AI in marketing decision-making – benefits and examples
The benefits
The benefits of applying AI to strategic marketing decision-making are expected to include these:
· Increased speed of decision-making, especially in response to new data being available or competitive threats emerging, allowing companies to capture the benefits of stronger market positions earlier.
· Identification of missing data.
· Increased rationality, particularly via removal or reduction of cognitive bias by decision-makers.
· Creation of a common basis for decision-making.
· Incorporation of learning from experience.
· Higher quality management of marketing projects.
Examples of application
In this section, we consider some of the ways in which AI might be used in different areas of marketing decision-making and planning. It is based on the analysis framework proposed by Stone and Woodcock (2014) for analysing the impact of digitalization but applied to AI. A similar matrix needs could be developed for corporate strategy. There is not always a clear separation between marketing strategy and business strategy. Nor is there a clear separation between overall marketing strategy and strategy for different elements of the marketing mix. For example, decisions about marketing channels have strategic aspects (Stone et al., 2002) as well as tactical aspects, particularly as digital approaches change the balance between channels and the roles that different channels play. These decisions can affect every aspect of marketing.
However, Tables I-III present an attempt to show the main changes that digital marketing has made to different marketing activities, as digitalisation of marketing is a precondition for the data to be made available for AI to be deployed, and examples of possible AI deployment. The tables cover in turn marketing strategy, marketing mix and marketing management.
The benefits
The benefits of applying AI to strategic marketing decision-making are expected to include these:
· Increased speed of decision-making, especially in response to new data being available or competitive threats emerging, allowing companies to capture the benefits of stronger market positions earlier.
· Identification of missing data.
· Increased rationality, particularly via removal or reduction of cognitive bias by decision-makers.
· Creation of a common basis for decision-making.
· Incorporation of learning from experience.
· Higher quality management of marketing projects.
Examples of application
In this section, we consider some of the ways in which AI might be used in different areas of marketing decision-making and planning. It is based on the analysis framework proposed by Stone and Woodcock (2014) for analysing the impact of digitalization but applied to AI. A similar matrix needs could be developed for corporate strategy. There is not always a clear separation between marketing strategy and business strategy. Nor is there a clear separation between overall marketing strategy and strategy for different elements of the marketing mix. For example, decisions about marketing channels have strategic aspects (Stone et al., 2002) as well as tactical aspects, particularly as digital approaches change the balance between channels and the roles that different channels play. These decisions can affect every aspect of marketing.
However, Tables I-III present an attempt to show the main changes that digital marketing has made to different marketing activities, as digitalisation of marketing is a precondition for the data to be made available for AI to be deployed, and examples of possible AI deployment. The tables cover in turn marketing strategy, marketing mix and marketing management.