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The awareness of probability was observed in ancient cultures through the discovery of primitive dice games made with animal bones. The history of analytics in the workplace, as it is currently known (defined as predictive analytics), probably started in ancient Roman times, when the concept of insurance was first created. While the previous example showed that analytics for business had been around for some time, it is only relatively recently that there is an increased emphasis on the use of analytics in the modern firm. Credit card firms and retail catalog companies relied on analytics to drive their business models, for most of the latter half of the twentieth century. The use of advanced analytics for business also grew around the Millennium since the widespread use of data warehousing and relational databases on client servers. Moreover, Machine Learning and Artificial Intelligence Techniques, which have been around for many decades, have had very few breakthrough successful applications up until recently when cloud computing and being able to take advantage of the infrastructure of companies, such as Amazon and Google, with their Cloud Services enabled these algorithms to be used to their full extent in firms. This powerful infrastructure availability coupled with BIG DATA is creating breakthrough applications across many business models on a consistent basis. This chapter explores the use of advanced analytics across different business functional areas. It also introduces some breakthrough models, which include Netflix, Pandora, eHarmony, Zillow, and Amazon, and explores how these are not only changing the lives of consumers but also changing the nature of the workplace and creating new issues for firms such as data protection and liabilities for the actions of automated algorithms.

Sales organizations embrace technological innovation. However, salespeople’s willingness to use new technology influences a firm’s return on investment, representing a significant concern for the organization. These concerns highlight tensions regarding the tradeoffs associated with technology implementations. The purpose of this study is to offer insights that help reduce the complexities of sales technology (ST) by exploring the changing dynamics of contemporary business relationships.

This paper synthesizes the ST literature using the service ecosystem perspective to propose the sales techno-ecosystem (STE) framework, providing new insights into organizational decision-making related to the ongoing digital transformation of sales tasks.

This synthesis of the ST literature with the service ecosystem seeks to clarify the impact of technology within the evolving nature of buyer–seller relationships by providing four unique perspectives.

Perspective 1 reviews the sales-service ecosystem framework and develops the theoretical underpinnings and relevant terminologies. Perspective 2 summarizes critical aspects of the ST literature and provides foundations for future research in the STE. Perspective 3 offers a more granular view, explicating roles and contexts prevalent in buyer–seller–technology interactions. Perspective 4 provides a set of tenets and advances research questions related to each tenet.

The culmination of these four perspectives is the introduction of five key tenants designed to help guide strategy and research.

The paper advances Hartmann et al. (2018) service ecosystem paradigm by explicating critical aspects of its ST domain to generate insights for theory and practice.

This study aims to explore the changing nature of the inside sales role and the individual capabilities required for success. Additionally, it examines the influence of organizational structure on inside sales force capabilities. Although business-to-business firms are investing heavily in inside sales forces, academic research lags behind this evolution.

Using a two-study qualitative approach, the authors examine contemporary inside sales forces’ responsibilities and operational configurations. Study 1 uses a cross-industry sample of sales leaders and professionals to examine roles and responsibilities. Study 2 used the second sample of sales leaders and professionals to explore the impact of various organizational configurations.

The study identifies important differences between inside and outside salespeople in terms of job demands and resources; inside salespeople’s greater reliance on sales technology and analytics than outside counterparts; and existing control systems’ failure to provide resources and incentives to match with inside salespeople’s increasing strategic benefits and job demands. The study also explores four distinct inside–outside configurations. The differences among these configurations help to explain the distinct benefits and costs of each configuration regarding the company, customer and intra sales force processes, which, in turn, determine inside salespeople’s strategic benefits and job demands.

The authors discuss the theoretical implications of these findings for research on the evolving roles and capabilities of the inside sales force; antecedents and consequences of firms’ choice of inside–outside sales force configurations; and the impact of technology and the inside sales force. They propose a research agenda that includes a series of specific future research questions.

This study informs managers of the unique role of the inside sales force and how it differs from their outside counterpart. The results inform managers of the issues inherent to various inside sales configurations, helping them determine, which configuration best addresses their customers’ needs.

This research provides a detailed, updated account of the differences between inside and outside sales forces and the benefits/costs of major inside–outside sales force configurations. Drawing from job demands-resources, organizational structure and strategy-context fit theories, the authors develop research propositions about the underlying structural differences of inside-outside sales force configurations; how these differences drive the inside sales force’s increasing strategic benefits and job demands; and organizational choice of inside sales force configurations. A research agenda is then presented.

A vast amount of complex data is being generated in the business environment, which enables support for decision-making through information processing and insight generation. The purpose of this study is to propose a process model for data-driven decision-making which provides an overarching methodology covering key stages of the business analytics life cycle. The model is then applied in two small enterprises using real customer/donor data to assist the strategic management of sales and fundraising.

Data science is a multi-disciplinary subject that aims to discover knowledge and insight from data while providing a bridge to data-driven decision-making across businesses. This paper starts with a review of established frameworks for data science and analytics before linking with process modelling and data-driven decision-making. A consolidated methodology is then described covering the key stages of exploring data, discovering insights and making decisions.

Representative case studies from a small manufacturing organisation and an independent hospice charity have been used to illustrate the application of the process model. Visual analytics have informed customer sales strategy and donor fundraising strategy through recommendations to the respective senior management teams.

The scope of this research has focused on customer analytics in small to medium-sized enterprise through two case studies. While the aims of these organisations are rather specific, they share a commonality of purpose for their strategic development, which is addressed by this paper.

Data science is shown to be applicable in the business environment through the proposed process model, synthesising micro- and macro-solution methodologies and allowing organisations to follow a structured procedure. Two real-world case studies have been used to highlight the value of the data-driven model in management decision-making.

The main objective of this marketing strategy case is to understand why and how a firm should evaluate and rework its own business to stay competitive and aligned with external market forces.

Technical Solutions Group (TSG), was the India-based technical support division of Quatrro Global Services and focused on providing support to customers of large Original Equipment Manufacturers (OEMs) and software developers around the globe. Because of the increased competition and structural changes at the customer end, the business was facing flattened revenue growth and a drop in margins. TSG had to make strategic decisions on how to continue the business, either within the current paradigm of being a business to business business or transforming into a business to consumer business leveraging digital technology.

This case can be used in an undergraduate (upper level) or graduate-level course on marketing strategy, digital strategy or strategic management.

Teaching Notes are available for educators only.

CSS 11: Strategy.

Practitioners and researchers have reached a consensus that supply chain analytics is a strong determinant for desirable organizational outcomes such as supply chain performance and agility. The purpose of this paper is to examine a configural combination (i.e. causal recipes) subsuming supply chain data analytics, firmsize, age and annual sales to predict supply chain agility based on knowledge-based theory.

Survey data (n = 215) were obtained from firms operating in the United Arab Emirates. Consequently, fuzzy sets qualitative comparative analysis (fsQCA) technique was applied to the data to establish causal recipes that are necessary and sufficient to achieve high scores of supply chain agility.

Results from fsQCA support the major tenets of complexity theory that several configural combinations (i.e. supply chain data analytics, firm size, firm age and annual sales) are sufficient and necessary conditions for achieving higher scores of supply chain agility.

This study is first of its kind in understanding the association between supply chain data analytics and agility with fsQCA technique. This research also offers a headway for supply chain managers in identifying configural combinations of antecedents manifesting high scores for supply chain agility. Implications for theory and practice are illustrated as well as future research course.

This paper aims to test whether passive Wi-Fi visitor analytics is a useful and effective method to measure consumer engagement towards food trucks located within an outdoor activation area at an Australian metropolitan university.

Using passive Wi-Fi visitor analytics to ping and track smart devices, data was collected over 90 weekdays capturing data from 522,548 unique smart devices.

The data collected in this feasibility study was able to identify the most and least popular food trucks by displaying the differences in both bounce and engagement rates, suggesting that passive Wi-Fi visitor analytics are feasible and useful in this context. Furthermore, the results also demonstrate that food truck vendors and marketers should not engage in random rotation, but instead remain static to try and increase familiarity.

Current visitor tracking technology (i.e. ticketed sales, sales data and survey) is limited as it may not provide an accurate measurement of foot traffic, identify engaged patrons who passed by but did not complete a purchase and be available due to commercial sensitivity and confidentiality. Thus, the current research is the first to examine customer engagement (i.e. unengaged walk-by vs engaged but bounced vs engaged sales) with food trucks within an activation area by using passive Wi-Fi visitor analytics.

当前的论文旨在研究被动 Wi-Fi 访客分析是否是衡量消费者对位于澳大利亚城市大学户外活动区域内的流动餐车的参与度的有用且有效的方法。

使用被动 Wi-Fi 访客分析来跟踪智能设备, 从 522,548 个独特的智能设备收集了超过 90 个工作日的数据。

该可行性研究中收集的数据能够通过显示跳出率和参与率的差异来识别最受欢迎和最不受欢迎的流动餐车, 这表明被动 Wi-Fi 访客分析在这种情况下是可行和有用的。 此外, 我们的结果还表明, 流动餐车供应商和营销人员不应随意轮换, 而应保持静止从而增加顾客熟悉度。

当前的访客跟踪技术（即售票销售、销售数据和调查）是有限的, 因为它可能无法：（1）提供客流量的准确测量; (2) 识别路过但未完成购买的参与顾客; (3) 由于商业敏感性和保密性而可用。 因此, 目前的研究是第一个通过使用被动 Wi-Fi 访客分析来检查激活区域内流动餐车的客户参与度（即, 未参与路过, 相比于参与但跳出, 相比于参与售出额）。

The purpose of this paper is to investigate the initiation of accounting information system projects. Specifically, it examines the role of the predictive analytics (PA) project initiator in the integration of financial and operational sales forecasts.

The study uses a field study method to address the studied phenomenon in eight Finnish companies that have recently adopted PA systems. The data are primarily based on 19 interviews in the companies and five interviews with the PA consultants.

The authors found that initiators appear to play a major role regarding the degree of integration of financial and operational sales forecasts. The initiators from an accounting function have a tendency to pay more attention to the integration than the representatives from other functions, such as operations and sales.

The study also makes a practical contribution to companies in showing and discussing the important role of the accounting department as an initiator of a project if the target is to achieve a tight coupling of financial and operational forecast figures, i.e., “one set of numbers”.

Even though companies have increasingly adopted PA systems in recent years, we still know little about how the initiation affects the design of accounting information systems overall. The central contribution of the paper, therefore, is to show that if a PA project is initiated by the accounting department, data integration becomes more likely. It contributes also to the discussion related to the appropriateness of data integration in the context of forecasting.

This paper aims to empirically test, from a regulatory portfolio management standpoint, the application of liquidity-adjusted risk techniques in the process of getting optimum and investable economic-capital structures in the Gulf Cooperation Council financial markets, subject to applying various operational and financial optimization restrictions under crisis outlooks.

The author implements a robust methodology to assess regulatory economic-capital allocation in a liquidity-adjusted value at risk (LVaR) context, mostly from the standpoint of investable portfolios analytics that have long- and short-sales asset allocation or for those portfolios that contain long-only asset allocation. The optimization route is accomplished by controlling the nonlinear quadratic objective risk function with certain regulatory constraints along with LVaR-GARCH-M (1,1) procedure to forecast conditional risk parameters and expected returns for multiple asset classes.

The author’s conclusions emphasize that the attained investable economic-capital portfolios lie-off the efficient frontier, yet those long-only portfolios seem to lie near the efficient frontier than portfolios with long- and short-sales assets allocation. In effect, the newly observed market microstructures forms and derived deductions were not apparent in prior research studies (Al Janabi, 2013).

The attained empirical results are quite interesting for practical portfolio optimization, within the environments of big data analytics, reinforcement machine learning, expert systems and smart financial applications. Furthermore, it is quite promising for multiple-asset portfolio management techniques, performance measurement and improvement analytics, reinforcement machine learning and operations research algorithms in financial institutions operations, above all after the consequences of the 2007–2009 financial crisis.

While this paper builds on Al Janabi’s (2013) optimization algorithms and modeling techniques, it varies in the sense that it covers the outcomes of a multi-asset portfolio optimization method under severe event market scenarios and by allowing for both long-only and combinations of long-/short-sales multiple asset. The achieved empirical results, optimization parameters and efficient and investable economic-capital figures were not apparent in Al Janabi’s (2013) paper because the prior evaluation were performed under normal market circumstances and without bearing in mind the impacts of the 2007–2009 global financial crunch.