How can ChatGPT and other AI’s help your business?

In recent years, the term artificial intelligence (AI) has achieved great notoriety, whether in the business market (which wants to implement technology in its products and services as a means of remaining competitive and leveraging its results in different ways) or in academia, where the number of searches on the subject remain high.

Despite its recent popularity, the first studies in the area were not in this century, but in the 1950s, when pioneers Allen Newell and Herbert Simon founded the first artificial intelligence laboratory at Carnegie Mellon University, and John McCarthy and Marvin Minsky created the AI Lab at MIT (Massachusetts Institute of Technology). In the 1960s, Joseph Weizenbaum, a computer scientist and researcher at MIT, developed the first chatbot to simulate human interaction: the Eliza software.

The study and creation of artificial intelligence is not limited to the creation of robots. In fact, AI is a technology that allows the interaction between different systems that learn through experience i.e. they perceive changes and adjust to them.

Now that we have a bit of historical context, let’s focus on the question:

How can AI really help your company/business?

Artificial intelligence (AI) can be used as a means of improving decision making and data processing. For example, AI can help a company predict consumer behavior, analyze large amounts of data to uncover insights into company performance, or optimize the product creation process. In addition, it can help with process automation and the creation of new products or services, as well as cost management. In short, AI can help companies become more efficient, agile and facilitate the achievement of their goals.

Every process that is capable of optimization requires customization for the specific business situation, with the aim of obtaining more efficient results, and it is no different with artificial intelligence. To optimize the results of an AI model, it is necessary to adapt it to your business, and this is the greatest challenge for companies today. Although there are several market solutions that facilitate the creation of artificial intelligence models (or even ready-made solutions), specialized technical labor is still required for their adaptation and optimization.

The AI of the moment is undoubtedly ChatGPT. It has revolutionized the way we access AI and has generated debate about its merits and challenges. The startup OpenAI, the tool’s creator, already had other solutions on the market (such as DALL-E, an artificial intelligence that creates images from textual descriptions). ChatGPT stands out for being a solution that can help in many areas of a company. As an AI model designed to converse naturally with people, it can answer questions, create theories, challenge misconceptions, and come up with appropriate solutions to problems, all via an intuitive dialog format.

The following is a simple list of the possibilities of ChatGPT:

• Resolution of doubts (Watch out, Google!)
• Automatic review of good implementation practices.
• Everyday use, such as creating diets, gym workouts, travel plans (The sky’s the limit! Try it!)
• Automatic bug detection and correction.
• Automatic generation of documents and reports.
• Generation of new codes, helping with the implementation of projects.
• Text translation without losing translation quality.
• Participating in pair programming — a software development technique in which two programmers work together at a workstation.

It is worth mentioning that we still do not know the full potential of ChatGPT and that there is still a vast field to be explored, but the trend for these tools is to become increasingly useful, even indispensable, within companies.

On February 6, 2023 Google announced its intention to demonstrate the company’s new bet: Bard, which they hope will compete with ChatGPT. The news was announced in this post: An important next step on our AI journey. Bard’s algorithm is known to have the Language Model for Dialogue Applications (LaMDA) technology and is fed with information from the web, which tends to make its responses more reliable and up-to-date than ChatGPT’s. Although it shows great promise, we will have to wait to discover its full potential.

Artificial Intelligence in the context of Marketing

As mentioned, artificial intelligence is an excellent technological resource that can help companies become agile, efficient and optimize their results. DP6 understands that working with this technology is essential for developing strategies based on data, and for creating technological solutions for managing this data.

When it comes to marketing data, there are many applications for artificial intelligence models, including:

Data analysis: Analysis of large amounts of data and identification of patterns and trends that can be useful for developing marketing strategies.

Sales forecasting: Predicting future sales of a product or service, helping to plan marketing campaigns and allocate resources.

Target audience segmentation: Identifying groups of similar clients and generating inputs for the development of personalized marketing strategies for each group.

Optimization of advertising campaigns: Optimization of advertising campaigns in real time, adjusting targeting and budget according to campaign performance.

There are several artificial intelligence models and algorithms that can be used in the field of marketing data consulting, namely:

• Supervised machine learning: Algorithms like Linear Regression, Decision Trees and Random Forest can be used to predict sales and customer buying behavior.
• Unsupervised learning: Algorithms such as Clustering and Principal Components Analysis (PCA) can be used to identify similar customer segments and understand patterns in marketing data.
• Reinforcement learning: Reinforcement Learning algorithms can be used to optimize budget allocation in advertising campaigns, learning from the performance of each decision in relation to resource allocation.
• Neural networks: Algorithms such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) can be used to analyze marketing data (such as text and images) and extract valuable insights.
• Deep learning: Deep Learning Algorithms such as Deep Convolutional Neural Networks (DCNNs) and Deep Recurrent Neural Networks (DRNNs) can be used to analyze more complex marketing data and identify more subtle patterns.
• Natural Language Processing (NLP): Algorithms in this category can be used to analyze consumers’ feelings about a brand, product, or service. They can also be used to classify data such as emails, social media messages and other types of content. They can even analyze trends and patterns in large datasets to understand consumer preferences and behaviors.

These are just a few examples of AI models and algorithms that can be used in the field of marketing data consulting. Exact usage will depend on each company’s specific needs and the type of data available for analysis.

If you’re familiar with DP6, you know that we’re a company that helps other companies to use their marketing data, make informed decisions, and increase the efficiency of their marketing strategies. To do this, we assist in data collection and analysis, in the development of data-driven marketing strategies and in the implementation of technological solutions to manage this data.

An example that illustrates the efficiency of algorithms aimed at optimizing results is an implementation carried out by DP6 for one of our customers, from which excellent results were obtained. For this, a cost function and a revenue function were used, and the revenue equation that modeled the system was given by: revenue = k*ln (cost). A profit function was modeled to constrain costs to a specific budget. After modeling the problem and creating the appropriate equations, it was possible to implement an optimization algorithm that found the maximum profit point. The objective was to provide suggestions for the allocation of investment in media channels, based on the results obtained from the attribution model. The result of implementing this algorithm was a 52% increase in qualified customers and 28% in ROAS from performance campaigns after a 21% reduction in investment in a big player.

It is very difficult to predict the future of artificial intelligence (AI) because this is a rapidly evolving field with many potential applications. However, we can expect that AI will be used in virtually all sectors, as it can perform tasks autonomously, provide accurate diagnoses and much more. In addition, artificial intelligence can also be used to create new features and intelligent solutions, allowing people to interact with it in more efficient and intuitive ways.

Profile of the author: Lucas Tonetto Firmo | A Computer Engineer graduate of Universidade São Judas Tadeu with an MBA in AI and Big Data from USP, Lucas is passionate about Technology and its ability to transform society’s way of life. He worked for two years developing websites and web applications and is currently a Data Engineer at DP6.

Profile of the author: Jane Thais Oliveira | With a Bachelor in Science and Technology from the Federal University of Vales do Jequitinhonha e Mucuri, she is a student of Engineering Physics at UFVJM and is passionate about data, machine learning and artificial intelligence. She currently works as a Data Engineer at DP6.