This is a guest post by Johan Treutiger, Åsa Ahlgren, Erik Thure, and Erik Tengblad of Arthur D. Little.

Over the past five years, organizations across most sectors have embraced the move from simply selling products to providing ongoing services to their B2B and B2C customers. The disruption of the pandemic has only accelerated this change. As we described in our previous article, this means you can now purchase almost anything “as a service”, from coffee to lighting or machine bearings.

However, for most organizations this transition has only gone so far. They may have added subscription services to their offerings, but have not yet gained full value by implementing outcome-based business models (OBMs), in which customers purchase a guaranteed outcome to meet their needs.

OBMs transform the customer relationship. Imagine signing up with an organization that guarantees that you won’t be burgled or that you will consistently be sharply dressed. These are examples of the radical possibilities that OBMs offer to customers – and to organizations. A successful OBM typically provides deeper customer value, greater insights into individual requirements, and longer and more profitable customer relationships compared to traditional online or offline business models.

Focusing on outcomes also increases company valuations – ADL research shows that companies that rely on digital assets with recurring revenue sources and extensive data-based solutions are valued 2.4 times higher than companies with hard assets that focus on one-off sales.
Additionally, OBMs support greater sustainability. Rather than replacing (and disposing of) products, they focus on “getting the job done” and delivering a real outcome, whether washing laundry or making a call. This delivers lower environmental impact and higher customer satisfaction.

The building blocks of OBMs are now available. Connected products and services enable gathering of greater volumes of continuously generated data, which can be analyzed at scale using AI and machine learning to better understand individual customer preferences and launch transformative products and services. Automation and wider partner ecosystems can deliver these services at scale and with guaranteed outcomes. For example, California-based clothing company Stitch Fix, instead of just selling clothes, delivers a curated selection of garments to customers based on AI analyses of their responses to style questionnaires and previous purchases.

However, OBM adoption is currently in its early stages due to its complexity and the risks it brings. Achieving this level of servitization maturity demands a fundamental shift in business models and operations – it goes far deeper than simply launching a service to customers. Organizations need to transform themselves internally, build strong data skills and competencies, handle changing risks and liabilities, and recruit a wider ecosystem of partners. Most businesses therefore remain at the stage of providing subscription-based services, unlocking limited additional value.

This article outlines how organizations can start to transform themselves, overcoming the challenges to build deeper, data-driven customer relationships, achieve higher revenues, and reshape their operations around OBMs.

The journey to outcome-based business models

The first step in the OBM journey is to evaluate whether, where, and how it can be applied. The three key questions to ask are:

• Could additional services be bundled into our offering to increase customer value?
• Could our existing and new services be turned into a continuous service offering?
• Could we offer a guaranteed outcome to address current inefficiencies, and how could this change our innovation and business efforts?

If the answer to one or more of these questions is yes, the next step is to detail how the shift can begin and how to leverage data and technology to achieve transformation. Academic research and real-world experience identify three major sequential steps that organizations must take to successfully embrace OBMs, as shown in Figure 1:

1. Scope extension: Adding new services or increasing the scope of services. Initially, these are typically basic services (such as ad hoc maintenance), with a gradual shift towards a more complete and advanced offering.
2. Time-frame extension: Building on step one by extending the time frame of services, such as adding an ongoing support contract. This moves from a transactional to a relational model, with a deepened relationship with the customer.
3. Guaranteed outcomes: Rather than simply delivering services, the provider guarantees the outcome to the customer. This calls for internal transformation, closer collaboration and innovation with partners, and higher accountability.

Companies tend to approach this shift gradually, building on each step, which provides learning and evaluation opportunities. However, it is vital to understand that moving to an OBM is not simply a question of continuing the journey – it requires a radical reshaping of the organization, customer relationships and partner ecosystem.

The challenges to outcome-based business models

While most organizations recognize the transformative power of OBMs, the majority have currently reached step one or two on their servitization journey. For example, car manufacturers such as Volvo and Land Rover now offer certain vehicles through subscription models, including previously additional costs such as insurance and maintenance in the monthly charge. However, these incumbents face competition from pure outcome-based platform players, such as Uber and Lyft, which are vehicle-brand agnostic but solve the end customer’s “job to be done”, namely, to transport them from one point to another.

Successfully moving to an OBM requires overcoming a range of key challenges:

1 . Organizational mind-set shift

Extending the customer relationship to focus on outcomes requires the entire business model and mind-set to be re-engineered – all at the same time. Capabilities have to be developed around collecting and effectively processing data throughout the customer lifetime, building deeper customer relationships and managing a larger, more complex partner ecosystem. The typical number of customer interactions in B2C OBMs grows tremendously, requiring automation of both interfaces and data communication.

Changing everything from organizational design to customer touchpoints and employee incentives would be a major challenge if carried out separately. However, the shift to OBMs necessitates them all happening simultaneously, either in a separate business area or for the whole company.

Additionally, customers themselves may be suspicious of change and need to see a concrete demonstration of the ongoing value it will bring. Some may enjoy the experience of completing a job themselves – such as driving to a destination or browsing through a physical clothing store.

2. Customer data and analytics skills

Effective collection and analysis of data is at the heart of delivering successful outcomes. However, for customers to share this data they must see the benefits and be assured information is being used responsibly and securely.

These new data skills and competencies, including AI and robotization, may not currently be available within an organization – and may be difficult to find in the wider market. Almost half (46 percent) of UK businesses say they struggle to recruit for roles that needed data skills, and this picture is likely to be similar in other countries and regions.

3. Higher financial risk and new liabilities

By guaranteeing outcomes to customers, it will be crystal clear to all parties if these have not been fulfilled. Companies are selling a promise, not a product. This greater accountability increases the pressure on organizations to constantly deliver.

Organizations therefore need to achieve a balance – promising realistic outcomes that are attractive for customers while guaranteeing that they have the internal resources and structures to meet them.

OBMs also demand a new approach to predicting and communicating revenue targets. Initially, they can negatively impact profitability and cash flow due to the costs of developing new services, lower initial payments, and the risk of penalties. Moving forward, while revenue streams will be more constant, the risk of penalties need to be factored into models to deliver a realistic market forecast. The shift from an upfront product sale to a model in which payments are made in arrears demands excess cash to smooth over the transition, as well as careful management and investor communication for listed businesses.

However, once the company has fine-tuned its promised delivery levels and accounted for development costs, additional sales have an incremental cost. The recurring-revenue nature of outcome-based sales and greater customer lifetime value compensate many times for lower initial profitability.

The success factors for outcome-based business models

Achieving a successful transition to an OBM involves businesses overcoming these key challenges and fundamentally changing their culture and operations, based on five areas:

1 . Generate ongoing senior leadership support and business model autonomy

Given the complexity – and scope – of the necessary business changes, it is crucial that senior management leads and supports the shift. Transformation must begin with a shift in organizational mind-set focused on the business logic of moving to outcomes and recurring customer relationships.

Everyone must be aligned to ensure there is a consensus of where the company is heading and why. Governance structures and roles and responsibilities will need to adapt to fit with the new model. Strong communication is necessary to explain the benefits of an OBM to all employees throughout the change process.

For an incumbent player, an OBM should typically be piloted and nurtured separately from traditional product or product-and-service business models. This allows organizations to test and learn together with customers. When the OBM is fine-tuned, it can be scaled to replace traditional business models.

2. Effectively harness data and become data driven

Every organization now generates increasing volumes of data, but to unlock its value this data must be collected, stored, and made available for timely, detailed analysis, with the results shared with the right stakeholders. This requires both new technology capabilities and a broad range of human skills. Data must first be collected, either through Internet of Things (IoT) sensors or new software capabilities, and then provided for analysis that can underpin monitoring and decision-making. AI and machine learning provide more precise, faster analysis to optimize OBMs, something that was unnecessary in a traditional product sales environment or even earlier in the servitization journey.

Technology must span the entire ecosystem – for example, integrating with partner and customer systems across the supply chain to provide a complete view of all data and factors involved in delivering a successful outcome. Digital twins of products are needed in many OBMs to keep track of performance and take proactive steps to ensure service levels are met. Automation and robotization enable delivery of guaranteed outcomes at scale, removing the need for constant human involvement.

Employees must be trained and skilled in using analysis tools and AI techniques, such as predictive analytics, to turn data into valuable business information. This often demands large-scale recruitment, which puts the onus on organizations to appeal to data scientists to attract and retain their talents. The founder of Stitch Fix, Katrina Lake, stresses how data science is the company culture – the business is dependent on data for its very existence.

Elevator company Kone has incorporated IoT sensors into its connected elevators, which enables it to utilize predictive analysis to highlight when maintenance is needed. This has led to a 40 percent drop in unforeseen faults. Downtime can then be scheduled for optimal times to avoid disruption, based on passenger usage data. Building on this, it would be possible to implement an outcome-based model, with charging based on actual elevator use.

3. Building a highly customer-centric view is a necessity

With an OBM, the interface with the customer changes. Indeed, in the case of companies previously selling products through indirect channels, it creates completely new requirements. Customer service and customer experience capabilities need to be created or transformed, with an accompanying shift in metrics, to focus on long-term, outcome-based KPIs. Employees who work close to the customer must demonstrate skills and behaviors such as flexibility, relationship building, service centricity, authenticity, technical adeptness, and resilience, which may not have previously been needed.

Ensuring customer buy-in to the OBM is critical. Achieving this requires not just delivering on contracted outcomes, but also constantly showcasing additional problems or “jobs” that can be solved to ensure the relationship is maintained and improved.

Toyota’s Kinto mobility solution provides customers with vehicle usage paid for and accessed on demand. It is extending this solution towards an OBM by collecting customer driving data and using it to personalize the experience, with the goal of offering “cars that evolve in tune with people”. In addition, Kinto’s brand promise focuses on “ever better mobility for all”, going beyond simply cars and focusing on Transportation-as-a-Service.

4. New capabilities and a closer partner ecosystem

Delivering outcomes requires new capabilities, which necessitates greater involvement of partners throughout the business model. This may not simply be in specific areas (such as delivering onsite support), but intertwined throughout the organization. These open business models bring risks through an increased reliance on partners outside a company’s direct control, but add essential skills, capabilities, and innovation to achieve outcome-based success. Software partners or vendors play a particularly important role in gaining access to required technologies, digital assets, and platforms to drive transformation and change.

5. Rapid, customer-driven innovation and agile processes

Existing processes and incentives are normally based on current business models. For example, a truck manufacturer may have a maintenance arm that is tasked to maximize its revenues, incentivizing teams to carry out lengthy and expensive repairs on customer vehicles. If, instead, the business provides a guaranteed level of vehicle availability to the customer, processes must shift to completing repairs as quickly as possible to meet contractual obligations. Innovation processes must also change to involve co-creation with customers and partners to accelerate the pace of innovation and ensure improvements fit with their demands and needs. An agile mind-set and working methods underpin a successful shift to an OBM, focusing on high responsiveness to customer demands while ensuring all new features deliver true customer value. Few companies have totally shifted to OBMs, but many are benefiting by tapping into specific elements.

Insights for the executive

OBMs promise a revolution in the customer relationship that delivers greater value for organizations, whether measured in higher and more predictable revenues, stronger relationships, sustainability, or higher valuations. Achieving this requires a substantial company transformation, which needs to start now and focus on:

• Begin with ideation and definition. An OBM requires you to deeply understand the outcome the customer is ultimately looking to achieve. The successful OBM must start with an ideation process to define the outcome and the main levers to get there.
• Ring-fence shift and investment. A shift to an OBM should initially be ring-fenced to ensure it is not killed by traditional business models and incentives. When proven and fine-tuned, it can be broadened to replace parts (or all) of the legacy model.
• Invest to become “data-first”. Successful OBMs rely on mastering data. Consequently, investments and change efforts are needed to develop and recruit the right skills and change the culture and behavior within the organization to become “data-first”.
• Understand and begin transformation. Refocusing away from product sales demands new processes, incentives, customer interactions and structures throughout the customer lifecycle aligned with the OBM. This change must be led from the top, with buy-in from all. Short feedback loops and rapid, customer-driven innovation processes are required to continuously enhance outcomes and create long-term customer relationships.
• Broaden partnerships across the value chain. Investment in the right infrastructure and tools (e.g., AI/ML, predictive analytics, IoT, consumption monitoring and digital twins) must be combined with a more open approach to partnerships to drive innovation and successful outcomes. M&As and alliances are required for specific technologies and capabilities to gain and sustain competitive advantage.

Sidebar 1
The burglar-free residence

The US market for home alarm systems is worth USD34.9 bn. However, the majority of security companies have a similar business model focused on installing, maintaining, monitoring, and responding to alarms when someone breaks into the residence.

Imagine instead if a company offered a “burglar-free house”, guaranteeing the customer’s desired outcome, which is protecting their home and family. This would directly steer the company efforts, innovation, and pricing model in new directions.

By leveraging both historical and current data (such as the area, type of residence, time, historical burglaries, whether the residence is empty, and external events), the company can fine-tune predictions of when and where a burglary could be attempted and develop appropriate preventive measures.

Innovation would switch to these preventive actions rather than incrementally improving an alarm system to signal when a break-in happens. Areas such as outdoor surveillance, vehicle/facial recognition, body detection, automatic lighting, robotic patrols, and a “systems thinking” approach that leverages and analyzes data and insights from all connected residences would provide potential innovation.

Partner firms could be leveraged for increased security, such as delivery companies visiting the premises, while virtual assistants such as Alexa or Google could support consumer communication. Robotic lawn mowers could even be turned into “robo-guards” as a first line of surveillance. By leveraging data, AI, and digital technologies and creating the right partnerships, outcomes could be guaranteed and the trauma of burglary removed. Given the obvious advantages, there would be a strong incentive for the consumer to continuously share data to improve the service, which would create the basis for long, profitable relationships.

Sidebar 2
Einride

Many start-ups have focused on electric and even autonomous vehicles. Swedish freight technology company Einride is going a step further. Its electrified, self-driving trucks, or “Einride Pods”, are not for direct sale – instead, customers pay a monthly fee for a complete Capacity-as-a-Service solution. Equipment such as connected electric and autonomous trucks, charging solutions, and connectivity services are an integrated part of that offering, with all assets planned and orchestrated by an intelligent operating system. This shift from a reactive approach (selling a product) to proactively delivering an outcome demonstrates the possibilities in even the most traditional sectors, such as transport and logistics.

 

This is a guest post by Johan Treutiger, Åsa Ahlgren, Erik Thure, and Erik Tengblad of Arthur D. Little.

 

About the authors

Johan Treutiger is a Partner in Arthur D. Little’s Stockholm office and leads the Nordic Strategy & Organization Practice (pictured).

Åsa Ahlgren is a Manager in Arthur D. Little’s Stockholm office and a member of the Strategy and Organization Practice.

Erik Thure is a Business Analyst in Arthur D. Little’s Stockholm office and a member of the Strategy and Organization Practice.

Erik Tengblad is a Business Analyst in Arthur D. Little’s Gothenburg office and a member of the Strategy and Organization Practice.

 

 

 

 

 

Image by kalhh from Pixabay