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Artificial Intelligence Marketing Matrix

 Nowadays, there already is a multitude of potential applications for marketing based on artificial intelligence. These potentials can, in principle, be

subdivided into the dimensions “automation” and “augment” as well as on

the basis of the respectively associated business impact. In the case of the

augment applications, it is especially a matter of intelligent support and

enrichment of complex and creative marketing tasks that are currently still

performed by human actors. Artificial intelligence can, for example, support the marketing team in media planning or in the generation of customer insights (see the practical example  “The Future of Media

Planning—AI as a game changer”). First and foremost, the augment potential is already more strongly developed in those companies that reveal a high

degree of maturity in the AI maturity model. Planning and decision-making

processes are also supported or already performed here by artificial intelligence. With regard to the automation applications, it is hardly surprisingly

noticeable that with them, both the degree of maturity and the distribution

are significantly more developed in comparison. There are many automation

applications, for example, that already have a high degree of maturity and

use in practice today. This includes marketing automation or real-time bidding, for example (Fig. 3.11).

There are, however, applications that are used comparatively little in practice today despite their high degree of maturity and high business impact.

One area of application this phenomenon applies to is the principle of

lookalikes that can be used for lead prediction and audience profiling. In the

B-to-C field, this can easily be put into practice with Facebook Audiences

(https://www.facebook.com/business/a/custom-audiences).

This principle can also be easily applied in the B-to-B area (see practical example Sect.“Sales and Marketing Reloaded—Deep Learning

Facilitates New Ways of Winning Customers and Markets”). Behind this

is the possibility of strategically identifying new potential customers on the

basis of the best and most attractive key accounts of a company, who are

similar to the key accounts in such a way that it can be presumed that they

are likewise interested in the company’s products.



The way it works is easy to understand: Customers—in the B2B area,

these are companies—can be characterised on the basis of various aspects.

Besides classical firmographics such as location, business sector and the

company’s turnover, these also include information about their development, digitality and their topical relevance. In times of big data, this enormous amount of information can be mainly acquired from the companies’

presences on the web, because every day, up-to-date posts about new prod-panies that have the same DNA—the so-called lookalikes—can be identified 

ucts, changes within the company as well as on other subjects are published on the website and on social networks. On the basis of these aspects,

all companies can be characterised comprehensively, on the basis of which

a generic customer DNA is generated. In a subsequent step, further companies that have the same DNA—the so-called lookalikes—can be identified on the basis of this generated generic customer DNA. The result is a

pool of potential new customers, the approaching of whom offers promising

opportunities.

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