AI Applications for the Commercial Area

Our approach to business data exploration transcends human capabilities to perform analysis on a growing and overwhelming ocean of data.

Using state-of-the-art Machine Learning techniques, we perform multi-variable analyses that cross-cut customer, market and product data to identify patterns, correlations, interdependencies, and catalysts. We uncover valuable insights that often go unnoticed with traditional business intelligence tools.

These findings enable our clients to make informed and strategic business decisions for:

• Identify covert business opportunities
• Personalize offers and communications
• Anticipating consumer trends and behaviors
• Adjust customer acquisition and loyalty strategies.
• Promote the development of new products and services, or the creation of new functionalities.

Sales forecasting is a key exercise to maximize revenue, improve customer experience, ensure product launches and optimize inventory management, as in the case of stores.

We develop sales forecasting applications that employ predictive algorithms along with the most advanced Machine Learning techniques. We not only use historical and real-time data, but also integrate external variables such as campaigns, seasonality, location and product features to provide accurate and actionable estimates for commercial and sourcing teams.

Our solution is complemented by inventory optimization systems. By merging sales data and supply chain signals with our sales forecasting model, our systems can recommend the optimal amount of stock, reduce surplus and prevent stock-outs, lowering costs and improving resource management.

We design applications that analyze pricing and sales data to find the optimal price for a product or service to maximize profitability per customer or customer segment, as well as to activate promotional campaigns and customized discounts.

Our optimization methodology focuses on advanced data analysis, which allows us to identify predictive patterns of changes in supply and demand. We include historical data, real-time price and sales tracking by customer segment, competitor monitoring, and demand forecasting considering variables such as seasonality, advertising efforts, promotional actions and market trends.

A deep and detailed understanding of your audience is crucial to capture leads, convert them into customers and build customer loyalty. We design hyper-segmentation models based on the analysis of statistical, demographic, geographic and transactional data (online interactions and purchase history). Using Machine Learning algorithms, the system classifies and scores these contacts with a maximum level of granularity, to automate and personalize the company’s interactions with its audiences at all times.

Our applications can focus on the hyper-segmentation of leads or customers. With leads, the objective is to create engagement and speed up conversions with the personalization of messages and offers, in line with the scoring and nurturing practices used in demand generation strategies.

They are also valuable for segmenting existing customers. Here, segmentation is refined by analyzing transactional and post-sales behavioral data. The challenge is to maintain relevance and foster loyalty to increase customer lifetime value (CLV), and to design personalized upselling and cross-selling strategies based on buying patterns and preferences.

We design models that send early signals of risk of abandonment to those responsible for the commercial area.

We implement advanced Machine Learning techniques to classify customers according to their risk of churn, allowing companies to develop highly personalized and effective promotions and retention strategies.

We analyze in-depth historical customer data, including purchase patterns, online browsing behavior and social media activity, to accurately predict impending churn. This proactive approach not only improves customer retention, but also increases the effectiveness of loyalty campaigns.

Sentiment analysis is a powerful tool, empowered by the digitalization of society, that enables companies to capture the voice of the customer effectively. Our applications use advanced Natural Language Processing (NLP) technologies to evaluate and classify the opinions and emotions expressed by customers and consumers on various digital platforms, such as social networks, online forums, product reviews and feedback emails.

This analytical approach allows us to detect and understand consumers’ underlying attitudes and feelings towards a brand or product. By interpreting this data, our clients can proactively identify and respond to customer concerns, improve their experience and adjust product and communication strategies.

We also use generative artificial intelligence to go beyond discerning between positive, neutral or negative sentiment. We pick up subtle nuances, specific contexts, and invaluable actionable feedback, for better calibration of corporate response.

We create recommendation systems that deeply analyze the buying patterns and online browsing behavior of customers and consumers. Their objective is to identify and anticipate their needs and preferences, in order to recommend highly personalized products or services.

By integrating Machine Learning algorithms and generative AI models into our systems, we can more accurately predict which products or services will spark the most interest in different customer segments, making it easier to cross-sell and suggest relevant improvements or upgrades.

These systems draw on real-time data and accumulated learnings to dynamically adjust recommendations, ensuring that offers are relevant and timely. This personalization not only enhances the customer experience, but also optimizes inventory and maximizes customer lifecycle value through strategic recommendations of complementary products, or upgraded versions.

We develop AI-based applications to automate document management in the commercial area, with special emphasis on routine processes of classification and creation of documents such as periodic reports, budgets, pro forma, invoices, incident reports, orders and contracts.

The contribution of our Machine Learning algorithms is twofold. On the one hand, they optimize the systematic classification of documents and transactions, increasing efficiency and minimizing errors. On the other hand, they know how to process large volumes of data to produce periodic detailed reports, providing insights and actionable recommendations to business managers, in an automated way.

We also employ generative AI models to generate customized business documents. Our systems produce documents that comply with local legal and regulatory requirements while being customized to each company’s specific payment terms and terms of sale, ensuring compliance and relevance.

These applications ensure streamlined document and administrative management, freeing marketing, sales and customer service teams to focus on more strategic initiatives.

We develop cutting-edge generative artificial intelligence applications that integrate with our clients’ internal databases to optimize their interaction with leads and customers. Our chatbots incorporate deep language models (LLMs) and semantic search techniques to enhance your customer experience while optimizing your operational costs through the use of semi-autonomous virtual agents.

These conversational systems are trained according to our customers’ specific guidelines, providing instant responses in clear, natural language. It is estimated that a chatbot, applying best practices, can handle up to 80% of customer interactions, handling routine tasks and queries with great accuracy and efficiency. This autonomy makes it easier for human teams to focus on more complex queries and higher value-added tasks, thus increasing service quality and customer satisfaction.

Hyper-personalization of content represents the most sophisticated evolution of marketing, in which every interaction with the customer is tailored to their individual needs and preferences. Here, generative AI is the protagonist, allowing brands to go beyond “traditional” audience segments to reach the individuality of each consumer.

Using generative AI, our applications analyze real-time behavioral data, previous interactions, and stated preferences to create highly personalized content. From marketing emails, to product recommendations on websites, to promotional offers in mobile apps, each piece of content is unique to the recipient.

Generative AI not only tailors the message to the user’s preferences, but also adjusts the tone, style and formatting to resonate optimally with each individual. This is achieved through advanced models(LLMs) that can write text, select images and design layouts that appear to be handcrafted by a marketing team dedicated to a single client.

With hyper-personalization, companies not only increase conversion rates and customer loyalty, but also improve brand perception by demonstrating a deep understanding and care for their customers’ preferences. This 1-to-1 marketing methodology, powered by our AI technology, is the future of customer engagement, taking personalization to an unprecedented level.