Although artificial intelligence does not replace human marketing teams, it does support employees in controlling campaigns or even in content marketing. In the future, AI could increasingly displace a popular means of expressing branding: emotions.
The AI threat is becoming a different game. Companies have decided to restructure a number of processes to protect against this perceived menace.
One change will be to localize marketing measures, even more, pushing to make the distribution of content production decentralized; accompanying this action by an organic reduction in personnel. Eventually, however, algorithms will replace only part of the marketing efforts.
Although the news about AI has far less impact than previously thought, AI will dramatically change marketing. A recent McKinsey study shows that regarding the various business functions in companies, there is the most significant potential of AI in marketing. It is believed that there will be close to $6 trillion in value-added potential. Interestingly, at present, there’s a minimal number of successful AI applications in marketing.
Artificial intelligence is an entirely autonomous, self-learning system.
Some believe that AI is still not a practical reality. Still, there are impressive examples like Google’s Alphago — a program for the board game Go. The program was able to beat multiple professional Go players, supposedly showing the increased potential of AI.
These AI programs are still based on a rather mechanical approach that “learns” diligently from large amounts of data. There are still some laboratory situations that have little to do with corporate reality.
The analogy of neural networks such as occur in the human brain is also misleading. We are quantitatively miles away from the neuron quantity frameworks and relationships of the human mind. We still do not know how the human brain works — but we think that we can build it?
Even though the AI approaches seem to pretend to be quasi-intelligent, they can sustainably support and change marketing strategy. From a business perspective, the dimensions of automation and augmentation ultimately matter. AI must be able to automate the marketing processes well, and AI must also be able to support and optimize all marketing decisions.
The AI marketing matrix.
A marketing matrix is the majority of AI applications, relate to the automation of marketing functions and processes. In this context, systems also make their own simple decisions.
These decisions are usually about the substitution of human activities by artificial intelligence (AI) to achieve cost and efficiency benefits. There are many automation applications that already have a high level of maturity and practical application today. These include, for example, marketing automation or real-time bidding.
In contrast, augmentation applications are particularly concerned with the intellectual support and enrichment of complex and creative marketing tasks, which are currently performed by people.
Intelligent support allows AI to analyze competitors, audiences, and trends automatically. Marketers can use these insights to develop or adapt their strategy. These insights enrich decision-making processes with relevant information. The actual decision is not automated and depends on the operator’s choice.
AI can also help marketers manage the increasing complexity of channels and touchpoints regarding augmentation.
Thus, both the value contribution of a channel and the necessary interaction of the channels to optimize the conversion can be calculated. Based on extensive customer journey data, the optimal media budget allocation can be determined over time. The final media plan including the organizational division of roles is then — at least today — created and evaluated by people.
Due to the greater complexity and creativity of these tasks, both the degree of maturity and the spread are significantly less pronounced compared to the automation examples by AI.
However, there are also applications that, despite their high degree of ripeness, are still used comparatively little in practice today. One area of application to which this phenomenon applies is the principle of lookalikes, which can be used to identify and profile audiences.
Collecting content on an analytical level.
Artificial intelligence can gather over about 10,000 data points on the Web about companies or consumers. Besides this massive amount of data, AI can determine and profile new target groups via deep learning algorithms.
In the B2C area, for example, this can be implemented well with Facebook custom audiences. AI is also playing an increasingly important role in content marketing. Algorithms help to semantic conceptualize content. For example, the word-to-vec algorithm automatically forms content as vectors that formalize the actual content abstractly. This representation is much more potent than the typical index of content. On this basis, automatically similar or complementary content can be found on an analytical level.
So-called “long-short-term-memory-recurrent-neural-networks” can also produce new content by predicting the nearest words and phrases, starting from words, considering the temporal context.
One example of these “neural networks” is the AI-supported creation of an issue of British marketing magazine The Drum. Thousands of copies were printed from the issue, with the AI selecting images as well as adapting texts and designing the pages. The AI was fed with data from the winners of the Golden Lion at the Cannes Lions International Festival of Creativity.
It was not just about creating the magazine, but also creating artificial intelligence that appeals to the taste of the lifestyle audience.
Based on such procedures, so-called robot journalism is becoming increasingly creative. Algorithms are capable of automatically searching the web for information, bringing it together and making it a readable piece of content. Data-based reports in the field of sports, weather or finance are often created automatically today.
In the area of newsletter marketing, AI can also help to derive the correct subject lines and headings in emails and newsletters.
Using this intelligence — AI can generate text modules that have the highest conversion probability for the respective target group. There are also solutions that automatically create and test visuals for campaigns according to the target group.
In addition to the described content creation, AI will also increasingly adopt content curating and distribution when it comes to combining and promoting. AI will automatically publish and distribute content on various platforms.
The “de-emotionalizing” of marketing.
Another marketing trend fueled by artificial intelligence is (chat) bots. The topic of bots is not new but is experiencing a unique quality and significance in the last two years.
This rise in popularity is due to the rapid development of AI, platforms, communication devices, and speech recognition. Network communication is increasingly shifting into the messenger and chatbot world of Whatsapp, Facebook Messenger, Snapchat and Wechat.
The onliners leave the digital public and are therefore difficult to reach for brands. They move in the “invisible” part of the digital world for others.
For example, they no longer share the content via Facebook news feed with everyone but limit themselves to sharing their content via messenger with a manageable circle of friends. Thanks to voice assistants, users can ask Siri or Google Assistant for the current weather, switch on the lights via Alexa, play music or read a message aloud.
The access of Apple to the Smart Assistant market with the Homepod will bring new usage scenarios and target groups armed with this knowledge base.
In addition to these simple everyday tasks, the systems are increasingly developing into a digital assistant and a virtual representation of the consumer. A digital assistant has an impact on customer communication and interaction. If the consumer selects his favorites from the hit lists on a Google search or an Amazon product search, the bot recommendation usually includes product or information.
The bot sovereignty thus replaces the active evaluation by the consumer.
While the current communication is still between consumer and corporate bot, it will be increased next year. Therefore, marketing activities have to be adapted to the bot channels. SEO and SMM will also have to rethink things. The so-called “Bot Engine Optimization,” or BEO for short, transforms the guideline “Rule the First Page on Google” into “Rule the first Bot Answer.”
The focus is on personalized one-to-one campaigns from bot to customer.
One consequence of the increasing use of these systems in marketing could be that emotional brand engagement loses its relevance and marketing becomes objectified. “Boo-hoo,” the marketers exclaim. But, the objectified customer occurs because purchase decision processes are now made more rational than before.
The logical immunity to emotion and empathy.
The development of smart homes or smart products leads to rational purchasing decisions — bots now increasingly represent “the people.” The refrigerator “decides” when milk should be bought. A digital representation of the customer is logically immune to emotional and empathic advertising, thereby losing its meaning.
The ideological brand value is becoming irrelevant.
The ideological value of the brand is irrelevant to the customer bot, who in the optimal case acts objectively through the digital signature of the customer, acting as his deputy in e-commerce. Thus, the companies and customers access to the platform becomes more important than the brand itself.
Conclusion
Big Data provides the fuel for artificial intelligence, which can be used successfully in marketing and communication in particular. AI can capitalize data from online interactions, social media or other digital sources in an automatable and scalable way.
Such a powerful weapon always includes the potential of abuse; the discussions show the market power of the so-called GAFA economy (Google, Amazon, Facebook, Apple). The algorithms allow for a never possible personalization of communication.
On the other hand — the new AI algorithms also allow, and possibly make — the risk of targeted and manipulative disinformation.
Corresponding regulations and ethical standards as well as increasing the media and judgmental competence of sovereign consumers are required here. And these former giants of social conscious may be woefully unprepared or inadequately prepared for monitoring something as formidable as AI.
Overall, increasingly data-driven and analytical marketing will have to answer the question of the right balance between automation and personal interaction. Besides, the corresponding implications for the consumer should be considered.
Will appropriate bot-power strengthen the consumer in the form of digital assistants who know and represent his actual preferences? Or, more likely, will AI become more of a match for a perfectly designed data and analytic ecosystem of digital behemoths?
