30 August 2017

From Big Data Analytics to Big Tool Stack

Are we still discussing Big Data or we should start reviewing and monitoring its different tools and platforms? Also, how organization should determine the right tool to utilize as part of its data analytics architecture?

In today’s competitive business environment, Internet of Things devices and Information Services increasingly produce large amount of data in disparate structures. Many Open source and commercial tools continue to pop up to deal with the different characteristics of Big Data. As a result, there is an abundance of tools and platforms to analyze Big Data or act as building blocks of such. Just by reviewing open source tools, we have come across 300 tools. That number is final after we applied strict filters like legitimity of the source, license type, and last commitment activity. We are not talking about Big Data anymore, we are talking about Big Tools.

To extract value from Big Data, an organization should determine the right tool to utilize as part of its data analytics architecture. The right tool would depend on the characteristic of the data to be analyzed and the domain that the organization is operating under. The organization would train its IT workforce to obtain the technical expertise to be effective with those tools. Businesses incur costs when they try to adopt these tools or change their existing source codes to run on newer versions. In other words, technical debt. In the Big Tools era, there is no standard on how these tools come together and compose a data analytics architecture. Most of these tools are unknown to business world, some of these tools even we didn’t know. To illustrate this, Apache Beam and Apache SAMOA are good examples. Latest trends in the big data domain is moving towards providing a level of abstraction to utilize popular data processing platforms. Apache Beam implements its dataflow programming model on multiple processing platforms like Apache Spark and Apache Flink. Apache SAMOA enables programmers to apply machine learning algorithms on data streams. Applications developed with SAMOA can be executed on Apache Storm and Apache Samza. Moreover, new models and tools continue to emerge at a fast pace in Big Data domain. There is no established method to track the newest developments particularly for the open source tools.

We are working towards developing an open source big data analytics architecture. We are trying to keep it as simple as possible to provide a comprehensive picture on big data analytics lifecycle. For academia, the architecture will provide the state of the art, tools that are missing, and tools that are mature enough to be used as part of a research. It will also provide the method for tracking notable new open source tools popping up in different sources. For technical people, it will help determine the tool to use for a particular implementation. Small and medium sized enterprises can provide services using some these tools addressing the gaps in a bigger architecture. For an established firm trying to develop a strategy, the architecture will provide the comprehensive picture on what fits where. Commercial big data solution providers can also benefit from this architecture. They will see the capability they lack and collaborate with a small sized enterprise to provide that capability.

Mert Gokalpand Keres Kayabay are working with Mohamed Zaki to build this architecture. We will publish a working paper soon on this topic. 

15 August 2017

Article by Katharina Greve, Cambridge Service Alliance, University of Cambridge

Co-creation is here to stay. For companies that understand the concept and execute it well, the rewards can be far greater than even the most efficient internal R&D system can deliver. By embracing co-creation, companies are able to outperform the market, develop better products and get them onto shelves faster, provide superior service, and build deeper, more loyal relationships with their customers. Co-creation is a core capability for unleashing the immense resourcefulness of outsiders. Yet, ten common co-creation myths prevent companies from seeing the potential to innovate better.

1. Co-creation should only include customers

Co-creation is a term often used to describe the process of creating value involving companies and their customers. Although this is not wrong, it is important to highlight that co-creation can also include other stakeholders such as suppliers, employees, distributors, NGOs and regulators. In fact, involving other stakeholders is essential because they directly or indirectly shape customers’ experiences. Co-creation with suppliers, for example, can be a great way of reducing costs and improving the efficiency of existing products or services. For instance, Ford’s Aligned Business Framework was launched in 2005 to empower suppliers to share information at the earliest stages of the design phase. The goal was to boost trust, transparency and supply-chain efficiency which subsequently has led to closer working relationships, earlier access to innovative supplier-sourced design features[1] and, importantly, to lower production costs.

2. Co-creators should only be the usual target audience

Asking what can be a homogeneous target audience for feedback on your product or service prototype is useful but may result in similar responses and reactions. Companies that reach beyond the usual customer segments, however, may receive diverse, unique and creative ideas that unlock value from innovative opportunities that were initially not considered. For example, a company invited people to participate in the co-creation process of a special ‘hopping mat’ for children. The hopping mat was originally designed to enter answers to mathematical questions in order to study school material more playfully. However, an elderly person put forward an idea for a further use of the mat by highlighting the physical workout that is also taking place in the process. Co-creators who are not the usual target customer can be a valuable source of fresh ideas.

3. Co-creation is only appropriate for products

Digitalisation has transformed books, music, maps, cameras and calendars into digital services. These are not just products but are a combination of both the digital artefacts themselves and the services that deliver them. This makes the co-creation activities different from those of products alone. But involving co-creators helps unlock the potential of digital services as well as products.[2]For example, a company offering customisation of shoes through its online platform was able to receive authentic customer feedback about its pricing strategy through an offline co-creation approach. Not able to capture the customer’s first impression online, the company used an open innovation space in order to co-create together with its customers. In this setting, co-creators pointed out that the company’s pricing strategy is irritating: “Shoes with laces should be more expensive than those without”.Although the production cost was not significantly different, the company decided to adjust prices in accordance with the feedback.

4. Co-creation is only suitable for large companies

Co-creation success stories are emerging across various industries, and involve both small and large firms. For example, The LEGO Group employs co-creation to successfully engage many of their adult customers in the design process of new products. Peter Espersen, head of global community co-creation at LEGO explains: “We have learned some big lessons. First, that creativity has almost no boundaries; people can do amazing things. Second, we can’t always predict what consumers want. [..] When you combine the idea of LEGO with another strong community, and a fun one, that is rocket fuel in the engine - it really works.”[3]  Small and medium-sized organisation can also exploit co-creation benefits by involving outsiders in their innovation process. For instance, a small apparel company asks its customers which of its next season clothes they would want to wear.  Based on the customers’ input, the company can decide how much to produce and how much stock it will need. This reduces inventory costs, and the products are better fitted for the customer needs. Furthermore, a more accurate forecast of supply and demand for specific items can be achieved. For small companies, this kind of approach can be the key for success, reducing uncertainty about the success of products in the market.[4] Also, co-creation allows firms to mitigate risks related to the development of new products and services. As all stakeholders contribute ideas to creating a product or service, the chances of it failing are considerably reduced.

co creation labs 2

5. Co-creation is only fit for B2C

The most publicised co-creation cases - such as giffgaff, eBay and Zipcar - are usually in a direct-to-consumer setting. B2B leaders may dismiss it as an approach that they can afford to ignore. Most B2B firms are disconnected from their end customer and hence may lack a clear understanding of their needs and wants. A company that has managed to overcome these challenges is Diehl Controls, a leading developer and manufacturer of display, control and drive systems. To familiarise future users early on with the possibilities offered by new products, Diehl Controls presented itself at the theme world ‘SMARTer Living’ in the Nuremberg-based innovation laboratory JOSEPHS®. Although Diehl operates in a B2B context, the company used this innovative platform to address end customers directly and asked for their opinions regarding their products. The results of the surveys they conducted were directly used to improve the products of Diehl Controls. In addition, its participation in this theme world led to initial contact with potential customers who are interested in smart home solutions.[5] Enabling this direct interaction with end customers helps companies experiment more easily with new product offerings and make the final product as close to the customers’ needs as possible. This is important in the B2B context as much as the B2C.

6. Co-creation is only for beneficial for the private sector

Co-creation has spread rapidly in the business sector. In the public sector, however, adoption of the co-creation approach is a fairly recent occurrence driven by rising citizen expectations[6]. In the traditional government model, a public organisation obtains resources through a budgetary allocation and then uses those resources to deliver services to stakeholders. The citizens that take advantage of these services are largely passive - they do not actively influence the design or delivery of the service. Contrary to this traditional approach, a public-sector co-creation initiative allows public sector entities to open their value chains to the stakeholders whom they serve. Stakeholders become active contributors in the co-creation of the public sector value proposition[7]. Co-creation has several benefits: it reduces public sector costs of between 20% and 60%, and increases stakeholder satisfaction, generate better outcomes[8], improve the image of the state sector in the perspective of citizens[9]. Thus, co-creation offers a real-world response to the innovation imperative that most public-sector entities face nowadays.

7. Co-creation is only relevant for the developed world

The concept of co-creation is not only relevant for developed countries, it also has an essential role in unlocking opportunities in the 'base of the pyramid' (BOP) markets. Co-creation between multinational corporations, economically disadvantaged people, potential business partners, non-profit organisations and other players is shaping products, services, business models, mindsets, business ecosystems and even whole communities in BOP markets. For multinational companies that try to enter developing countries, close collaboration and co-creation with stakeholders may be even more critical.[10] In these markets, it is important to co-create business models; successful BOP ventures “often included having the product and business model development co-evolve. Partner organizations co-designed the entry strategy, including the delivery of the product or service” [11] (p. 14). For instance, CEMEX, a retailer of cement, concrete and aggregates, learned from Mexican communities that to serve the local DIY home-building market, it would have to provide financing and construction training as well as materials.[12]

Katharina Greve Dr Veronica Martinez in co-creation lab

8. Co-creation is a stand-alone tool

Co-creation does not need to be a stand-alone tool, it can also be linked to a variety of other initiatives and approaches. For example, when an established customer community has been formed around a brand, a community app can be used to add social conversations and receive insights to support research and co-creation activities. Alternatively, companies can choose to combine internal and external innovation activities. For example, a company started to develop an app to manage personal finances better. During the first year and a half, its internal research and development efforts took place before they started involving customers in the co-creation process. Depending on the situation, companies tailor the co-creation approach to their individual needs. This way, co-creation is not a standalone approach but it is integrated into a wider innovation activity.

9. Co-creation is a final test before launching a product

Co-creation can be employed in all four phases of the new product development (NPD): ideation, product development, commercialisation and post-launch, co-creation in the early stages of the NPD can lead to more innovative ideas [13][14]. Consequently, co-creative companies do not wait until a new product is designed and launched to receive customer feedback. In fact, beta versions and prototypes are released to customers and other stakeholders for ideas and feedback which are then assessed and incorporated into the product or service. Users are even considered to have a greater ability for idea generation than the employees of a firm[15]. In this context, companies can greatly benefit from the iterative co-creation process by receiving real-time feedbackAs a result, co-creation reduces the cost of creating new products and services as well as the marketing associated with it.

10. Co-creation offers answers to questions a company asks

The term co-creation can be described as “any act of collective creativity where more than one individual is involved, resulting in something that is not known in advance”[16]. This definition should prepare companies to expect the unexpected: creative and value adding ideas may come from stakeholders not only about a certain product or service, but also about other areas in the value chain such as product packaging, areas of application, sales channels and even raw materials used. For example, a company aiming to improve the shoe shopping experience with its innovative 3D foot scanner aimed to obtain information related to the market needs and privacy of data through co-creation with customers. Although, the company gained insights on these aspects, it also received feedback concerning the material of the 3D scanner foot plate. During their trials, customers noticed that the material of the foot plate shows if someone has sweaty feet which makes the measuring experience rather embarrassing. Consequently, the company changed the material of the foot plate. This highlights that co-creation can offer answers to questions but it can also deliver unexpected insights in areas companies initially did not consider.



The Cambridge Service Alliance is a unique global alliance between leading businesses and universities. It brings together the world's leading firms and academics, all of whom are devoted to delivering today the tools, education and insights needed for the complex service solutions of tomorrow.  Its members are BAE Systems, Caterpillar Inc., IBM and the University of Cambridge's Institute for Manufacturing and Judge Business School. Find out more here.






[1]  http://www.autonews.com/article/20130805/oeM10/308059994/ ford-strengthens-bonds-with-its-elite-suppliers
[2] Chowdhury, S., 2012. Co-creation of Innovative Digital Services. In 35th Information Systems Research Seminar in Scandinavia, Sigtuna, Sweden, August 17-20, 2012 (p. 12).
[6] Alves, H., 2013. Co-creation and innovation in public services. The service industries journal33(7-8), pp.671-682.
[8] Bason, C. (2010). Leading public sector innovation: Co-creating for a better society. Bristol: The Policy Press.
[9] Vigoda-Gadot, E., Shoham, A., Schwabsky, N., & Ruvio, A. (2008). Public sector innovation for Europe: A multinational eight-country exploration of citizens’ perspectives. Public Administration, 86(2), 307–329.
[10] Nahi, T., 2012. Co-creation at the base of the pyramid. Paper for Corporate Responsibility Research Conference, University of Leeds, UK.
[11] London, T. and S.L. Hart 2004. 'Reinventing strategies for emerging markets: Beyond the transnational model', Journal of International Business Studies, 35: 350-370.
[12] Sánchez, P., J. Ricart, and M. Rodriguez. 2005. Influential factors in becoming socially embedded in low-income markets. Greener Management International 51: 19–38.
[13] Hoyer, W.D., Chandy, R., Dorotic, M., Krafft, M. and Singh, S.S., 2010. Consumer cocreation in new product development. Journal of service research13(3), pp.283-296.
[14] Witell, L., Kristensson, P., Gustafsson, A. and Löfgren, M., 2011. Idea generation: customer co-creation versus traditional market research techniques. Journal of Service Management22(2), pp.140-159.
[15] Kristensson, P., Gustafsson, A. and Archer, T., 2004. Harnessing the creative potential among users. Journal of product innovation management21(1), pp.4-14.
[16] Sanders, L. and Simons, G., 2009. A social vision for value co-creation in design. Open Source Business Resource, (December 2009).


Images from top: the living lab, JOSEPHS® - the Service Manufactory in Nuremberg; Katharina Greve and Dr Veronica Martinez participating in the co-creation process at the living lab, JOSEPHS®.

15 June 2017

A Systems Perspective on Business Model Evolution

The Case of an Agricultural Information Service Provider in India

By Chander Velu

Business models are complex activity systems that summarise the architecture and logic of a business, and defines the organisation’s value proposition and its approach to value creation and capture. The role of the business model is to act as a mechanism to enable the core value proposition to be transferred as benefits to the customer. This is especially so when new technologies provide the basis for new customer value propositions. However, often new business models need to be altered from the initial version in order to create the design that might be sustainable and profitable. We present a longitudinal and in-depth single case study of a unique, mobile-phone-based information service for farmers in India. The firm was formed by a major global blue-chip company. In particular, the case study examines how the new firm evolved its initial business model from a mobile-phone-based information service for farmers to a transactions platform for agricultural crops between buyers and sellers, and subsequently incorporating an engagement-based solutions provider business model for banks and other agricultural-related businesses.

The study builds on three themes emerging from the systems thinking literature in order to highlight the organisational capabilities that enables business model evolution. The three themes are:
  1. Balanced redundancy refers to the ability of the firm to stretch and create additional overlapping resources in order to perform experiments while running the existing business model.
  2. Requisite variety refers to the extent to which components of the system obtain a variety of information to understand the environment better.
  3. Cognitive discretion refers to the freedom to perceive and construct an idiosyncratic meaning.
We explain how these three constituent organisational capabilities enabled the new firm to innovate its business model in order to explore and develop an appropriate customer value proposition to create and capture value. The lessons from the paper would be helpful for managers as they create new business models and need to evolve them from their original design.

Read our papers:


Velu, C. (2017) A Systems Perspective on Business Model Evolution: The Case of an Agricultural Informational Service Provider in India, Long Range Planning, forthcoming.

10 May 2017

Exploring the Journey to Services

Do you know what your service journey looks like?

By Veronica Martinez, Chander Velu and Andy Neely

Many organisations when exploring their transition to Services ask themselves the question: ‘what does a service journey look like?’  At the Cambridge Service Alliance, this question also emerged when our Industry Partners met to discuss ‘the shift to services’ – among them Presidents, Vice Presidents and Directors of Caterpillar, Zoetis, GEA, IBM, BAE Systems and Pearson. Interestingly, around the table, none of the firms could articulate the lessons from their own service journeys in a comprehensive manner. This is not an uncommon issue in organisations embarking on the journey to provide services.

So, we setup an interdisciplinary team of academic and industrial partners to explore the Journey to Services. The concept itself is not new but certainly largely unexplored. Through 7 years of an in-depth study of three comparable firms and countless sets of workshops and interviews with other firms (for academic details please read our journal paper), we jointly discovered what a service journey looks like:

The Five Key Lessons You Need to Know about the Service Journey are:
  1. The service journey in industrial manufacturers is neither logical nor structured but much more emergent and intuitive in nature.
  2. Similar steps, different journeys. Some organisations followed similar steps but the sequence of these were different. Often the sequence of steps in the service journey is described as a ‘back and forth’ sequence – or trial and error. Exploited by choices, the typical examples include: the services are ready to be sold, but the sales training and/or incentives for selling services are missing. Services are offered to customers, customers buy them, but the accounting systems are set to manage product transactions and not service contracts. Services are designed, as products, consequently the service experience is missed and gradually the services fail.
  3. The evolution and coexistence of different services. Typically, in the first three years of the service journey, organisations incrementally evolve by offering basic to intermediate services. After the fourth year, organisations follow ‘the continuous evolution of the basic and intermediate services and the emergence of complex services’. Then, the coexistence of basic, intermediate and complex services varies across the service continuum.
  4. The pace of change. Once organisations embark in the service journey, they are in continuous ‘change’ (flux) as opposed to punctuated interventions of change. This is the continuous granular change at different functional levels throughout the organisation.
  5. Service Strategy: Seven associated stages of the service strategy model should be considered by organisations to manage their service journeys.
From our perspective, we think the understanding of the service journey has evolved significantly over the last years. This is the first framework that longitudinally maps the journey to services. Firms which have used the framework express more confidence in managing the transition process and are more prepared to handle the issues that they confront. In future, organisations urgently need to focus on the dynamic evolution of their service journeys, particularly on the proactive management of individual lifecycles of their services. As Joseph Schumpeter expressed – the importance to focus on the ‘creative destruction’ within their processes of transformation.

Read our monthly paper,  journal paper ‘Exploring the journey to services’ or listen to our podcast.

Paper:

28 February 2017

Customer Loyalty Analytics


Customer loyalty is a strategic priority for organisations. We did some work with one of our industrial partners to build a data-driven method to better assess and predict customer loyalty. Organisations still use single-question customer metrics, such as the Net Promoter Score (NPS), which is popular despite recent studies arguing that customer loyalty is multidimensional, and therefore firms require to combine behavioural and attitudinal data sources. One of the reasons why organisations rely on NPS is the simplicity to administer measuring customer loyalty. However, the picture now gets complicated because customers interact with firms that are leveraging new technologies such as mobile applications, social media platforms, virtual reality, drones and the Internet of Things to provide smart services and enable a seamless customer experience. The complexity of using these technologies within an organisation’s myriad touchpoints has led to a data explosion across touchpoints in the entire customer journey. Thus, it is more difficult to rely on single metrics like the NPS. In the Cambridge Service Alliance, we investigated this area and built a novel customer loyalty analytics method that demonstrates an approach to utilising data more effectively to assess and predict customer loyalty in complex business-to-business (B2B) service organisations.

To acquire a holistic view of customer loyalty, we integrated data across multiple systems. The data was classified into three categories: attitudinal, behavioural and demographics. The attitudinal data was collected from the customer survey, which includes structured (NPS rating) and unstructured data (verbatim comments). The behavioural data was collected from the financial system. This data consists of sales (new, used, lease), product support (parts and service transaction types) and customer service agreement (CSA) transactions (parts and service transaction types). Two groups of customers were identified: those who have a maintenance contract with the company, referred to as Customer Service Agreement (CSA) customers; and those who deal in a transactional setting, referred to as Product Support (PS) customers. Demographic data, which contains the regional locations of customers, was included. In total, we collected 1,044,512 transaction records over a three-year period.

Our predictive model used:
  1. RFM to transform customer transactional data into profitability scores, facilitating the categorisation of customers based on their purchasing behaviour
  2. The K-means clustering model technique to segment data points into groups, each containing data points similar to one another and dissimilar to data points in other groups. In our work, we divided customers into 11 groups based on their RFM scores. This was accomplished using the K-means segmentation algorithm
  3. Active Customers Predictive-We have identified active customers who had not churned and had company dealings in the form of transactions
  4. Demographic- we used the geographical location of customers, which appears within a transaction record.
  5. Text-mining Model- We developed a linguistic-based text-mining model to analyse the open-ended customer comments in the survey. A sentiment score for each comment was then calculated.
Our customer loyalty model enabled us to classify customers as either churners or loyal customers based on these predictive indicators. Our predictive model was built using neural and Bayesian network classification techniques. The accuracy results of these two algorithms were compared. The steps employed in the model’s construction are three-fold. First, a training set, 60 per cent of the entire data, was used to develop a training model. Then we tested the model against new data in the construction stage, which formed 30 per cent of the original data. Here the model was fine-tuned to decrease the error of false predictions. Finally, the model was validated against the remaining 10 per cent of customer data. These three steps are performed to ensure the repeatability and validity of the prediction model.

Our model shows how firms can compare NPS scores with repurchasing behaviour as a loyalty assessor, using predictive variables such as the RFM model, demographics, active customers and textual customer complaints, while the most popular performance measure, the NPS, completely disregards this. Clearly, customer loyalty measurement requires holistic loyalty-tracking initiatives. Based on the amalgamation of data sources, the actual underlying customer loyalty can be fully assessed and interpreted through the use of big data analytics. Furthermore, our model has the predictive ability to determine whether customers are likely to churn, thereby increasing the model’s functionality. Over the three years, if the organisation used NPS as an indicator for customer loyalty, over half of the customers were considered to be completely satisfied. However, using our analysis, we identified many misclassified NPS categories, which has misled the company. For example, in 2013 and 2014 we found that approximately 500 customers were considered to be detractors when they were classified as promoters according to the NPS classification. Thus, NPS alone is not sufficiently accurate for organisations. If organisations want to understand why their customers churn, the answer could come from the verbatim comments provided in survey data or social media. Our text-mining model enables us to analyse the root causes of customer complaints, which, expressed in these free verbatim comments, uncover potentially vulnerable customers that NPS would have considered loyal and not requiring intervention strategies.

If you want to read more about this work, please read our paper or listen to my webinar