Skip to main content

The Right Use of Ai Algorithms in Marketing

 As suggested by the afore-mentioned negative examples, certain risks are

lurking in the background for companies that use algorithms in marketing.

It is thus essential for companies to fully understand the algorithms applied

and their limitations and for the algorithms to be used wisely. In addition,

algorithms have to be supervised and controlled so that they are in harmony

with the principles of the company and the image of the brand.

Another aspect is the ever-increasing concerns of customers regarding

their privacy, which can arouse mistrust of the use of algorithms. If the

customer sees too much personalised advertising, this can be perceived as

creepy, especially if the advertising is based on very deep insights into private information. This is also called overkill targeting and can reduce the

success of the marketing strategy, The creepiness that the customer can

experience emerges from an imbalance in the distribution of the information. The company advertising knows more about the customer than the

other way round.



Companies also need to be aware that by the collected and analysed data,

they have an advantage over the customer and can thus manipulate and

misguide their perception. If consumers are only shown pre-sorted information, they have no chance of obtaining an overall view. There is thus the

risk that individuals exploit algorithmic marketing without heeding any ethical aspects. For the trust of the customer to be gained, the marketers must

ensure that the algorithms adhere to the codex of digital ethics and privacy,

and observe manipulation and selection of information as well as communication behaviour.

For a successful application of algorithms in marketing, it must also be

considered that not all factors are analysed in context. The customer’s mood,

the weather or the presence of other people, for example, can influence the

customer’s purchasing behaviour. For this reason, an algorithm should con-

tain as many variables as possible but also elements of surprise and chance,

in order to not be too predictable. Another disadvantage of algorithms is

that they are often restricted in their ability to analyse why a customer made

a certain decision. As suggested by the afore-mentioned negative examples, certain risks are

lurking in the background for companies that use algorithms in marketing.

It is thus essential for companies to fully understand the algorithms applied

and their limitations and for the algorithms to be used wisely. In addition,

algorithms have to be supervised and controlled so that they are in harmony

with the principles of the company and the image of the brand.

Another aspect is the ever-increasing concerns of customers regarding

their privacy, which can arouse mistrust of the use of algorithms. If the

customer sees too much personalised advertising, this can be perceived as

creepy, especially if the advertising is based on very deep insights into private information. This is also called overkill targeting and can reduce the

success of the marketing strategy, The creepiness that the customer can

experience emerges from an imbalance in the distribution of the information. The company advertising knows more about the customer than the

other way round.

Companies also need to be aware that by the collected and analysed data,

they have an advantage over the customer and can thus manipulate and

misguide their perception. If consumers are only shown pre-sorted information, they have no chance of obtaining an overall view. There is thus the

risk that individuals exploit algorithmic marketing without heeding any ethical aspects. For the trust of the customer to be gained, the marketers must

ensure that the algorithms adhere to the codex of digital ethics and privacy,

and observe manipulation and selection of information as well as communication behaviour.

For a successful application of algorithms in marketing, it must also be

considered that not all factors are analysed in context. The customer’s mood,

the weather or the presence of other people, for example, can influence the

customer’s purchasing behaviour. For this reason, an algorithm should contain as many variables as possible but also elements of surprise and chance,

in order to not be too predictable. Another disadvantage of algorithms is

that they are often restricted in their ability to analyse why a customer made

a certain decision.


Comments

Post a Comment

Popular posts from this blog

Possible Limitations of AI-Based Bots

 The examples above already show the present-day potential of AI-based bots. At present, these systems are still in an early stage and still have certain limitations and potentials for optimisation. Twitter Bot Tay by Microsoft Most bots at present are reactive service bots. Engagement bots that actively interact with the users as market and brand ambassadors go one step further. The most famous example here is the chatbot Tay by Microsoft. Microsoft removed Tay from the web apologetically within one day. The example shows that the uncontrolled training of bots by the community can lead to fatal consequences. AI systems still have to learn ethical standards. It thus becomes apparent that even bots require a kind of guideline. Like a journalist has to observe editorial guidelines, bots have to observe certain standards. The next generation of AI-based bots must control and create the possible room for communication. IBM Watson has been able to celebrate quite a few respectable resul...
Post a Comment
Read more

What is Machine Learning

 The term machine learning (ML) as a part of artificial intelligence is ubiq- uitous nowadays. The term is used for a wide number of various appli- cations and methods that deal with the “generation of knowledge from experience”. The well-known US computer scientist Tom Mitchell defines machine learning as follows: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E (Mitchell 1997). An illustrative example of this would be a chess computer program that improves its performance (P) in playing chess (the task T) by experience (E), by playing as many games as possible (even against itself ) and analysing them (Mitchell 1997). Machine learning is not a fundamentally new approach for machines to generate “knowledge” from experience. Machine learning technology was used to filter out junk e-mails a long time ago. Whilst spam filters that tack- ...
Post a Comment
Read more

What is Data Protection and Data Integrity

 As a matter of principle, when it comes to data protection, a differentiation must be made between personal data and data involving companies. As soon as inferences can be made to a specific individual and single data levels are being worked at, a moment has to be taken to consider: What is being processed? Is there already a business relationship? Which permissions or legal consent elements are at hand? Customer data may not be collected without permission and may also not be resold. Anybody who acts carelessly here can quickly render themselves liable to prosecution. In principle, the following applies however: Almost anything is possible with the customer’s consent. This is the reason why Facebook can act with the data to such an extent, because consent has been given, even if only few users have probably fully read and understood the Terms of Use. Likewise, a relatively far-reaching data processing in the scope of an ongoing customer relationship under the motto “for our own p...
Post a Comment
Read more