In the digital age, marketing has evolved significantly, but many companies still face considerable challenges in profiling and analyzing potential customers with the data they capture in current experience models. Today, most processes are carried out through digital and automated tools (Ads, landing pages, forms, chatbots, CRMs) that capture certain data and bring it to the marketing or sales areas or specialists for processing. These second steps in the flow require human intervention for the analysis and treatment of potential profiles to sell a product or service, and it is in this profiling that the process becomes a bit more manual if we want to consider unstructured data (such as comments, LinkedIn taglines, posts on X) to complement the captured information. However, the future looks promising, where automation, combined with an advanced level of reasoning in flows and artificial intelligence agents, will allow companies to have robotic marketing specialists or those with reasoning characteristics in the analysis and profiling of clients. This transformation not only streamlines customer acquisition but also improves the accuracy and effectiveness of commercial strategies, leading to a radical change in marketing dynamics.
But let’s see, all this introduction is easier to understand with the created example. What do we do if we ask a specialist to leave us the selected high-profile potential customers in a resource that salespeople can use? With the ability to read comments, texts, the person’s biography, or other data, and interpret that this is indeed the customer we wish to obtain. Well, the purpose of this article is not just to tell it but to show it.
The current state of analysis and profiling
Nowadays, many companies, both in the real and technological sectors, try to capture and analyze data about potential customers through various tools such as ads (Ads), online forms, landing pages (single pages with a purpose - selling), and customer relationship management systems (CRMs). However, despite having automated systems for data capture, the process of profiling these leads remains overwhelmingly manual and based on human intervention if TOP clients are required. But there are also other profiling methods, as I once heard from a bank; in their words, "The selection we make of whom we send the X marketing or sales campaign to is everyone who has an account at our bank," and we were talking about a premium product that should have been for the bank's most profitable clients.
Some statistics are revealing: a HubSpot study suggests that companies can spend between $2,000 to $5,000* per acquired customer due to high marketing costs and inefficiency in conversion processes. Moreover, 70% of salespeople say that lead profiling is one of the most challenging tasks and often consumes a significant amount of time. This means that the marketing engine slows down, which in turn increases the total customer acquisition cost (CAC) and significantly reduces the return on investment (ROI).
* The cost number varies by country, company, market, audience, etc. But it is always high.
In an environment where every second counts and every dollar invested in advertising must yield results, the situation becomes unsustainable. Therefore, the crucial question arises: What if the activity of profiling and analyzing leads could be automated, but with a level of reasoning that allows AI to make more informed decisions instead of merely processing data?
Potential of automation with reasoning
Imagine a system that not only collects data but also interprets and acts on it. Automation with a level of reasoning could mean that the profiling process would not solely depend on manual review, which is often subject to biases and human error. Instead of waiting days or weeks for a well-founded analysis of a batch of prospects, companies could receive insights in real time about which leads are most likely to become clients.
However, this advancement of sending unreviewed information directly to users, leads, and customers is not without risks. There is a possibility that premature communication with a potential customer, generated by an automated system, may not be accurate. There could be errors related to product features, pricing, or even the type of service they sell. For example, a customer could receive a message that does not reflect the actual capabilities of a product or its benefits, which could lead to negative brand perception. Without human review to ensure the quality and accuracy of the information, companies risk losing not only a lead but also the trust that lead might have had in the brand. Or, explaining what happened to me with a project in 2023, our content generation models were spitting out very readable information, well-written, with brand words, but the intent of the text was completely wrong and instead of informing, it misled. Therefore, my recommendation is to test and understand, optimize, and then always leave a place for a human to review before sending the information.
Creating a marketing specialist agent
Leveraging the possibilities of artificial intelligence, we have developed a marketing specialist agent that performs two key functions:
1. Profiling Potential Customers: This agent analyzes demographic data, online behaviors, and other relevant factors to create a detailed profile of leads. Thus, it can identify individuals who are more likely to become clients.
2. Operational Tasks: Once the leads have been segmented, another agent with a more operational role is responsible for executing various tasks, such as:
- Filtering and marking leads as MQL (Marketing Qualified Leads).
- Creating a summary of the analysis generated by the specialist agent.
- Storing information in a database in Airtable, allowing for easy and quick access by the sales team.
This approach allows for a continuous and efficient workflow. The specialist agent focuses on in-depth analysis, while the operational agent handles administrative task execution.
Opportunities for optimization
The implementation of this system not only improves efficiency but also presents multiple optimization opportunities:
1. Acceleration of the Process
Automation with reasoning significantly reduces the time spent on repetitive tasks. Less time spent on data collection means that the marketing team can focus on strategic activities, such as creating attractive and analytical campaigns.
2. Scalability
Thanks to automation, it is easier to scale marketing operations. As the customer base grows, the agents can manage a larger volume of data without proportionally increasing staff, allowing companies to expand their reach without a corresponding increase in costs.
3. Improvement of Accuracy
AI can identify patterns and correlations in the data that may be too complex or difficult to detect in manual analysis. This accuracy leads to more effective segmentation and therefore, more successful campaigns.
4. Reduced Costs
By decreasing dependence on manual work and optimizing resource utilization, companies can observe a reduction in operational costs. In the long term, this can translate into significant savings.
Beyond lead generation
The potential of AI does not stop at lead generation and analysis. The intention is to further explore the capabilities that allow for expanding the functions of our automated system. One possible extension of this flow would be:
- Initial Copy Generation: Using AI, personalized content could be created that resonates with the needs and desires of the potential customer.
- Direct Interaction: Initiating conversations with leads proactively or, alternatively, automatically sending contact details using platforms like Telegram or WhatsApp, so a salesperson can quickly communicate.
Integration with sales and marketing teams
As the process is refined, new possibilities arise, such as creating an automated data analyst that evaluates sales and marketing campaigns in real time. This would provide valuable insights that could be used to adjust business strategies in an ongoing improvement cycle (24x7) and iteratively adapt strategy, communication, and channels.
Examples of future functionalities:
- Management of Initiatives: Our system could suggest marketing initiatives that align with emerging trends, based on the performance of previous campaigns.
- Interdepartmental Coordination: Integrating marketing results with the sales department and management, allowing for continuous adaptation of business strategies.
The infinity of business processes
The best part is that this type of solution is endless. The flexible architecture of artificial intelligence allows companies to adapt and scale their workflows according to their specific needs. Regardless of the sector, from customer service to project management, AI has the potential to revolutionize the way we operate.
Making connections
In conclusion, the combination of advanced tools such as Google Sheets, n8n, DeepSeek R1 from Groq, OpenAI API, and Airtable, integrated into an intelligent flow of marketing agents, can transform the way companies operate and relate to their potential customers. Automation, accompanied by deep and precise reasoning, can significantly reduce the total customer acquisition cost and improve the quality of communication. Optimization, speed, and economy of scale are just the tip of the iceberg in a world where artificial intelligence redefines the limits of marketing and sales.
Equip yourself for the future of marketing!
If you want to know more about how to implement these artificial intelligence solutions in your business and create a more efficient and automated workflow, feel free to seek information on the subject. The good thing is that there is a lot, free, paid, easy, hard. The secret is to limit the trials and always seek the benefit for you. I clarify, it is either an increase in revenue, a reduction in costs, or efficiency in time. If you don't achieve that, all you've done is spend on digital toys.
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