At the latest after MMC Ventures realized that only 40 percent of agencies that claim to use artificial intelligence actually do so, the question arises: How far have we actually come with this topic?
{{divider}}
AI marketing needs a reality check
If you use Google Alerts to scan the frequency with which articles on this topic are published, we have made tremendous progress. But is this only on a theoretical basis? AI seems to have arrived in every area of business (and also in households). This is also true in marketing. But here too, the question arises: has it arrived in theory or in practice?
If you examine the use cases with a marketing mindset, the result is somewhat more subdued compared to the ongoing GoogleAlert alarm.
But let's take it step by step: The 21st century and digitalization have changed every aspect of life within society. Artificial intelligence, self-driving cars, and talking robots, which were once the stuff of science fiction movies, have now become reality, and global companies rely on them. All of these technical masterpieces have one thing in common: they need data to function properly.
Yes, the same old story about big data—but why again?
And why does the term continue to cause concern? When analyzed accurately, big data promises in-depth insights or even predictions about customer needs, trends, and the competition. While this offers new opportunities, it also increases complexity.
Marketers who follow content marketing (and who doesn't?) are faced with the task of producing large quantities of high-quality content that is still individualized and takes into account insights from big data. Since the purchasing cycle does not begin with a sales pitch, performance marketing must step in and design its (lead nurturing) campaigns to be demonstrably successful. Potential buyers actively engage with their needs and independently gather information—pull—and solution ideas.
{{divider}}
Fact Check AI Marketing: What You Need to Know
Artificial assistants such as Amazon's Alexa, Google's Assistant, and Apple's Siri are already playing a role in the business-to-consumer sector. Marketers at business-to-business companies are currently faced with the decision of how to capitalize on this trend.
And the "old" B2B challenge is throwing a spanner in the works.
Voice assistants work perfectly once they have been used for a while and are familiar with their users' everyday lives and way of expressing themselves. This usually requires a clearly identifiable user base. However, as a B2B marketer, the message should not only reach a target person or family who makes an individual purchasing decision, but must also meet the expectations of many decision-makers at the same time. In the business-to-business sector, purchasing decisions are usually made by several stakeholders —not only because of budget decisions—who also come from completely different departments and therefore exhibit different behaviors.
In order to reach each of these target individuals with relevant content, data streams must be bundled and interpreted correctly.
{{divider}}
Big data—now we're in a fine mess
For years, we have been "supplying" companies with business intelligence tools and all kinds of KPI metrics. SaaS providers have earned themselves a golden nose and repeatedly opened the door to upper management with the phrase: "You're not measuring KPI XYZ yet? But you should be! It will provide you with insightful insights!" This always seemed to make sense, especially to management, but now the time has come to ask what should be done with the data (/rare insights). However, the fun of data analysis comes to a halt when it is realized that the amount of data has grown infinitely and analysis seems nearly impossible. So what now?
The growing volumes of data generated by networked devices and consumer demands, coupled with technological progress, have logically led to the use of artificial intelligence. Globalization and digitalization have become the multipliers of this demand. With the help of artificial intelligence, the potential of data can be fully exploited. AIs are not afraid of big data and do not even bat an eyelid when the amount of data to be analyzed doubles—after all, pattern recognition by AIs makes big data manageable. The technology expands the scope of human expertise and can identify unforeseen problems.
Digression: Machine Learning
Machine learning—to be clear—is part of the field of AI and focuses on the ability of machines to receive data and learn for themselves. This works by changing algorithms and classifying them as correct once they learn more about the data they are processing.
{{divider}}
AI marketing: successful examples
The most successful examples of AI use in marketing today can be divided into four categories.
- Recommodation Engine meets Affiliate
- Hyper-personalized customer experiences
- Social media – not dead after all?
- Analysis of customer data – Insights
1. AI in affiliate marketing
Affiliate marketing is growing and supporting many bloggers. But what does the development of AI look like in this area?
AI makes it possible to refine affiliate marketing or even revolutionize this advertising technique. AI systems have the ability to understand the semantics of new, previously unanalyzed pages through self-learning and to place ads based on this information. The ability to analyze context also takes online ad placement to a whole new level.
Before advertisements are placed, the systems can analyze content from across the entire World Wide Web in parallel and obtain an up-to-date picture of public opinion. Based on this analysis, specific content is then displayed. The intelligent programming goes so far as to adjust parameters for specific times, locations, or events AND parameters based on user behavior. Displaying offers for umbrellas when it is raining at the user's location is still one of the "old" use cases. With AI, the appropriate color and perhaps a handle with initials embossed on it are also displayed.
2. AI and customer experiences
Customer experiences can also be optimized with AI. Among other things, this involves using chatbots to provide advice. The web enables customer and behavioral data to be collected in real time and can draw conclusions by stringing together algorithms. A bot serves to process these findings and offer solutions to the customer. The AI enriches the analyses it performs with experience it has gained from the population.
This also means that it becomes smarter with increasing use and can independently recognize current trends. A potential customer is thus able to obtain detailed information by using a bot before consulting or purchasing through human-to-human interaction. This saves time for buyers and streamlines the marketing and sales process for the company offering the product or service.
3. AI in social media marketing
Social media marketing attempts to leverage the social effect generated by networks. Companies use this "informal" atmosphere to anchor their brand in the minds of potential customers. Due to the different specifications of the networks, the content must be tailored to the customers.
For example, Twitter, the microblogging platform, only allows posts of up to 240 characters. These circumstances require social media teams to post specific content on each platform. This results in increased effort. With intelligent extensions, firstly, even simple content can be created automatically and, secondly, the perfect time to post messages can be identified automatically.
Social media AI solutions can create posts from existing content that are optimized for the respective network and promote them. The most important facts can be automatically generated in post form from interviews or blog articles, as AI algorithms can understand semantics. Campaigns can be launched automatically through the automated analysis of web traffic, internet trends, or events.
Severe weather warnings can be picked up by AIs and then independently converted into a post. For example, online shops can use meteorological data to promote certain products via social media platforms. Another area of application is complaint management via social media platforms. Intelligent agents not only recognize and understand text, but also images and logos, and can respond to them themselves. They can actively intervene and prohibit content or contain potential shitstorms with arguments.
4. AI Insights
With ever-increasing amounts of data, marketers find themselves in a position where they have more information than they can process. Coupled with growing demands and the desire for transparency in marketing success, the use of AI systems to understand vast amounts of data and draw conclusions from it is becoming necessary.
Through intelligent analysis and automatic evaluation, marketers gain access to new customer segments and information, giving them significantly more opportunities to build campaigns and strategies based on these specific conditions. The sequence of advertising measures also plays an important role. For example, some users are more sensitive to advertising banners at the beginning of a website visit than others, who are more interested in an immediate offer.
With the help of big data, it is possible to predict how a user will behave. AI can then be used to tailor a customer journey accordingly. Insights and analytics algorithms guided by AI make it possible to engage in one-to-one communication. Such scenarios are hardly conceivable when performed manually and with several thousand users. Nevertheless, companies invest a lot of energy in learning from customer journeys without considering AI systems.
The application of intelligent digital solutions is only slowly finding its way into practice, as there is a lack of best practice examples.
{{divider}}
Fact Check: 3 Takeaway Notes
- Firstly, artificial intelligence is capable of streamlining processes, promoting the exchange of information, and thereby providing significant support to marketing teams.
- Secondly, artificial intelligence can perform thorough analyses of customers and the environment at a speed that humans could not achieve. AI analyses do not end with a list of figures, but with tangible recommendations for action. AI systems solve the task of bringing all information together by accumulating data.
- Thirdly, the use of artificial intelligence can improve the overall performance of the department. Not only does it enable recommendations for action and individualization, but also the appropriate, automated execution of campaigns based on individual customer requirements.
With AI in marketing, every contact with a digital footprint can be advised individually and exclusively. Artificial intelligence is the right tool for organizing big data and making it usable, ultimately justifying investments in big data projects. Today's intelligent solutions are capable of operating in a specific area and sharing data with each other. The AI applications that have been designed are also stand-alone solutions that are connected by bridges and increase their effectiveness when combined. If we look at the few examples that AI marketing offers today, these are often only those campaigns that use the term AI as a driving force for their campaign.
As things stand at present, marketing staff remain the driving force and masterminds. Creative and strategic tasks in particular are (still) their responsibility. Collaboration between different teams or partners therefore remains a human endeavor for the time being. But we can rejoice, because the monotonous and repetitive tasks of pattern recognition are being taken off our hands. Ultimately, this will help us master big data and justify its introduction.
