“Machine learning advertising is like sarcasm: everyone talks about it, but few understand”.
We used to think that neural networks are something that will trigger the rise of gadgets. Some suppose that all tech revolutions need to be banned because “There’s no way a microwave would teach me how to live!”.
To say it very broadly, this is true, because AI and ML already determine our reality. But instead of the microwave, we’re assisted by Google, Siri, Cortana, and Alexa.
Predictive analytics software solutions can anticipate equipment breakdowns in production and the behavior of retail customers. Moreover, they make logical and analytical decisions based on a raft of information almost like people. That’s why the trained algorithm is broadly used by marketers.
Today we will figure out how exactly ML is helping digital marketing and ads.
The Dream of Perfect Advertising
Surprisingly, machine learning and advertising are closely intertwined. Marketers use smart algorithms to find identical user actions performed in a browser. This helps them anticipate the behavior of other users and promptly adjust advertising.
All of this is about the ideal approach to relationship building, which is based on a great deal of information. It includes socio-demographic data such as gender or age, interests, website actions, client’s browser and device type, and location. These findings are used anonymously to assess promotion success.
Machine Learning Models for Advertising
- Regression. It’s a prediction of future numerical values based on information obtained in the past. Used for ads’ budget forecast, conversion probability.
- Clustering. The division of similar objects into clusters, dissimilar to each other. Used in machine learning targeted advertising by interests, music genre, and so on.
- Finding association rules. Finding patterns in a dataset. Used in dynamic remarketing, cart recommendations.
Why is Machine Learning Powerful in Digital Marketing?
In short, trained algorithms are all about data applicability and quick decision-making.
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The marketers’ routine looks as follows: they frame hypotheses, test them, assess, make a deep dive into the campaign. It’s a long, boring, and sometimes incorrect process because the information is extremely volatile and is updated every second. When using ML, data evaluation takes place in a matter of minutes.
Thus, a specialist can quickly react to transformations in the quality of traffic generated by advertisements and get rid of the monkey job.
Moreover, machine learning in online advertising increases the value of the analysis results. As long as they are manually analyzed by a marketer, the data becomes outdated and their value inevitably rolls downward.
In a word, algorithms process hundreds of requests, systematize them and provide results in the form of a ready-made answer to a question. That’s how advertising is using machine learning.
Using ML in Promo is Nothing but a Successful Strategy. Why?
Companies willing to use machine learning in digital advertising are already winners. And here’s why.
- Algorithmic ads are effective in 90% of cases
It’s always difficult for a person to process a huge amount of data. Any marketer wants to analyze everything, but often he works amid a tight budget and has to favor certain information. Prioritization can trigger frugal data volume and ad campaigns of low effectiveness.
Companies are aware of these problems and frequently turn to ML to eliminate the factor of human faults. After all, misconfigured advertisement not only annoys users but also reduces brand loyalty and can keep it off the radar.
Computer algorithms, on the contrary, provide more accurate audience-friendly recommendations.
Moreover, it’s easier for AI to keep up with the times and embrace all changes in the market because digital technologies are developing along with human progress (btw, even faster).
These factors help machine learning as targets to be as relevant and impactful as possible.
- Campaigns become more accurate and personalized
The user’s attention to personalized ads is significantly higher than to ads in general. Its essence is to give information relevant to the audience. This can radically alter ad significance and its ROI.
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To hit the interests of customers, you again need to process a huge data flow: region, weather, time zone, interface language, user browser, operating system, previously viewed videos, searches, and so on.
You must admit that doing it manually is difficult as hell. This is better done by ML, which tracks all points of interaction with clients.
Moreover, having analyzed large amounts of data, ML can reproduce correlations between advertising and audience that the human brain is not capable of. Roughly speaking, AI can determine that young people who enjoy rock and figure skating at the same time will download an app or buy a certain product or service. In any case, without ML, it will be difficult to achieve effective personalized advertising.
- Machine deep learning significantly reduces costs
AI in digital marketing can maximize results for every dollar you spend on advertising. With the boosted effectiveness of an ad campaign, machine learning makes a return on ad spend beneficial.
- ML triggers unique and high-quality content, evolving digital advertising
Projects have long been using neural networks to create unique content. Algorithms are used to create landing pages, texts, and videos, as well as to develop advertising materials.
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Algorithmic copywriting is one of the brightest machine learning advertising examples. Chase bank uses the Persado AI service to write advertising slogans. Surprisingly, when inquiring people, most of them found slogans written by algorithms more attractive than those written by copywriters.
Furthermore, data-driven machine learning can help develop more creative visuals for display advertising campaigns. Artificial intelligence analyzes everything from fonts and colors to images and button sizes.
For example, the Bidalgo project offers a tool that checks visual media to determine which creative factors will lead to the greatest reach for target audiences. Based on the given recommendations, you can optimize your pics or footage in a video editor online in real-time so that the advertising campaign has a raving success.
- Machine learning algorithms help to find distribution channels for advertising
AI is now involved in a seemingly very interpersonal and delicate process – working with influencers and brand ambassadors. For example, ML has already helped the Mazda auto manufacturer find bloggers to promote the launch of the new model. The computer analyzed popular profiles, highlighting those that best suit the company’s values and style.
Useful Tools and Solutions to Boost Your Ad
Here’s the list of machine learning apps that are almost like a white knight for every marketing specialist.
- GumGum. A program that analyzes posted online content and helps you choose the best website place for advertisement.
- WordStream. A tool for evaluating the effectiveness of campaigns launched in Google Ads and Facebook. The platform analyzes the content and recommends using other keywords, landing pages, and so on. The number of checks is not limited.
- Phrasee. A platform to help you optimize your brand language. In other words, AI adapts to the marketing language and tone of the company and creates a huge variety of ads, emails, newsletters, and so on.
- Pattern89. A solution that uses AI to predict the success of Facebook and Instagram ads before launch. It’s important to note that the program analyzes everything from the creative used to the targeting parameters.
- Beam.City. The project uses AI to analyze an advertising campaign in all channels, automatically configure campaigns, monitor and optimize them. Algorithms help you find the ideal consumers for subsequent targeting, which makes it easier to understand your audience.
Not Only Marketing: What are Other ML Areas of Application?
#1. Production
Neural networks help minimize the risk of production downtime, which can cost a plant millions of dollars. So, ML collects data from the sensors on the equipment and analyzes what indicators trigger failures. In the future, using this information, experts can predict when and why a downtime will happen, and how to avoid it.
Moreover, AI is effective in managing any kind of production. With the help of sensors and trained algorithms, you can reduce the percentage of defective products; optimize individual steps so that they take less time and effort; use fewer materials for production, which means lower costs; and automate individual stages of production.
#2. Finances
Most often, managers use smart algorithms to assess a client’s creditworthiness. All this helps banks to automate the issuance of loans and avoid the risk of stumbling into insolvent clients.
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ML is also very effective in fraud detecting. Financial organizations regularly lose money due to fraudulent transactions. Special algorithms help to recognize such operations – they learn to detect signs of suspicious operations and block them in time.
#3. Medicine
According to multiple experiments, AI significantly improves customer service in medical centers. The faster the registration process at the clinic goes through, the fewer queues it triggers, the more convenient it is for doctors to work and the less irritated patients they have.
Moreover, the use of ML in medicine means effective disease diagnoses. If you load examinations into the program, it can be taught to make diagnoses in the same way as doctors do. Also, with the help of technology it’s easy to spot anomalies and diseases earlier. Once detected, the patient outcomes can be improved significantly.
#4. Retail
Ecommerce AI solutions can predict customers’ behaviors: determine who will purchase soon; understand who prefers particular products to recommend them; offer personalized discounts to push up sales. Moreover, ML can level up retail businesses offering smart analytics and NLP solutions.
#5. Logistics
Fuel is one of the main costs in logistics. Machine learning can help you cut expenses by optimizing routes or figuring out how to reduce the number of cars while maintaining productivity.
Machine learning helps to avoid supply disruptions as well. Algorithms predict risks on the way, help prevent them, and adjust delivery times, taking into account all factors.
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Wrapping up
Machine learning has a very positive outlook. In 2020, 34% of companies in Europe, the United States, and China have used artificial intelligence and machine learning. According to experts, the machine learning market will grow by 42% by 2024.
Artificial intelligence and machine learning are already tightly integrated by startups and market juggernauts into most marketing tools. No advertising system functions without smart algorithms. They significantly increase the effectiveness of online advertising, makes the systems convenient and useful for users, facilitates the work of advertisers, helping to solve complex and perform routine tasks, and at the same time takes into account many factors for decision-making that a person cannot take into account.
Also, thanks to the accurate processing of big data, machines come to the rescue when information needs to be analyzed in reputational risk management or environmental analysis. Given that AI is not slowing down, it can be expected that technology will increasingly accompany marketing campaigns and machine learning companies will develop more tools, allowing you to save money and promote your services more effectively.
Yet wondering whether to integrate ML into your working processes? Don’t doubt: ask for advice from a machine learning consulting and AI company. The correct answer won’t keep you waiting!
Author Bio
Brian Jarvis is a digital marketer with more than 10 years of experience. He is a contributor to Content Marketing Institute and regularly quoted as an expert in large media outlets. His job is to make business known all over the Internet.
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