Marketing Models, Management Science & Decision Making

Experiences and insights into decision-making processes … and how to shed light in the forest of data

Just a few years ago, Gartner estimated that at least one-third of the one hundred largest global companies and organizations surveyed by Fortune were suffering from information crisis due to their inability to effectively assess and govern information, and predicted that “by 2023 80 percent of organizations will target data literacy competencies for development.”

Given the recent and accelerating explosion of information technologies and AI, we can imagine what the current situation might be, especially in small and medium-sized enterprises, which, alas, account for about 98 percent of total companies (not only in Italy!), and in which the problem is exacerbated by unfamiliarity with advanced information systems.

Never mind further considerations about the evolution of technologies, data and information: even a child would understand that making appropriate and timely business decisions based on the enormous mass of information and data available is becoming increasingly difficult for everyone.

I therefore think that sharing with you some thoughts and experiences on the topic of data and information management in marketing (except for an anomalous case!) may be useful to all of us, especially if I can stimulate an exchange of ideas with all those of you willing to contribute (my e-mail is below).

As you may have guessed from the title of this article, this series of some 20 articles will focus on decision making in marketing:

• marketing models, of which I’ll try to give an “operational” definition, serve precisely to make decisions based on the available information and data (objective or estimated, precise or approximate, exogenous or endogenous, predictable or unpredictable, controllable or uncontrollable, measurable directly or indirectly, or even difficult to measure), as well as on decision makers’ experience, intuition, and judgment
• management science, which is synonymous of operations research, (“ricerca operativa” in the few Italian departments of economics that realize how important it is, not just for engineers!) is the discipline that helps to address and solve problems, and then to make decisions accordingly, using qualitative and especially quantitative models, such as marketing models
• without forgetting that it’s not enough to be able to process and interpret data: it is especially critical to know what relevant data to collect, process, and interpret.

I will therefore make some room not only for “cross-cutting” topics with respect to different applications, but also for more “vertical” insights, precisely on relevant data to be collected in specific contexts: market share estimation and pricing decisions.

In any case, the subject is a tough one (not to say tragic, if you think of the situations mentioned above!) and you will forgive me if, here and there, I will indulge in some boutades and Pindaric flights tending toward the “scurrilous,” just to downplay!

Those of you who want to make sure they don’t miss any of the articles in the series can email me at the address below.

Enjoy the reading, hope to hear from you.

gandellini@nestplaninternational.com