One of the traditional definitions of marketing describes it as the activity of defining products and services offered by a company and communicating them to existing or potential customers. This activity can be broken down into several streams that are typically described as variations of the following categories [McCarthy, 1960]:

Product – analysis of marketing opportunities, planning of product lines and product features, assortment planning.
Promotion – all methods of communication between the company and its customers: advertisements, recommendations, customer care, and others.
Price – pricing strategies, including posted prices, price discounts, and price changes over time.
Place – historically, this refers to the process of making a product or service available to the end user through various distribution channels. More recent interpretations emphasize the role of product discovery and convenience to buy, with the argument that distribution is becoming less relevant with the rise of digital marketing channels [Lauterborn, 1990].

This categorization is well known as the marketing mix. The mix can be viewed as a set of variables that a marketing manager or marketing software can control to influence the position of products and brands in the market. Each component of the marketing mix represents a broad area that can be viewed and studied from different perspectives. The subject of algorithmic marketing can be better understood by distinguishing the following two aspects of marketing activities: strategy and process. We use the term strategy to label long-term top-level business decisions that define the value proposition of the company and set the overall direction for its marketing processes. For example, a retailer has to define its target market, customer services, and product lines as parts of the business strategy. The process is an implementation of the strategy that focuses on tactical decisions that support continuous functioning of the company. Continuing the example with a retailer, high-level pricing and promotional strategies require numerous decisions about how to select consumers for promotional campaigns or how prices for individual products should change over time.

Although the scope of neither strategy nor tactical processes can be rigorously defined, and there is no clear boundary between these two counterparts, we can argue that the strategy side is more focused on exploration, analysis, and planning involving human judgment, whereas the process side is more focused on execution, micro-decisioning, and, most importantly, automation. This makes the process side of marketing especially attractive for our study, although both strategy and process can be described from the viewpoint of data science and clearly benefit from data-driven methods. The short summary is that the subject of algorithmic marketing mainly concerns the processes that can be found in the four areas of the marketing mix and the automation of these processes by using data-driven techniques and econometric methods.

The definition of algorithmic marketing

We define algorithmic marketing as a marketing process that is automated to such a degree that it can be steered by setting a business objective in a marketing software system. This implies that the marketing system should be intelligent and knowledgeable enough to understand a high-level objective, such as the acquisition of new customers or revenue maximization, to plan and execute a sequence of business actions, such as an advertisement campaign or price adjustment, with the aim of achieving the objective, and to learn from the results to correct and optimize the actions if needed. This basic principle is illustrated in Figure 1.1. In this book, we also use the term programmatic to refer to highly automated marketing software systems and services, and the terms algorithmic and programmatic are used interchangeably in most contexts.

Figure 1.1: A conceptual view of the algorithmic marketing ecosystem.

Although it would be ideal for a programmatic system to be perfectly automated and autonomous, we do not consider this a principal goal or design requirement. On the contrary, a programmatic system is typically maintained by many people, including data scientists, engineers, and analysts, who develop and adjust models and algorithms to improve the system’s efficiency and capabilities. It can also consume the outputs of strategic analysis and planning done elsewhere with nonprogrammatic methods and, possibly, in connection with some other problems. However, the system’s ability to understand the business objective and work through the entire process from the objective to measurable results is essential. Again, it is important to keep in mind the limitations and perils of automation in marketing. In many real-life applications, it is more appropriate to view programmatic systems as intelligent tools that enable marketers to efficiently achieve what they want, rather than as their replacements.

To be continued