microsegmenting of the labor market, and designing policy recommendations. This increase in output was implemented while simultaneously reducing costs.
An example from the private sector shows how predictive data analytics can be used to improve revenue and the customer experience. A North American airline built a machine-learning system based on 1,500 customer, operations, and financial variables to measure both satisfaction and predicted revenue for its 100 million daily customers. An initial use case was to improve how the airline compensated customers for delays and cancellations. At the time, the airline had no way of prioritizing the customers who were at the greatest risk of taking their business elsewhere.
A team of data scientists, customer-experience experts, and external partners worked together for three months to build the system and develop data-driven customer-satisfaction scores, which proved more insightful and actionable than survey-based data. The new model allowed the airline to identify and prioritize customers with whom its relationship was most at risk because of a delay or cancellation. It also optimized remediation strategies for each customer, which enabled the airline to offer personalized compensation to save the relationship and reduce customer defection on high-priority routes.
Fully deployed, the new system helped the airline improve its overall customer satisfaction by a factor of eight and reduce churn among high-value, at-risk customers by 60 percent. The analytics team continues to develop new data products and use cases to improve its quality of service and overall company performance. Similar predictive data analytics can be used by public-sector organizations to improve efficiency and quality of services in a range of use cases, such as managing tax liability backlogs, addressing business insolvencies, and improving workforce productivity.
Run a responsive engine
Customer needs change quickly; successful organizations notice and respond in kind while maintaining long-term resilience. The key to responsiveness is to organize around customer priorities and empower interdisciplinary teams led by business owners to test solutions and adapt them based on results. Resilience, meanwhile, comes from having a stable set of objectives and being strategic about building the capabilities required to enable their successful execution.
A feature of responsive and resilient organizations is a central team that acts as an engine for setting priorities and embedding change. This team prioritizes work based on customer needs and value, coordinates internal and external improvement activities, keeps stakeholders aligned and engaged, leads talent strategy and capability building, and helps design and sustain continuous change. It ensures that the organization is continually testing, learning, and transforming in line with its customers’ needs, and that the necessary mindsets and ways of working are implemented throughout the organization.
A North American government department that will serve 20 million people over the next 25 years sought to transform its end-to-end customer experience and services in the face of three major headwinds:
- a growing application volume that was expected to outpace its capacity to process them
- greater customer expectations of a better, faster, and more transparent digital experience
- increasing pressure to support remote work and better-quality digital services
The department needed to modernize its ways of working to prepare for the future. It took a holistic approach, starting with examining the strategy for service delivery. Executives and middle management crystallized a vision for the organization underpinned by clear outcomes and priority customer journeys. To achieve their vision, they adopted new working methods and set up a central office to oversee the effort, measure its impact, and guide transformation activities in the following two areas:
- Agile delivery: The department launched and scaled a customer-experience digital factory model with agile cross-functional teams. The model reimagined and accelerated the delivery of end-to-end journeys and prioritized them based on their impact, which was defined in terms of productivity lift and customer and employee experience improvements.
- Talent: The department partnered with academic and technology organizations and brought in more than 100 digital practitioners to complement internal talent, scale delivery, and increase software development. It also began treating its employees as internal customers, using data and design thinking to create better experiences for them through greater flexibility, empowerment, and training.
By combining a clear vision, a defined set of intended outcomes, prioritized customer journeys, and an agile delivery model with enhanced digital talent, the department achieved a significant lift in productivity, and won a national award for customer experience in the public sector in 2021.
Capture the benefits
Leaders of successful organizations are intentional and precise about the outcomes they want to achieve, and they rigorously measure progress at every step of implementation. A strong business case defines clear productivity benefits, which usually include enhanced output, greater customer satisfaction, and reduced cost. Specific objectives should be decided by the leadership team committed to realizing them.
When they are guided by the results of the design thinking process and insights from data analysis, leaders can evaluate trade-offs and make clear, informed decisions about what outcomes to measure. Once they define objectives and key results and communicate these to the organization, they should embed them into operational targets and change management practices to ensure they are delivered.
The organizations described in the previous sections ensured they captured the benefits of their customer-centric productivity transformations using the following methods:
- The leadership team of the North American government agency defined its vision as delivering a customer experience on par with those in the private sector. They then focused on identifying the main pain points and challenges for users and designing solutions to improve the areas that were most important to achieving the vision. The agency’s customer trust score increased by 40 percent, and its employee favorability rank rose from the tenth to the third decile among national government agencies.
- The airline’s delay and compensation pilot demonstrated that it could double its productivity. The team developed a real-time tracker to identify the most important customers to retain, and the customer-service team embedded this prioritization in its processes. To measure progress, the teams jointly defined key performance indicators such as the volume of contacts, customer satisfaction scores, and scores measuring the likelihood of attrition.
- The North American government department’s leadership team measured its progress in improving the customer experience and increasing productivity by tracking a series of focused leading and lagging indicators. This included a composite metric for the quality of the customer experience and time savings as a proxy for productivity.
The need for change is clear, given citizen expectations of a great customer experience grounded in digital services and the increasing pressure on public finances. Proven tools and methods are available to help agencies think like their customers, realize value through data analysis, build a responsive and resilient organization, and define measurable objectives with accountability for results. By deploying the four critical practices described, public-sector organizations can begin to overcome the challenges that have previously stymied such efforts at increasing digital productivity.