Deconstruction and Analysis: Demonstrating the Value of Marketing (Hanssens and Pauwels, 2016).

In this article the authors aimed to demonstrate the value of the marketing function in companies (Hanssens and Pauwels, 2016). This peer-reviewed article brought many topics to the analysis, as well as suggestions for future research. The authors provide a deep discussion of points with plentiful evidence based on empirical data. The following main points were discussed:

  • The difficulty of the marketing value assessment
  • The influence of marketing objectives on marketing value metrics
  • Methods and findings; about assessing marketing value
  • Communicating marketing value within the organization

All these topics are explored by the authors using the rigor of scholar analysis of prior investigations by others, and their own research data. The following is a description of Hanssens and Pauwels’ work for this article defining each main point and touching upon relevant sub-topics, the evidence provided on the discussion and the analysis provided. 

The difficulty of the marketing value assessment. The marketing profession is a difficult one, with challenges related to demonstrating the value of marketing efforts in a product line’s sales profit and growth, as well as the misconceptions surrounding their profession and the primacy of other company functions, such as finance (Zorn, 2004). The concern for Hanssens and Pauwels throughout this article is why marketing is so difficult to assess.  Providing reasons for this, they bring several root causes for this main point:

  • The relationship between metrics is not well understood. For this they presented evidence from research work that indicates that metrics are usually moving in complex relationships that are often nonlinear and poorly correlated. They mention specifically research by Stahl et al. (2012) on product differentiation, which tends to be related with higher profitability and at the same time lower customer acquisition and retention rates (Stahl et al., 2012).
  • The difficulty of understanding the connections between metrics makes it difficult for researchers to consolidate the accumulated knowledge base, and even more difficult for companies to select metrics that are meaningful for their objectives.
  • Connecting metrics is important for a thorough understanding of the value of marketing and understanding the profit function as a multivariate or elastic function (Hanssens and Pauwels, 2016)
  • Reaching the goal of assessing marketing value correctly requires good performance metrics, causal links between the metrics, and communication of the analysis done on these linked metrics. This is the essence of the rest of this article discussion.

The influence of marketing objectives on marketing value metrics. The importance of this main point is directional. Companies need to assess their needs and expectations from the results of marketing efforts. To accomplish this, marketers need to be clear in four main aspects from the marketing effort perspective:

  • Reconciling different marketing objectives.  It is important to recognize that increased profit is not the only objective of a marketing campaign.  Multiple objectives may need to be accomplished in marketing that might be contradictory. For instance, Hanssens and Pauwels bring evidence to this point from Natter (2007), who performed a case study analysis that describes the need for the reconciliation of multiple objectives. The study focuses on a retailing company (name not disclosed), where a profit optimization campaign needed to be modeled considering the impact of price changes on demand against volume-based discounts provided by suppliers. Looking at both components of the equation, the volumes and price combined function resulted on the optimization of the market share objective of the company. 
  • Effectiveness and efficacy. There is a significant difference between effectiveness and efficacy in marketing campaigns. Often you can’t get both; for instance, a campaign based on social media might be efficient, less costly, but may not be effective due to limited access of social media for some customer sectors. On the other hand, a broad public campaign should be very effective in customer contact but is more expensive (Hanssens and Pauwels, 2016).
  • Defining the scope of marketing. This sub-point example comes from work done by Webster, Malter, and Ganesan. (2003), where they discuss the impact of marketing organizations around their stiles of marketing scope. Marketing scope can be limited or broad. Limited scope refers to marketing campaigns that are prepared by small marketing organizations, rather simpler approaches like an advertising campaign. This usually occurs in organizations that are small, subsidiaries of bigger companies, engineering product focus or business to business companies.  The broad marketing scope is the big marketing function of companies that consider the marketing function a profit driving operation center. These are mainly big corporations with significant consumer focus; the example or evidence provided by the authors is Procter & Gamble and Diageo (Pauwels, 2014).  The authors acknowledge that most companies fall in something in between.
  • Marketing Budget vs. Allocations. The relationship between these two financial concepts greatly impacts an organization’s marketing function. The analogy given is that for a CEO the CMO budget of 100 MM is an investment.  For the CMO team the distribution of those 100 MM between the function groups represents an allocation, which is used to drive the tactical aspects (Mantrala, Sinha, and Zoltners, 1992). Quantitative evidence is brought through studies like Sorescu and Spanjol (2008), who performed an empirical study on the effect of product and process innovation, both strategic actions, and their effect on company growth.

Methods and Findings About Assessing Marketing Value.  The analysis of marketing is a science, it is currently looking into elasticity functions and relational functions that permits a meta-analysis of the market information collected from adequate metrics. The authors brought a progressive presentation of the deep analytical tools they and other authors have developed. The following sub-points describe the analytical methods used by mostly academic authors.

  • Methods, models, surveys and experiments.  The market impact of marketing campaigns can be assessed using two types of data: primary data, which is the data collected in empirical testing methods such as surveys and experiments. And secondary data, which is the historical data available from the different market analysis metrics (Hanssens and Pauwels, 2016).  The two types of data differ on the depth of inferences that can be drawn from them. Primary data is useful to establish causal relationships between the independent variables (metrics) and the dependent variable, which is the marketing study’s subject (profit, sales, market share, or other).  Relational information can be drawn from experiments. In addition, primary data collected from surveys can provide the information on “the reason why” (Hanssens and Pauwels, 2016).  Secondary data analysis is readily available, as recent advances on statistical software, on descriptive statistics techniques and multivariate analysis techniques have made the interpretation of data more accessible and faster than the collection of primary data (Hanssens, 2014). According to Hanssens and Pauwels (2016), the best information about markets’ aspect of interest will come from a study that combines both primary and secondary data. Evidence of this comes from a study performed on the furniture company Inofec (Wiesel, Arts and Pauwels, 2011), where the authors performed initially a metrics-based model simulation combined with an experimental design that provided final optimized conditions for marketing channel mix between two options, paid search and direct mailing.
  • Findings on marketing investments and allocations. Further insight is provided on the impact of budget process from the strategic perspective and tactical execution.  When budget process is seen in a strategic way and marketing is seen as an investment, the usual intended result is to have profit growth. The analysis of model’s elasticity reveals that actions on the realm of strategic perspective, such as innovation and differentiation of brand and customer assets, will produce organic growth. On the other hand, tactical actions, like advertising and price promotion, will contribute little to organic growth but will promote profit improvements (Dorfman and Steiner, 1954, Hanssens and Pauwels, 2016, table 3, p 180).
  • ●      Connecting and integrating soft and hard metrics. Having an integrated perspective of the market information domains of soft aspects (attitudinal metrics) and the hard aspects (behavior and performance metrics) shall result in excellent model tools for marketers.  Few studies were identified by Hanssens and Pauwels on their article. Evidence provided includes a study by Pauwels and Van Ewijk (2013), who performed an analysis of the effect of online behavioral metrics and attitude survey metrics on brand sales.
  • Dealing with risk. Knowledge of marketing value elasticity variables promotes the CMO’s ability to predict and therefore learn how to deal with risk. The use of well-developed models can help the CMO project critical financial metrics out of market data under different economic circumstances, modeling them and returning with recommendations to the CEO for data-based decision making (Pauwels, 2014).

Communicating marketing value within the organization.  The fourth main topic discussed by Hanssens and Pauwels (2016) on this article were the dimensions of communicating the value of marketing to an organization. The evidence provided proves that there are three different perspectives on the communication process that marketers need to understand and implement.

  • Communicating marketing objectives in data dashboards. Communication is key to business success and key metrics are usually established to communicate indicators of business performance. Business data and the use of performance predictive metrics, including those that assess marketing, should be an aid rather than a menace to marketers’ jobs, as people and data have complementary usefulness (Blattberg and Hoch, 1990). Analytical marketing dashboards are an excellent tool for communicating key marketing metrics that take into consideration the data and human intelligence (Pauwels et al., 2009).   Dashboards enforce consistency by helping to monitor performance, goals and strategic planning, and can be used to communicate with important stakeholders.
  • Adapting communication to the style of the decision maker. Dashboards can be adapted to the decision maker’s style, for instance, analytical decision makers can be delighted by models and elasticity relationships. On the other hand, an intuitive decision maker can adapt better to data presented with visual aids like charts or graphs (Hanssens and Pauwels, 2016). 
  • Adapting communication to the marketing organization. This topic is a matter of inclusion, where decision makers need to consider the voice of the shop floor resources, in this case the market analyst.  An example of a case study evidence was brought to discussion, specifically how a venture capital company uses a model algorithm in their board of director’s decision making. Curiously this article mentions that the company named the algorithm as a member of the board of directors (Wile, 2014).

Hanssens and Pauwels’ article was structured with a robust and logical order of information that deals nicely with, first, establishing an understanding of the problem of assessing the value of marketing, followed by the perspectives of the metrics that better suit the marketing objectives, how to model them and ending with the communications strategies. They showed a clear progression of information for each main topic. They presented their arguments on each topic, evidence and examples, and provided a structured and substantiated analysis of the topics. The authors make a good progression of concepts, moving from the simple metric use to the more complex modeling interpretation of them. The communication aspect was a great complement to the very dense data-driven discussion in the modeling section.


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