How do fashion retail customers search on the Internet?: Exploring the use of data mining tools to enhance CRM

This paper seeks to determine the usefulness of data mining tools to SMEs in developing customer relationship management (CRM) in the fashion retail sector. Kalakota & Robinson’s (1999, p.114) model of ‘The Three Phases of CRM’ acts as a basis to explore the use of data mining software. This paper reviews the nature and type of data that is available for collection and its relevance to CRM; providing an advisory framework for practitioners for them to examine the scope and limitations of using data analysis to improve CRM. The data mining tool examined was Google Analytics (GA); an online freeware tool that enables businesses to understand how people find their site, how they navigate through it, and, ultimately, how they do or don’t become customers of it (Google Analytics, 2009). Establishing these relationships should lead to retailer development of enhanced web site aesthetics and functionality to coincide with consumer expectations. The paper finds that the competitive nature and homogeneity of the fashion retail sector requires retailers to improve the ‘reach, richness and affiliation’ (Hackney et al) of their sites by using technology to explore CRM

Towards Design Principles for Data-Driven Decision Making: An Action Design Research Project in the Maritime Industry

Data-driven decision making (DDD) refers to organizational decision-making practices that emphasize the use of data and statistical analysis instead of relying on human judgment only. Various empirical studies provide evidence for the value of DDD, both on individual decision maker level and the organizational level. Yet, the path from data to value is not always an easy one and various organizational and psychological factors mediate and moderate the translation of data-driven insights into better decisions and, subsequently, effective business actions. The current body of academic literature on DDD lacks prescriptive knowledge on how to successfully employ DDD in complex organizational settings. Against this background, this paper reports on an action design research study aimed at designing and implementing IT artifacts for DDD at one of the largest ship engine manufacturers in the world. Our main contribution is a set of design principles highlighting, besides decision quality, the importance of model comprehensibility, domain knowledge, and actionability of results

To boardrooms and sustainability: the changing nature of segmentation

Market segmentation is the process by which customers in markets with some heterogeneity are grouped into smaller homogeneous segments of more ‘similar’ customers. A market segment is a group of individuals, groups or organisations sharing similar characteristics and buying behaviour that cause them to have relatively similar needs and purchasing behaviour. Segmentation is not a new concept: for six decades marketers have, in various guises, sought to break-down a market into sub-groups of users, each sharing common needs, buying behavior and marketing requirements. However, this approach to target market strategy development has been rejuvenated in the past few years. Various reasons account for this upsurge in the usage of segmentation, examination of which forms the focus of this white paper. Ready access to data enables faster creation of a segmentation and the testing of propositions to take to market. ‘Big data’ has made the re-thinking of target market segments and value propositions inevitable, desirable, faster and more flexible. The resulting information has presented companies with more topical and consumer-generated insights than ever before. However, many marketers, analytics directors and leadership teams feel over-whelmed by the sheer quantity and immediacy of such data. Analytical prowess in consultants and inside client organisations has benefited from a stepchange, using new heuristics and faster computing power, more topical data and stronger market insights. The approach to segmentation today is much smarter and has stretched well away from the days of limited data explored only with cluster analysis. The coverage and wealth of the solutions are unimaginable when compared to the practices of a few years ago. Then, typically between only six to ten segments were forced into segmentation solutions, so that an organisation could cater for these macro segments operationally as well as understand them intellectually. Now there is the advent of what is commonly recognised as micro segmentation, where the complexity of business operations and customer management requires highly granular thinking. In support of this development, traditional agency/consultancy roles have transitioned into in-house business teams led by data, campaign and business change planners. The challenge has shifted from developing a granular segmentation solution that describes all customers and prospects, into one of enabling an organisation to react to the granularity of the solution, deploying its resources to permit controlled and consistent one-to-one interaction within segments. So whilst the cost of delivering and maintaining the solution has reduced with technology advances, a new set of systems, costs and skills in channel and execution management is required to deliver on this promise. These new capabilities range from rich feature creative and content management solutions, tailored copy design and deployment tools, through to instant messaging middleware solutions that initiate multi-streams of activity in a variety of analytical engines and operational systems. Companies have recruited analytics and insight teams, often headed by senior personnel, such as an Insight Manager or Analytics Director. Indeed, the situations-vacant adverts for such personnel out-weigh posts for brand and marketing managers. Far more companies possess the in-house expertise necessary to help with segmentation analysis. Some organisations are also seeking to monetise one of the most regularly under-used latent business assets… data. Developing the capability and culture to bring data together from all corners of a business, the open market, commercial sources and business partners, is a step-change, often requiring a Chief Data Officer. This emerging role has also driven the professionalism of data exploration, using more varied and sophisticated statistical techniques. CEOs, CFOs and COOs increasingly are the sponsor of segmentation projects as well as the users of the resulting outputs, rather than CMOs. CEOs because recession has forced re-engineering of value propositions and the need to look after core customers; CFOs because segmentation leads to better and more prudent allocation of resources – especially NPD and marketing – around the most important sub-sets of a market; COOs because they need to better look after key customers and improve their satisfaction in service delivery. More and more it is recognised that with a new segmentation comes organisational realignment and change, so most business functions now have an interest in a segmentation project, not only the marketers. Largely as a result of the digital era and the growth of analytics, directors and company leadership teams are becoming used to receiving more extensive market intelligence and quickly updated customer insight, so leading to faster responses to market changes, customer issues, competitor moves and their own performance. This refreshing of insight and a leadership team’s reaction to this intelligence often result in there being more frequent modification of a target market strategy and segmentation decisions. So many projects set up to consider multi-channel strategy and offerings; digital marketing; customer relationship management; brand strategies; new product and service development; the re-thinking of value propositions, and so forth, now routinely commence with a segmentation piece in order to frame the ongoing work. Most organisations have deployed CRM systems and harnessed associated customer data. CRM first requires clarity in segment priorities. The insights from a CRM system help inform the segmentation agenda and steer how they engage with their important customers or prospects. The growth of CRM and its ensuing data have assisted the ongoing deployment of segmentation. One of the biggest changes for segmentation is the extent to which it is now deployed by practitioners in the public and not-for-profit sectors, who are harnessing what is termed social marketing, in order to develop and to execute more shrewdly their targeting, campaigns and messaging. For Marketing per se, the interest in the marketing toolkit from non-profit organisations, has been big news in recent years. At the very heart of the concept of social marketing is the market segmentation process. The extreme rise in the threat to security from global unrest, terrorism and crime has focused the minds of governments, security chiefs and their advisors. As a result, significant resources, intellectual capability, computing and data management have been brought to bear on the problem. The core of this work is the importance of identifying and profiling threats and so mitigating risk. In practice, much of this security and surveillance work harnesses the tools developed for market segmentation and the profiling of different consumer behaviours. This white paper presents the findings from interviews with leading exponents of segmentation and also the insights from a recent study of marketing practitioners relating to their current imperatives and foci. More extensive views of some of these ‘leading lights’ have been sought and are included here in order to showcase the latest developments and to help explain both the ongoing surge of segmentation and the issues under-pinning its practice. The principal trends and developments are thereby presented and discussed in this paper

Predicting customer's gender and age depending on mobile phone data

In the age of data driven solution, the customer demographic attributes, such as gender and age, play a core role that may enable companies to enhance the offers of their services and target the right customer in the right time and place. In the marketing campaign, the companies want to target the real user of the GSM (global system for mobile communications), not the line owner. Where sometimes they may not be the same. This work proposes a method that predicts users' gender and age based on their behavior, services and contract information. We used call detail records (CDRs), customer relationship management (CRM) and billing information as a data source to analyze telecom customer behavior, and applied different types of machine learning algorithms to provide marketing campaigns with more accurate information about customer demographic attributes. This model is built using reliable data set of 18,000 users provided by SyriaTel Telecom Company, for training and testing. The model applied by using big data technology and achieved 85.6% accuracy in terms of user gender prediction and 65.5% of user age prediction. The main contribution of this work is the improvement in the accuracy in terms of user gender prediction and user age prediction based on mobile phone data and end-to-end solution that approaches customer data from multiple aspects in the telecom domain

THE ROLE OF BUSINESS INTELLIGENCE IN BUSINESS PERFORMANCE MANAGEMENT

Business performance management (BPM) is a key business initiative that enables companies to align strategic and operational objectives with business activities in order to fully manage performance through better informed decision making and action. EffecBusiness Performance Management (BPM), Business Intelligence (BI), business processes, strategy, integration