Unlocking User Potential with AI: How ChatGPT Analyzes Birth Dates to Reveal Talents and Personality Traits

"If you're not on the internet, you're not in business."

This phrase (attributed to Bill Gates) became something of a manifesto for companies in the 2000s. Just imagine, only 25 years ago, businesses had to PROVE how crucial it was to have an online presence.

If we were to create a modern equivalent of this phrase, it might go something like: "If AI isn't integrated into your business, you don't have a successful business."

Is there still anything left to PROVE today? Do we need to argue about the necessity of adopting modern technologies? It's as obvious as 2+2=4. Using AI is no longer a trend—it’s the foundation. Let’s look at the numbers: they’re far more convincing than any argument.

Key Statistics and Market Analysis  

72% of companies are either actively using or exploring the potential of AI within their organizations.

The question arises: why did we see moderate growth (and even declines) in the 2018–2023 period, followed by a “boom” in 2024?

In our view, this can be explained by several factors. To begin with, at the beginning of the period, there simply weren’t affordable, powerful GPUs available. By contrast, in 2024, we are actively leveraging scalable cloud solutions. Additionally, the cost of implementing conversational tools discouraged small and medium-sized businesses - they could barely afford such investments. Workforce shortages also played a role.

But most significantly, there was skepticism and conservatism among both individual business owners and the public. From managers fearing that “an AI chat platform will replace me at my job” to statements like “we won’t let machines take over the world,” hesitation was widespread.

What, then, was the turning point?

Why did AI adoption become so widespread and essential in 2024?

GPT-4 and other generative AI tools provided businesses with access to natural language processing, content generation, and data analysis tools, dramatically increasing their appeal. At the same time, the cost of cloud solutions decreased (thanks to growing competition among providers such as AWS, Google Cloud, and Azure), making artificial intelligence more accessible to small and medium-sized businesses. Additionally, the market benefited from the readiness of infrastructure (the development of high-speed internet, 5G, and IoT).

Perception has changed. There is now a clear understanding of the benefits of conversational AI. We’ve seen real returns from using AI software for business: automating routine tasks, personalizing marketing, and increasing the accuracy of analytics. Great examples were set by giants like Google, Amazon, and Microsoft, inspiring other companies.

In 2024, many governments simplified laws for AI development, introducing clear regulatory standards, which reduced businesses’ fears of legal risks. However, the legal aspect remains complex and controversial (but we’ll discuss this below).

It is expected that the global AI market will reach 1,85 trillion by 2030.

Current Capabilities of Generative AI and Key Areas of Application

As expected, generative models are most widely and extensively used in marketing and sales. In second place is product and service development, and in third place is IT. 

Therefore, in this overview, we will also focus primarily on the use of artificial intelligence and machine learning in the context of marketing activities and the e-commerce sector.

Chatbots

AI apps to talk to bots, imitating human communication, have become one of the most important tools in e-commerce and beyond. They can significantly enhance customer interaction, increase sales, and improve business efficiency. Features of conversational AI:

• CRM Integration: automatic processing of customer data.
• User Behavior Analysis: gathering analytics to improve products.
• Multiplatform Capability: functioning across messengers, websites, and apps.

All major market players use chatbots. For example:

• American Airlines: assisting passengers with booking tickets.
• Bank of America: providing account information.
• Amazon: helping customers find products, place orders, and track delivery.
• Government agencies: scheduling appointments for citizens, etc.

There is only one rule for using chatbots: if your business is directly involved in working with people, you need a chatbot.

Voice Models

Conversational AI software has also found extensive and active use in:

• Voice Assistants for system management and executing commands, reminders, or information retrieval, i.e., simplifying routine tasks.
• Interactive Call Managers. AI can answer calls and record customer requests, or conduct calls through a database, switching to an operator when needed.

Example: In telecommunications companies, conversational AI services are used for hotlines, allowing for the optimization of operator workloads.

Personalized Product Recommendations

Customer engagement AI solutions analyze data on purchases, preferences, and user behavior (clicks, views, items added to the cart). Using this data, the models can predict which products are of interest to a specific user.

Applications:

• Dynamic Product Selections: AI generates recommendations in real-time based on the user’s recent actions on the website. For example, Amazon’s AI suggests products based on the user’s browsing history.
• Personalized Email Campaigns: Generative models can create personalized email content with product recommendations tailored to the customer’s interests.
• Bundled Offers: AI analyzes relationships between products and suggests additional items that may be needed (e.g., accessories for a previously purchased phone).

This is an excellent way to increase conversion rates through relevant recommendations and boost average order value through cross-selling.

Dynamic Pricing

Generative models process a variety of factors, including demand, competitor pricing, user behavior, and seasonality, to propose the optimal price for a product.

Applications:

• Competitive Data Analysis: The model tracks competitors’ prices in real-time and suggests dynamic adjustments to stay competitive.
• Demand Forecasting: AI analyzes trends and predicts how demand for a product will change, helping to determine whether the price should be lowered or raised.
• Pricing for Different Customer Segments: AI can offer personalized discounts or promotional prices for loyal customers, increasing retention.

What do you get? Maximized profit through a flexible pricing strategy. Reduced losses during sales due to excess inventory.

Adaptive Content for the Customer

Generative AI uses user data (age, gender, location, interests, previous purchases) to create a unique experience on the website.

Applications:

• Personalized Interface: Models can dynamically adjust the homepage display based on the customer’s preferences. For example, prioritizing a category that the user frequently browses.
• On-the-Fly Content Generation: Generative models create unique product descriptions or offers tailored to the specific customer. For instance, using personalized greetings in ads.
• Real-time Personalized Recommendations: Based on current user behavior (e.g., products added to the cart), chatbots suggest relevant descriptions, promotions, or products.

Outcome: you will not only increase customer engagement (through a personalized approach) but also boost brand trust by adapting the interaction experience.

Data Processing and Analysis

Conversational analytics tools provide intelligent processing of large volumes of data, helping businesses extract useful insights and make data-driven decisions.

Applications:

• Data Collection and Structuring: Automating the creation of reports from databases.
• User Behavior Analysis: Identifying user preferences to create personalized offers. For example, Spotify uses AI to analyze listening habits and suggest personalized playlists.
• Forecasting and Modeling: Generating forecasts based on historical data, assessing risks, and creating strategies to minimize them.
• Data Visualization: Creating charts, tables, and visual reports to simplify the interpretation of complex data.
• Anomaly Detection: AI can identify deviations in data (e.g., suspicious transactions or errors in accounting documents).

Business Process Automation

Generative models like GPT can replace or significantly simplify routine tasks by providing intelligent automated solutions. This allows companies to reduce costs, increase productivity, and focus on strategically important tasks.

Examples include:

Conversation automation in Amazon solves 70% of standard queries without human intervention.

• Generation of contract templates, reports, commercial proposals, and emails.
• Processing and categorization of incoming documents, such as invoices or contracts.
• Managing internal communications, creating training programs for new staff.
• Quick generation of advertising copy and creatives.
• Automatic content updates on websites and social media.

And this is just scratching the surface. We haven't even touched on how AI can be useful in solving transportation logistics, organizing warehouse storage, planning production standards, assessing financial risks, etc. It’s easier to frame the key areas of AI application as: "Tell us your task, and we’ll provide an AI-based solution for it."

Types of Chatbot Development Platform 

Platforms for developing chatbots can be divided into several categories based on their functionality, user audience, and technologies:

No-code 

These are suitable for users without technical experience. They provide visual builders where bots are created by dragging and dropping elements.

• Tars: focused on marketing automation.
• Landbot: a chatbot AI platform with a simple interface.

Low-code Platforms

These require basic programming skills but offer more flexibility than no-code solutions.

• ManyChat: Integration with Facebook Messenger and Instagram.
• Chatfuel: One of the powerful chat AI tools for small businesses.

Pro-code Platforms

Designed for experienced programmers. They provide access to APIs, SDKs, and libraries for creating complex solutions.

• Dialogflow by Google: Supports NLP (Natural Language Processing).
• Microsoft Bot Framework: Development for various channels, including Teams.

Generative Platforms

Use AI to create content based on user queries. Suitable for complex tasks and personalization.

• OpenAI ChatGPT API: Generative responses in natural language.
• IBM Watson Assistant: Enterprise-level AI chat technology.

Key Features to Look for in a Chatbot Development Platform

How to Choose the Right Chatbot Development Platform for Your Business? For small businesses, an intuitive interface is crucial, while developers may prioritize flexible configurations. Therefore, it's essential to first focus on the goals and needs of the specific project, and only then consider pricing for AI chatbot software.

To understand users effectively, the quality of natural language query processing is critically important. This means ensuring that the service supports NLP (Natural Language Processing).

As mentioned earlier, one of the prominent challenges with generative models is data security. It's vital to assess whether the platform supports encryption and complies with GDPR standards.

The platform should be able to handle growing user numbers and increasing task complexity. Therefore, scalability is also a significant consideration when choosing a chatbot platform.

Best Practices for Chatbot Development

To ensure your conversational AI for customer service truly benefits your business, there are several important considerations. Here are three tips to help you create an effective assistant:

1. Keep it simple: Avoid overloading the bot with too many functions. The simpler the navigation, the better the user experience. Your assistant doesn't need to be "the smartest." Its goal is to respond to the user's query as concisely and accurately as possible. Avoid using templates and overly formal language. The more human-like the bot feels, the more comfortable users will be.
2. Train the bot with real dialogues: The more real conversations you provide for training, the better the bot will understand user queries. After training, don't let the model run on autopilot. It should constantly receive new information and improve over time.
3. Identify triggers correctly: When should the bot switch the conversation to a human? For example, in cases of complex queries, user dissatisfaction, or when the user requests to speak with a live agent. The transition should be seamless.

For additional ideas and examples of successful conversational AI use cases, you can look at brands like Sephora (their chatbot gives excellent recommendations based on customer preferences), Domino’s Pizza, Uber, and others.

Case Study: Analyzes Birth Dates to Reveal Talents and Personality Traits

LYNQ is a mobile dating app developed by the Wezom team, with a unique focus on astrology as its key feature.

In this project, we utilized GPT-4 by OpenAI to implement some highly interesting and innovative functionalities.

      1.   Creating Chatbots

 Chat-based AI can engage in conversations with users, supports multiple languages, maintains conversation context, and understands user intentions. It can also answer non-standard questions.

Model: We used GPT-4 Turbo for natural dialogues. Settings: To maintain context, we provided initial messages in response to each request. Prompt Example: "You are an assistant that provides clear and informative answers."

      2.   AI Astrologer

AI can analyze natal charts of potential partners and identify astrological aspects that may indicate compatibility or incompatibility in various areas of life (love, work, friendship). Thanks to astrological data, the user's profile becomes deeper and more intriguing.

Model: GPT-4 for more in-depth responses. Settings: Included parameters like date, time, and place of birth to provide a more accurate astrological analysis. Prompt Example: "Provide a general astrological analysis based on the birth date, focusing on personality traits and life path."

      3.   Tarot Card Reading Calculation

Esoteric practices always capture interest, and the use of Tarot has made the dating process more exciting and mysterious. This created a unique atmosphere on the platform and attracted a target audience: people interested in mysticism and self-discovery.

Model: GPT-4 for complex interpretations. Settings: Predefined Tarot card values and their interconnections were used so the model could provide detailed answers. Prompt Example: "Based on the following cards [card names], give an interpretation for each card and the overall meaning of the spread."

      4.   Talents Based on Birth Date

This feature provides a boost to self-development and self-awareness, making the app even more unique and valuable. Understanding one's talents helps users describe themselves more accurately, which simplifies finding a partner with compatible interests.

Model: GPT-4. Settings: The model can give a general analysis of talents and strengths using the birth date data. Prompt Example: "Analyze the talents and strengths based on the birth date."

      5.   Clothing Style Recommendations Based on Ascendant

Analyzing the ascendant can help a person better understand their nature and how they are perceived by others. This knowledge can be the starting point for experimenting with their image and discovering their personal style. Well-chosen clothing boosts confidence, which motivates users to engage with the app and recommend it to friends.

Model: GPT-4. Settings: Used prompts considering the traits of the ascendant to personalize style recommendations. Prompt Example: "Recommend clothing styles that align with the personality traits and characteristics of a person with an Aries ascendant."

      6.   Generation of Natal Chart

People interested in astrology can find like-minded individuals and form a community within our app. This allows us to go beyond a standard dating app experience.

Model: GPT-4 with structured data for detailed responses. Settings: Used ChatGPT to generate interpretations. Prompt Example: "Provide an interpretation of the natal chart based on the date, time, and place of birth, detailing the main aspects and life themes."

      7.   Face Analysis for Personality Insights

Facial features can provide insights into certain personality traits, such as openness, extroversion, trustworthiness, and more.

Model: GPT-4. Settings: Based on a facial photo, the model generates personality traits or potential career strengths. Prompt Example: "Generate personality traits and possible career strengths based on the facial photo."

This functionality received highly positive feedback from users. They highlighted the unique experience and interesting interactive features that they had never tried before in any app. The use of AI not only attracted thousands of new users from the target audience niche but also helped the project gain significant recognition within the astrological community.

Conclusions   

There is no doubt that your project needs artificial intelligence. Whether in the form of a chatbot or an "intelligent" function within a warehouse management system, the specific implementation is less important. The key question to ask yourself is: "Who can I trust with conversational AI development?"

There are still many drawbacks of popular AI chatbots like ChatGPT (and its analogs):

1. Contextual Understanding Limitations: While GPT is good at analyzing text, it can struggle with complex or ambiguous situations. For example, misunderstanding a client's specific request. Have you ever tried asking a bot to connect you to a real person? It's a very questionable experience that could lead to lower customer loyalty and an increase in dissatisfied clients.
2. Ethical and Legal Considerations: The use of AI raises concerns about data privacy. Companies must comply with laws (such as GDPR) to protect user information. Moreover, there is no universal global legal framework on this matter yet.
3. Lack of Emotional Intelligence: Customer service automation tools are unable to recognize customer emotions or show empathy, which can be crucial in crisis situations. AI models are trained on data that may contain hidden biases, which could lead to discriminatory or offensive content.

To mitigate these issues, the AI system must be meticulously and precisely configured. The model needs to be well-trained and tailored to the specific needs of the business - only then can you achieve that “wow” effect.

Would you like to discuss the possibilities of implementing AI into your project?