Optimization is a mathematical principle — one that is too often misapplied in a marketing context.

The Merriam-Webster Collegiate Dictionary defines the word “optimization” to mean “an act, process or methodology of making something (a design, system, or decision) as fully perfect, functional or effective as possible.” It is a phrase aptly applied in disciplines such as mathematics or engineering, where perfect processes are theoretically achievable.

In mathematics, optimization refers to a technique for finding a maximum or minimum value of a function of several variables subject to a set of constraints. Such a formula is called an optimization problem and can be represented in the following way:

Given: a function f: A to R from some set A to the real numbers
Sought: an element x0 in A such that f(x0) ≤ f(x) for all x in A (“minimization”) or such that f(x0) ≥ f(x) for all x in A (“maximization”).

But somewhere along the line marketing professionals co-opted the phrase, declared it a buzzword, and, over time, began using it — often outside of its proper context. While there are indeed specific marketing scenarios in which the term optimization is apt and relevant, it should not be used to describe every process. Below, let’s take a look at the situations in which optimization is — and is definitely not — possible.

Optimization is for Mathematicians

To the extent marketing can ever be described as a science, it is certainly one of the social sciences. At its core, marketing, like psychology or sociology, is the study of human behavior. It is the application of past behaviors, experiences, and social mores as a means to successfully predict future behaviors.

The problem with treating marketing as a science, however, is that humans are, by their very nature, unpredictable. Thus, just because a concept worked once before does not mean it is guaranteed to work again. Indeed, humans tend to tire of repeated processes over time. This is antithetical to a discipline like engineering, where if a structural design results in a building that does not collapse on itself, that same design will likely hold up should it be constructed again within the same set of external variables. Consequently, “optimization” is not always the most accurate or constructive way in which marketers should refer to their methods.

When Optimization Can Potentially Work for Marketers

There are, however, specific areas where marketing analytics may achieve something akin to optimization. Prescriptive analytics simulate a multitude of predictions to find the most effective simulation within a given set. Unlike descriptive analytics (which involve understanding what happened in the past) or predictive analytics (which try to ascribe what could happen in the future), prescriptive analytics use existing data to advise on possible outcomes.

To use a simple example, let’s say your company has historically used two marketing channels: direct mail and email. Hypothetically, you could analyze the number and frequency of email campaigns against the number and frequency of direct mail campaigns. You could compare the costs of each type of campaign and monitor any change in sales volume or web traffic resulting from each. After studying the results of those two types of campaigns over time, your company might then be able to make an optimal decision about which type of marketing campaign should be used in the future. The keyword here, however, is “might.”

The Benefits of Focusing on Evolution

The truth is that the innate unpredictability of human behavior will likely break down even prescriptive analytics over time. For that reason, it can be beneficial for marketers to stop thinking and speaking in terms of optimization and turn instead to the concept of evolution. Marketing is not always a solvable equation, and the problems marketers seek to address often do not have straightforward solutions. They can only be analyzed and responded to in any given moment within a unique set of external variables — that’s what makes our jobs so interesting.

That said, great marketers can be brilliant scientists. They tweak. They respond. They evolve. They study trends, understand news cycles, and utilize history just enough to identify what resonates over time. They cannot always, however, make anything “fully perfect, functional or effective as possible.”

Optimization, in other words, isn’t always possible in marketing. It’s time to rethink the context in which we use this beloved buzzword and embrace the often murky but consistently fascinating reality of marketing in today’s fast-paced, hyper-digital world.

Written by Kevin Troyanos

I lead the Analytics & Data Science practice at Saatchi & Saatchi Wellness. I have focused my career within the healthcare marketing analytics space, empowering healthcare marketers with data-driven strategic guidance while developing innovative solutions to healthcare marketing problems through the power of data science. I’ve worked to measure, predict, and optimize marketing and business outcomes across personal, non-personal, digital, and social channels. I’ve led engagements with brands that span all stages of the product lifecycle, with a particular focus on established brands. My role is to guide the departmental vision and lead innovation initiatives, effectively positioning marketing analytics as a competitive differentiator and organic growth driver for the agency at large.

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