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