Category: Big Data

Soyer, E., Pauwels, K., & Seggie, S. H. (2019). Marketing in a Digital World (Review of Marketing Research, 16, 63-83.

Short Article

Abstract:
While Big Data offer marketing managers’ information that is high in volume, variety, velocity, and veracity (the 4Vs), these features wouldn’t necessarily improve their decision-making. Managers would still be vulnerable to confirmation bias, control illusions, communication problems, and confidence issues (the 4Cs). The authors argue that traditional remedies for such biases don’t go far enough and propose a lean start-up approach to data-based learning in marketing management. Specifically, they focus on the marketing analytics component of Big Data and how adaptations of the lean start-up methodology can be used in some combination with such analytics to help marketing managers improve their decision-making and innovation process. Beyond the often discussed technical obstacles and operational costs associated with handling Big Data, this chapter contributes by analyzing the various learning and decision-making problems that can emerge once the 4Vs of Big Data have materialized.

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with Steven Seggie, and Emre Soyer, AMS Review, December, 7 (3-4), 2017, 154-169

Full Article

Abstract:
The continued survival of firms depends on successful innovation. Yet, legacy firms are struggling to adapt their business models to successfully innovate in the face of greater competition from both local and global startups. The authors propose that firms should build on the lean startup methodology to help adapt their business models while at the same time leveraging the resource advantages that they have as legacy corporations. This paper provides an integrated process for corporate innovation learning through combining the lean startup methodology with big data. By themselves, the volume, variety and velocity of big data may trigger confirmation bias, communication problems and illusions of control. However, the lean startup methodology has the potential to alleviate these complications. Specifically, firms should evolve their business models through fast verification of managerial hypotheses, innovation accounting and the build-measure-learn loop cycle. Such advice is especially valid for environments with high levels of technological and demand uncertainty

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with Gatignon, Hubert, Xavier Lecocq, and Alina Sorescu. AMS Review, December, 7 (3-4), 2017, 85-89

Short Article

Abstract:
The Internet and the digital economy have prompted the creation of new business models (McKinsey and Company 2017). Technologies that can enable new business platforms and increased digital access to potential customers have significantly changed in the manner in which firms conduct business, from the creation of value (see, for instance, the literature on co-creation, e.g., Hoyer et al. 2010, or that on open innovation, e.g., Chesbrough et al. 2006), to the appropriation of value (see, for instance, research on freemium pricing, e.g., Pauwels and Weiss 2008, or on pay-per-use, e.g., Prasad et al. 2003). Competitive advantage from product innovation has become difficult to maintain for extended periods of time; as a result, incumbents are increasingly looking for ways to update and innovate their existing business models (Neus et al. 2017). For instance, Accenture reports that 80% of firms plan to grow via new business models over the next 5 years (Accenture 2014).
Changes in business practice have also led to a heightened focus on business models in the academic literature. Several recent reviews highlight the scope of this literature, which ranges from attempts to define and provide structure for the concept of business model, to examinations of specific types of business models (Coombes and Nicholson 2013; Foss and Saebi 2017; Massa et al. 2017; Zott et al. 2011). The majority of these articles, however, can be found in management journals. Attempts to study business models in marketing are scant, and typically focus on specific sectors of the economy (e.g., Wieland et al. 2017 on services, and Sorescu et al. 2011 on retailing) or on specific types of business models (e.g., Kind et al. 2009; Pauwels and Weiss 2008). Moreover, while value creation and appropriation have separately received attention in marketing, they have been rarely studied in combination, which is a prerequisite to understanding business models. This is a surprising gap, given that marketers are responsible for the design and implementation of several aspects of the value creation and value appropriation components of a business model. This special issue is a first step in stimulating more research on this topic in the marketing literature. Building on what we have learned from the management literature but bringing insights from the marketing literature’s expertise on building a viable value proposition, we can further enhance our understanding of how business models can be effectively designed in order to lead to sustainable firm performance.

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Handbook of Marketing and Finance , Shankar Ganesan ed., 2012 ISBN: 978-1849802727

Full Article

Abstract:
Marketers continuously engage in actions such as product launches and advertising that are aimed at generating profitable customer response and increasing key marketing assets such as customer satisfaction and brand equity. To the extent that these efforts are successful, they should enhance the financial outlook of the firm, which is of primordial interest to the investment community. However, the interpretation of the impact of these actions is not straightforward, because the effects tend to play out over time. As a result, it is quite possible that mispricing occurs, i.e. the investment community either overestimates or underestimates the future financial impact of a marketing action or marketing metric. The purpose of this chapter is to review time series methods as the primary research tool for evaluating the pricing of marketing actions and marketing assets. The main message here is: what is mispricing, what are various forms (especially the difference between contemporaneous and lags), how can we detect, what does it mean for investors/managers, and what do researchers do with the mispricing information? We discuss, in turn, methods for assessing the return (level) and the risk (volatility) of marketing for the investor, and we provide illustrative findings of each.

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Review of Marketing Research, 6, Naresh Malhotra, 2009

Full Article

Abstract:
Return on Marketing Investment (ROMI) is defined as the incremental margin generated by a marketing program divided by the cost of that program at a given risk level (Powell 2002). The typical formula is displayed in equation 1:

Return on Marketing Investment = [Incremental Margin – Marketing Investment] / Marketing Investment

Use of this metric promotes accountability for marketing spending, enables comparison across alternatives to decide on the best action and furthers organizational learning and cross-functional team work. Unfortunately, managers are struggling to define and calculate ROMI (Woods 2004), especially outside the price/promotions domain (Bucklin and Gupta 1999). A survey of over 1000 C-level managers (CMO Council 2004) revealed that over 90% of marketing executives viewed marketing performance metrics as a significant priority, but that over 80% were unhappy with their current ability to measure performance. Only 17% of marketing executives have a comprehensive system to measure marketing performance. The companies they work for outperformed other firms in revenue growth, market share and profitability. Thus, most organizations experience considerable roadblocks to fulfill the appealing promise of measuring ROMI and using it to enable better marketing decisions and higher performance. Since financial decisions within the firm in non-marketing domain are evaluated, at least in part, if not primarily, based on their return on investment, it makes investing in marketing activities more difficult by not having their comparable measure.

Several reasons underlie these difficulties, from the improper use of the term ‘return on investment’ for measures that do not include profits/margins nor investment costs (Lenskold 2003), to the lack of research into how return on marketing investment can be measured and how it can be used to enhance performance (Pauwels et al. 2008). Indeed, while many marketing practitioners and academics have expressed concern about marketing accountability and return on investment, the current push largely has come from outside the field, notably top management and finance (Lehmann and Keller 2006). Unfortunately, CEOs and CFOs have been disappointed by the most common responses of the marketing field, from ‘it is hard to judge the impact of marketing spend since so many factors come into play between the spending and the ultimate financial result’ (marketing practice), to ‘we already show it through our sales response functions’ (marketing academia). The authors’ experience in recent years demonstrates that such positions are of little help in bridging the gap between marketing and finance fields, enabling joint understanding and trust in ROMI calculations and ROMI-based decisions and building the standing of marketing in the C-suite.

Previous authors have already laid the conceptual frameworks for return on marketing investment (Lehmann 2005, Lehmann and Reibstein 2006, Rust et al. 2004, Sheth and Sisodia 2002, Srivastava et al. 1998). True to the focus of Review of Marketing Research on “implementing new marketing research concepts and procedures”, the current paper discusses 10 conceptual and implementation issues that complicate measurement and use of return on marketing investment. First, the ‘incremental margin’ in equation 1 (hereafter ‘return’) needs to be forecasted, in terms of magnitude but also timing and associated risk. Second, the investment could involve a combination of marketing actions and needs to be considered from the point of decision perspective. Once the components of returns and investment are measured, it is still unclear whether they should be combined for a focus on (7) impact versus efficiency and (8) realized versus potential return on marketing investment. Finally, acting upon measured return on marketing investment requires (9) clarity on how to weigh multiple objectives and (10) an understanding of whether high ROMI means the marketing action should get more or less investment in the future Often, spending more on programs with a high ROI will lower the ROI percentage but raise the total return, given we are generally at the diminishing returns stage of the response curve.

The remainder of this paper discusses all 10 challenges in detail, giving examples and critically examining how research has addressed and should further address these issues.

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with Imran Currim, Marnik G. Dekimpe, Eric Ghysels, Dominique M. Hanssens, Natalie Mizik and Prasad Naik, Marketing Letters, 15:4, 167-183, 2005, leading article

Full Article

Abstract:
This paper argues that time-series econometrics provides valuable tools and opens exciting research opportunities to marketing researchers. It allows marketing researchers to advance traditional modeling and estimation approaches by incorporating dynamic processes to answer new important research questions. The authors discuss the challenges facing time-series modelers in marketing, provide an overview of recent methodological developments and several applications, and highlight fruitful areas for future research. This discussion is based on the First Annual Conference on ‘Modeling Marketing Dynamics by Time Series Econometrics’ at the Tuck School of Business at Dartmouth, Hanover, New Hampshire, USA on September 16–17, 2004.

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with E. Dans, Journal of Brand Management, 8 (4-5), 303-314, 2001.

Full Article

Abstract:
Can established newspapers leverage their offline brand equity to the online edition in order to create visits and page views? This question is key for publishers, as they are now facing a change only comparable to the advent of the printing press in the 15th century. In the present study, both cross-sectional time-series analyses are applied to 12 Spanish newspapers. The findings indicate that brand equity in the marketplace can be efficiently leveraged into the marketplace. Online readership depends both on offline popularity and on the profile fit between the typical Internet user and the typical offline reader of the newspaper. The digital market dynamics are uncovered by the persistence modelling of visits, page views, and brand choice of each newspaper. First, the total number of visits initially evolves, but later stabilises. In contrast, page views continue to evolve as usage depth increases over time. Finally, brand choice is stable and proportional to the brand equity borrowed from the printed newspaper. The analysis yeilds specific recommendations for the three leading newspapers. 

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