The Application of Neural Networks in Marketing: Can AI Replace Traditional Marketers?

Additionally, neural networks are often assigned typical and monotonous tasks so that employees can focus on more complex goals that cannot be achieved without human involvement.

Neural networks are primarily utilized for advertising purposes. They help generate a lot of content and analyze the target audience based on more parameters than was previously possible.

By connecting the system to a database, it can deeply analyze user behavior based on Big Data. In 2023, business neural networks gather and study a vast amount of information that humans cannot handle.

Thanks to artificial intelligence, recommendation systems have emerged. For example, platforms like Kinopoisk, Netflix, TikTok, and others employ AI to offer users relevant and interesting content. Internet stores also create personalized advertising emails.

Texts. Services are capable of generating articles, comments, and social media posts. Moreover, marketing neural networks are valuable in providing the optimal structure for large texts if one is unable to do it themselves.

For example, let's take the Telegram ChatGPT bot, which now offers the free service of SMS activations called "Tiger SMS." Let's say we need a FB post on the topic: "How to Use Neural Networks in Marketing." We write to the bot in a free form, for example: "Hi, write a short FB post on the topic of using neural networks in marketing." And this is what the neural network suggests to us.

Not a bad result in terms of content and uniqueness, don't you think?

The key is to correctly formulate the query, describing the task details. If the prompt is too broad, the AI simply won't understand what is specifically required.

Here are a few example photos generated by the "Stable Diffusion" neural network:

Neural networks significantly simplify the execution of complex analytical tasks related to studying Big Data, object recognition in images, etc. For example, when connecting AI to a video surveillance system, it is possible to collect and analyze data on the attendance of sales offices, visitor satisfaction levels.

The neural network identifies patterns, draws conclusions, and predicts future events. Analytical models can predict product demand based on statistical data. AI performs technical analysis effectively and provides additional information that complements the conclusions of marketers.

Developers have created a special neural network for analyzing public opinion and comment sentiment in 15 languages - Luminoso. The only drawback is that manual information loading is required. The system can identify connections and compile information into diagrams. Based on the predictions of the neural network, it is possible to optimize the quantity of purchases, predict a decline in demand, and prepare a discount promotion.

In the future, neural networks for business will become automated tools for marketing research. Thanks to them, analytics will become significantly cheaper and faster.

Neural networks are used to optimize advertising campaigns. This task lies at the intersection of analytics, personalization, and content preparation. For example, the company  implemented neural networks to search for people wearing glasses on FB and sent them relevant offers. Thanks to AI, the company expanded its target audience by 4 times.

Neural networks simplify content production and analysis, allowing for the preparation of various data variations and conducting research at minimal costs.

Let's consider 4 popular projects that are actively used by companies to solve marketing tasks.

ChatGPT is the best project in the field of neural networks and artificial intelligence. It was developed by OpenAI, which has become a leader in the industry. Currently, ChatGPT has the best performance in text generation. It can mimic human speech, reason on philosophical topics, and create scientific papers.

The model learns speech during interaction with real people.

To use the service, registration on the website is required. There is a paid subscription that expands the functionality of the neural network and speeds up query processing.

DALL-E 2 is a neural network that generates images from text descriptions. It creates original artworks based on user input. The images resemble real-life paintings created by professional artists. Users can upload their own images and edit them using text prompts.

The key feature of DALL-E 2 is the ability to adhere to a chosen style. The neural network can create artwork in the style of Renaissance paintings, anime, or Russian postmodernism.

Users have access to 200 free image generations in the first month and 60 per month thereafter. To expand the tariff, they would need to pay $15 for 460 requests.

You.com is a new search engine that can answer any question using materials from the internet. It can engage in dialogue with users and provide detailed information on virtually any topic.

The first 10 texts or images can be created for free without creating an account. After that, users need to purchase one of the subscriptions starting at $10.

CopyMonkey  is a service for generating textual content. Initially, the platform was focused on e-commerce and created short product descriptions based on specifications. The developers later added more features, including support for multiple styles and the ability to choose specific text tasks.

Users can create 10 articles after registering for an account. To continue using the service, they would need to pay a subscription starting at 1599 rubles per month.

Neural networks are a promising technology that is now accessible to companies of any size, not just large corporations. Artificial intelligence can be used to optimize content production, information analysis, personalize advertising offers, and create reliable recommendation systems.

However, it is essential to check the generated content for plagiarism. The results of working with AI-generated data cannot be predicted, which can sometimes lead to images and texts that are nearly identical to those they were trained on. This is crucial from a copyright and SEO perspective.

Furthermore, it should be noted that neural networks cannot yet replace professionals. Companies still require strong marketers, copywriters, and artists.