Creating Customer Value Through Services

In the Cambridge Service Alliance we have long talked about the importance of focusing on outcomes - understanding deeply and intimately what it is that your customer or even your customer’s customer values and exploring how you can deliver this. One of the most powerful consequences of thinking this way is that it encourages you to change the way you think about the boundaries of your business.

Take, for example, Caterpillar - what is it that their customer’s customer values? Imagine, for example a mining operation or a quarry. Clearly the customer wants a safe working environment. Clearly they want equipment that is reliable and productive. Clearly they want minimum disruption to their operations and production schedules. But ultimately what they want is to be able to extract minerals in the volumes they need at the lowest cost. If lowest cost per tonne is what the customer wants, what can Caterpillar do to help their customer achieve this?

Well the first thing is they can recognize that the mine or quarry is a system - to achieve lowest cost per tonne you have to optimize the system and get all of the people and equipment working in harmony together. It is not enough for Caterpillar to be able to guarantee that their equipment has the lowest operating cost or even lowest total lifetime cost. Unless Caterpillar’s equipment works in harmony with the rest of the quarry the customer won’t achieve lowest cost per tonne.

Working in harmony requires coordination - coordination across mixed fleets of assets and equipment. One of the services Caterpillar and their Dealers now offer are quarry optimization services. They use the data coming back off their equipment to help the customer identify production inefficiencies and lost time. Trucks, for example, have sensors in their beds. As the truck is loaded with material, the sensors record the weight of material in the bed of the truck. So Caterpillar knows when trucks are fully loaded. They also track location, through GPS data, so if your data shows a truck is fully loaded, but its GPS position is not changing then its not moving. That’s lost time - once the truck is loaded it should be moving off up the haul road en route to dump its load in the crusher.

There are loads of similar examples. Bose thinks of itself not as a speaker manufacturer but as providing sound distribution systems. Pharmaceutical firms are reinventing themselves as healthcare solutions provides - seeking to find a new way to complete as the development cost of drugs increases and more and more drugs come off patent.

At this year’s Cambridge Service Alliance conference - creating value through customer services - scheduled for the 6^th October - we’ll be hearing from three leading providers of services and solutions - ABB, Rolls Royce and Zoetis. Each of them will be explaining how they have managed to develop business models - often enabled by data and analytics - to create value for customers by focusing on the outcomes their customers and their customer’s customers really want.

The Productivity Paradox: Is There a Measurement Problem?

There's been much debate in recent months about the productivity paradox - put simply there's a long standing concern that technology, particularly information technology, does not seem to deliver the productivity gains that might be expected. This concern has resurfaced in the UK, with the Government raising questions about why the UK's productivity has not grown as much as other countries. In fact George Osborne recently called the UK's low productivity growth "the challenge of our time".

This same topic came up in a recent email discussion with colleagues from ISSIP - the International Society for Service Innovation Professionals. This time prompted by an article in the Wall Street Journal entitled "Silicon Valley Doesn't Believe US Productivity is Down". In essence the Wall Street Journal argument was that developments in technology are not captured in the Government's productivity figures - apps that help people find restaurants more quickly or hail cabs from their phones clearly improve the efficiency with which we can do things. Doing more with less is a classic definition of productivity - so these apps must be improving productivity argues the Wall Street Journal (and those it quotes - including Hal Varian, Google's Chief Economist).

While I accept the argument that apps and associated technologies allow us to do more with less, I think there's a need to unpack the relationship between these developments and measures of productivity more carefully. Traditionally governments have measured labour productivity - in terms of GDP per hour worked. As technology replaces labour, GDP stays the same or increases, while labour hours go down - hence productivity increases.

However, there's an interesting new phenomenon which complicates the picture. Take, for example, Uber. I'm a fan of Uber - the app is great. Its convenient. I've never had a bad service from an Uber driver. I love the fact that I can rate drivers and they can rate customers at the end of journeys. I love the fact that the cost of the ride gets charged to my credit card and the receipt automatically emailed to me. But I also love Uber because it is cheaper - I pay less for a Uber car than I do for a black cab in London. Better service, pleasant drivers, lower prices - what's not to like. Other firms have similar business models - think Amazon or Airbnb. Still others provide me a service for free - Google and TripAdvisor - don't charge me for the information they provide, instead making their money through third parties.

When talking about productivity - or the lack of productivity - we need to think about the economic impact of these cheaper and/or free services. Lower prices to consumers must mean lower GDP. The efficiency gains are there, but they are not being captured in productivity gains because the benefits are being passed on to consumers in the form of lower prices, rather than captured in the official GDP statistics. Maybe a more nuanced discussion about productivity is needed - where we look at both sides of the equation - increases in value and hence GDP - and increases in efficiency reflected in lower costs to consumers.

A capability-based view of service transitions

by Ornella Benedettini

Exploiting service opportunities often requires manufacturing firms to shift to new service-centric business models, logics, processes, values – in other terms to transition into what sometimes is a very different organisational setting. This transition must be supported by appropriate firm-level capabilities, this meaning that the firm has to possess the abilities, skills, knowledge and resources that underpin the development and delivery of the services that it decides to offer. While some capabilities can be leveraged from the product domain (which is a substantial advantage of manufacturing over pure service firms), others are service-specific, and hence need to be developed or acquired on purpose. It is therefore particularly important for manufacturers to have a clear understanding of what service-related capabilities they need in order to generate value and performance from their service strategy efforts.

Despite this background, the issue of capabilities has been rarely addressed directly in existing academic and practitioner studies of service transitions. For this reason, we decided to conduct a research project that analyses the service-relevant capabilities of a sample of servitized companies on the basis of the types of services that they offer and the financial performance that they achieve. In practice, with this project we would like to show three things: (i) how manufacturers orchestrate service-relevant capabilities in practice, (ii) how different services require different capabilities, (iii) how/if greater service capabilities lead to greater firm performance. The study is based on the cases of 138 companies from the aerospace and defence sector. We map the levels and sets of capabilities of these companies using the Alliance ‘Service Capability Audit’ tool. Developed by Alliance Director Andy Neely through case studies and in-depth interviews with senior managers at 12 leading servitized firms, the tool identifies 12 bundles and over 70 individual capabilities along 4 key categories: value proposition, ecosystem awareness, value delivery, and accountability spread. We draw the information regarding service capabilities from the companies’ annual report narratives using the content analysis technique and Wordstat software. We further consider 15 categories of services that aerospace and defence companies may offer and content analyse the Capital IQ long business descriptions searching for evidence of these service categories.

The study is currently in progress. The data collected so far suggest that different companies have different levels and sets of service-relevant capabilities. Intriguingly, while the literature tends to assume that all service-relevant capabilities are equally important, our empirical investigation reveals a difference in emphasis among categories and bundles of capabilities. For example, within the value delivery category, we found rich evidence of internal capabilities that enable the delivery of the value proposition but much less evidence of capabilities related to the coordination of multi-party delivery, suggesting that perhaps the sample companies tend to rely on an internal delivery system rather than on a networked one. Similarly, evidence related to the accountability spread category is focused on acknowledging that the companies are aware of business risks rather than that they have mechanisms in place to control, share or mitigate such risks. Although we haven’t yet examined the relationship between capabilities and performance, some performance differences have already emerged among the sample companies that can be potentially explained by nature and extent of the shift to services. Notably, we found an inverse U-shaped relationship between number of services offered and both firm profitability and market value. Specifically, more services mean better performance, but only until when the companies offer 8-9 services, i.e. there is a limit to the amount of service diversification that can be proficiently engaged.

The aim of the project is to contribute to the research stream of the service infusion in manufacturing, furthering the understanding of the capabilities that influence the ability to transition to services for different companies. Nevertheless, from a managerial standpoint, we seek to develop practical insights on how manufacturers could align the configuration of service strategy and organisational capabilities. For more information on this research please read this paper

Servitization and Service Innovation in China: Reflections from Shanghai

I’ve just spent a week in China, visiting the Southern China University of Technology (Guangzhou) and Ceibs, the international business school in Shanghai. While at Ceibs I participated in the first seminar on “Servitization and Service Innovation”. Attended by around 100 people, industrial speakers at the seminar included eCoal (an online coal purchasing platform), HP, Sevalo (a construction and mining equipment services business) and SKF. While Professors Marjorie Lyles (Indiana University), Chris Voss (Warwick Business School), Xiande Zhao (Ceibs) and I delivered academic presentations. It was a great trip, fascinating in so many ways, but I thought I might write a short blog about some of the themes that came out for me at the seminar. These include:

1.     The importance of technology to China - all the speakers talked about the way technology is changing China’s approach to business. They talked about all the traditional topics - cloud computing, big data, mobile, the need for better security. But they also talked about internet plus, China’s equivalent to Germany’s industrie 4.0 and the rest of the world’s internet of things. They recognise that as more and more devices are connected to the net, ever greater volumes of data will be created and these data can potentially deliver new and valuable business insights if analysed and interpreted correctly.

2.     Platforms were also a major theme - many of the firms that spoke, including many of those in the audience, were looking to create platforms, often to combine buying power and/or to utilize spare capacity. eCoal, for example, has created a coal buying platform which allows it to drive significant cost savings by pooling purchasing across multiple organisations. HP claimed to be the world’s biggest retailer of paper. With their print on demand services, where you pay per page rather than for the printer, HP is forced to buy large volumes of paper. But with large volumes comes the opportunity to negotiate discounts for bulk purchasing.

3.     One reason so many firms were interested in platforms was the massive success of China’s three stars of eBusiness - Baidu, Alibaba and Tencent (the Chinese refer to them as BAT). These three firms dominate China’s discussion of eBusiness and have all successfully created platforms, which in turn create multi-sided markets. Tencent, for example, offers users access to free online games, sells the eyeballs to advertisers, but also sells the players of games equipment upgrades. A dominant question underlying many of the comments at the forum, was how do we create platforms that will allows us to capture multiple, complementary sources of revenue for our businesses.

4.     We also talked about challenges of servitizing - the fact that having a strong product heritage or brand sometimes makes it more difficult to offer services. Interestingly a number of the speakers referred back to the roots of their organisations, obviously product of their firm’s history, but I wondered whether history also constrained their thinking about the future. SKF asked some fantastic questions about servitization. How do we persuade our customers to buy solutions from us before we have proved their value? Who buys services and solutions? Procurement is typically not structured that way. It thinks about products and categories, yet services and solutions often cross multiple products and categories.

5.     And finally we talked about enablers of servitization - what would make the transition to services easier. Through the course of the seminar I heard five key themes: (i) get inside the mind of your customer’s customer. Understand what is value to them, so you can better help your customer create value for their customer; (ii) to understand you need deep relationships - ask yourself are we really close enough to our customers; (iii) seek to balance control and collaboration in the ecosystem - not everyone needs to control or create a ecosystem. Sometimes you have to accept you are part of one and the best you can do is seek to influence it. Think about creating win-win-win across the ecosystem to drive change; (iv) learn from your experience, codify it and share it; and (v) think about solutions - SKF has created solutions factories where they can work with customers to solve their problems. Using your own ideas and technology collaboratively with the customer is a great way of getting inside their minds and building a deep relationship with them.

One of the great privileges of life as an academic is the opportunity to travel, to experience different countries and cultures. I never fail to be inspired when I go somewhere different and meet someone new. My latest trip to China was no exception.

Through-Life Accountability - Managing Complex Services

Product–service systems involve long-term contracts and various suppliers and partners in both the production and operation of the system, resulting in significant risks for servitized manufacturers. However, and especially for organisations operating in safety-critical environments, these risks are substantial since the manufacturer usually retains the through-life management of the product–service system. For example, as a result of the long-term nature of service contracts, and the involvement of multiple organisations in the operation of the system, the main cause of a failure may not always be clear and as a result the manufacturer may face significant liabilities, ranging from reputational damage to claims for financial compensation, even criminal charges.

It is therefore particularly important for servitized manufacturers to have a clear ex ante understanding of where accountability lies in the event of failure of the product–service system throughout its life (through-life accountability), in order to reduce risks and improve safety. Fielder et al., define through-life accountability as “the duty to inform, justify and accept the consequences of decisions and actions taken during the entire lifecycle of assets and associated services”. To this end, the research reported in the paper associated to the blog analyses through-life accountability in a product–service system on the basis of the decisions and actions taken by those involved in the operation of the system, along with the resulting consequences of these decisions and actions. The analysis is based on the official investigation reports of 17 commercial aircraft accidents that occurred globally between 2006 and 2013. We map the decisions and actions taken (or not taken) by those involved in the operation of the system (e.g., airline, air-traffic control and manufacturer) along with the resulting consequences, in order to identify all the actors involved in the accidents along with the most common accident causes.

The results suggest that the cause of most of the accidents in our sample was the result of a small number of human errors, undertaken by a small number of actors. These accidents could have potentially been prevented if the organisations and actors involved had adopted the attributes of, and operated as, high-reliability organisations (HROs). In fact, most of the different categories of human errors identified in the analysis can straightforwardly be linked to the lack of one of the seven attributes of HROs, as these were identified in the relevant literature. Below table presents, in descending order, all the different categories of human errors, mapped against the relevant HROs attributes.

The analysis further reveals two key issues that lead to aircraft accidents: confusion over accountabilities and the inability to question authority, with an overall lack of a culture of reporting failures. This finding provides two key insights. First, it supports our argument that servitized manufacturers and all actors involved in the system need to have a clear ex ante understanding of where accountability lies in a potential failure of the system. Indeed, the suggested mapping methodology can successfully identify and quantify accountability and, therefore, it can provide manufacturers with key insights for improving safety. For example, a detailed mapping of critical failures in a specific product–service system can identify both the main actors that have control over accidents and the most common errors. These can equip manufacturers with key information that can be included in service contracts in order to clearly delegate accountabilities and protect the organisation against liabilities.

Second, it highlights how a manufacturer’s focus should depend on the level of hierarchy within the organisations involved in the system. To be more specific, in cases of high hierarchical organisations the roles, and therefore accountabilities, will be more clearly defined. Thus, incidents will be related to the inability to question authority and report failures. Therefore, the organisation needs to place greater emphasis on promoting flexible structures and shift decision-making to experts as required. In the cases of less hierarchical organisations, confusion over accountabilities is expected to be the main issue, and a balance needs to be found between human and systems redundancy, as this will have the potential to improve system safety. A focus on the remaining attributes is equally important.  An illustration is presented below.

If you want to read more, you can access the full paper here.

by Chara Makri, Cambridge Service Alliance

Watch Out for the Industrial App Economy as the Battle for the Industrial Internet Heats Up

About six months ago I wrote a blog entitled "GE, The Industrial Internet and the Battle to Come" - in which I asked the question "will GE be the equivalent of Apple, Facebook and Google for the industrial internet or will someone else seize this market?". Its clear the battle for the industrial interne is heating up.

Last week (on 5th March) Caterpillar announced it was extending its partnership with Uptake, a Chicago based predictive analytics company. Uptake have been developing predictive diagnostic and fleet optimisation solutions for Caterpillar's the locomotive business. Under the new agreement Caterpillar and Uptake will "develop an end-to-end platform for predictive diagnostics to help Caterpillar customers monitor and optimise their fleets more effectively". Notably the new technology will be available for both Cat and non-Cat products.

Today (12th March) Siemens announced it was creating an open cloud platform for industrial customers based on the SAP HANA cloud platform. Siemens will offer Apps for predictive maintenance, asset and data energy management. They are also opening their platform so other Original Equipment Manufacturers (OEMs) or indeed Apps developers can create their own applications to exploit the open infrastructure for data analytics.

Separately I've had conversations with half a dozen different firms, from a variety of sectors, in the last couple of weeks all of which have centered around the idea of an Industrial App Economy. It seems that there's a groundswell of opinion that the future for industrial services lies in open, cloud based platforms, where developers can offer Apps to make the end users service and support experience as seamless as possible.

There's an interesting question with all of these developments - namely how will the investments be monitisied? Is it through sale of the Apps? Provision of the insights that can be derived from the data? Or sales of new products and support services - as customers are tied in to particular OEMs? It'll be interesting to see how this battle evolves as other potential competitors for the industrial internet declare their hands.

Data-Driven Business Models (DDBM): A Blueprint for Innovation

“A Blueprint that can be utilized by established organisations to create their own business models that rely on data as key resource”

We live in a world where data is often described as the new oil. Just as with oil, the value contained within data is universally recognized. As the seemingly relentless march of big data into so many aspects of the commercial and non-commercial world continues, the practicalities of constructing and implementing data-driven business models (DDBMs) has become an ever-more important area of study and application. For today’s businesses, effective data utilization is concerned with not only competitiveness but also survival itself. In some industries, such as publishing, big data has spawned entirely new business models. For example, after a movement towards a digitally oriented distribution model and dwindling advertising revenues, certain publishers began to accumulate data relating to their online users – users whose demographic was particularly attractive to advertisers. This data could then be sold, enabling targeted and more effective advertising.

However, although big-data-oriented publications agree on the potentially positive impact of big data utilization, very few suggest how, in practice, it can be attained and none offer a research-based guide or blueprint that can be utilized by an existing business to help create and implement its own DDBM. The DDBM blueprint and the corresponding six fundamental questions of a data-driven business will allow existing businesses and start-ups to follow a step-by-step process to construct their own DDBM centred around the businesses’ own desired outcomes, organization dynamics, resources, skills and the business sector within which they sit. We are presenting an integrated framework that could help stimulate an organization to become data-driven by enabling it to construct its own DDBM in coordination with the six fundamental questions for a data-driven business.

  

The DDBM Blueprint suggests that creating a business model for a data-driven business involves answering six fundamental questions:

1. What do we want to achieve by using big data?
In order for a business to effectively utilize big data it is vital that its aims are clear and realistically attainable. Often an organization understands the potential value and benefit associated with data but fails to determine a specific aim before undertaking a time-consuming and costly data acquisition and analysis process. Seven key competitive advantages are attained; shortened supply chain, expansion, consolidation, processing speed, differentiation and brand. For example, the fashion retailer Zara aimed to achieve close to real-time customer insight into fashion industry trends and purchasing patterns so that it could better align itself with its customers, resulting in increased retail sales volume. Zara aimed to utilize a shortened supply chain to gain competitive advantage and incorporated near real-time sales statistics, blog posts and social media data into its analytic systems, to rush emerging trends to market.

2. What is our desired offering?
A business must decide in what way the DDBM construct will benefit the company’s current offering or, alternatively, create an entirely new one. Established businesses have a tendency to utilize data to improve or enhance their current customer offering, which is often called a ‘value proposition’.. A company can offer raw data that is primarily ‘a set of facts’ without an attached meaning. When data has been interpreted it becomes information or knowledge. Typically the output of any analytics activity attaches some insight or application.  For example, the mobile phone service provider AT&T increased the positive public perception of its brand after evaluating a customer sentiment analysis based upon both internal (current users) and external (potential users) data sources. This insight enabled AT&T to improve its product and service offering in areas considered most important to its potential and actual customers, thus maximizing the derived benefit from the investment.

3. What data do we require and how are we going to acquire it?
Data is obviously fundamental to a DDBM. Deciding which data is most applicable, and the nature of that data’s acquisition, is pivotally important to the success of a DDBM construction. Established businesses with a substantial number of customers, and therefore potential customer interaction points, are well positioned to effectively utilize customer-provided data within their DDBM, although this data is often combined with data from other sources. This high utilization of all available data sources by established organizations is indicative that these organizations understand the value of data and orient themselves towards becoming data-driven. For example, the fashion retailer Topshop combines customer-provided data, free available data from fashion blogs and social media, and existing data within its own databases when running predictive and descriptive analytics protocols to determine emerging trends within the highly competitive retail clothing industry

4. In what ways are we going to process and apply this data?
Methods of processing reveal the true value contained within data. Knowing which key activities will be utilized to process data enables the business to plan accordingly, ensuring that the necessary hardware, software and employee skill sets are in place. To develop a complete picture of the key activities, the different activities were structured along the steps of the ‘virtual value chain’. To gather data, a company can either generate the data itself internally or obtain the data from any external source (data acquisition). The generation can be done in various ways, either manually by internal staff, automatically through the use of sensors and tracking tools (e.g. Web-tracking scripts) or using crowd-sourcing tools. Insight is generated through analytics, which can be subdivided into: descriptive analytics, analytics activities that explain the past; predictive analytics, which predict/forecast future outcome; and prescriptive analytics, which predict future outcome and suggest decisions. In the financial services sector, where finely-tuned predictive analytic modelling influences business decisions, Goldman Sachs plans years in advance to ensure it has the capacity, hardware, processes and employee skill sets available to utilize increased data volumes and new technologies. In fact, approximately 30 per cent of all Goldman Sachs’ employees work in technology and development.

5. How are we going to monetize it?
Without the target of a quantifiable benefit to a business it is difficult to justify DDBM construction and implementation. Incorporating a revenue model into a DDBM is integral to its operational success. Seven revenue streams are identified: asset sale, giving away the ownership rights of a good or service in exchange for money; lending/renting/leasing, temporarily granting someone the exclusive right to use an asset for a defined period of time; licensing, granting permission to use a protected intellectual property like a patent or copyright in exchange for a licensing fee; a usage fee is charged for the use of a particular service; a subscription fee is charged for the use of the service; a brokerage fee is charged for an intermediate service; or advertising. Revenue models associated with a DDBM differ considerably from a standard subscription fee such as The New York Times for advertising. These models vary considerably between sectors and within industries.

6. What are the barriers to us accomplishing our goal?
Interestingly, our research and analysis revealed clear links between specific inhibitors to the implementation of a DDBM. Established businesses are experiencing cultural issues, personnel issues, and internal value perception obstacles to implementing a DDBM. Our study suggests that issues with personnel may be the most severe DDBM implementation inhibitors experienced by both new and established businesses and may be linked to a variety of other obstacles to a business becoming data-enabled.

 You can access full paper here.