Can Chat GPT generate sales forecasts?

The world of artificial intelligence is exciting, and it’s great that you’re tackling such hot topics in today’s business world.

When using ChatGPT, you quickly feel that this ChatBot can do it all. Without question, Large Language Models like ChatGPT are incredibly impressive. But can Large Language Models (LLMs) also generate accurate sales forecasts?

This question is highly relevant, especially for companies that want to make data-driven decisions, such as B2B wholesale companies. In this post, we examine the capabilities of LLMs and compare them to specialized machine learning and predictive analytics models, such as those used in Qymatix Predictive Sales Software.

First, congratulations on taking the time to delve deeper into this subject matter. Your desire to understand and utilize the best technologies for your business shows vision and commitment—qualities that can make a difference in the competitive marketplace.

What is a Large Language Model (LLM)?

Large Language Models (LLMs), such as ChatGPT from OpenAI, are AI models trained on huge text datasets. They are based on a transformer architecture* and often contain billions of parameters. LLMs can generate text, answer questions, and even perform tasks such as translation and summarization.

The key to their versatility lies in their generalization: they learn from various data sources and can apply them in a wide range of contexts. This makes them extremely useful for general language tasks but less suitable for particular, data-driven applications such as precise sales forecasting. Do you already recognize the first clues to answer the question?

Sales forecasting: requirements and challenges

Sales forecasts are crucial in the business world as they help companies plan demand, optimize resources, and develop customer retention strategies. They are usually based on structured data such as historical sales data, customer profiles, and seasonal trends.

For accurate forecasting, models need to do the following:

– Work with structured data: Time series analyses, historical transaction data, and external influencing factors are essential.
– The aim is to make forecasts as precise as possible. A forecast is created quickly. However, the value of forecasts lies in the degree to which they are fulfilled. This requires well-trained and up-to-date models.
– Domain-specific adaptation: Every company has individual requirements and unique features that require specific adaptations.
– Explanation and interpretability: Companies need comprehensible results to make well-founded decisions.
– Efficiency and scalability: The models must work quickly and use resources sparingly, even with large amounts of data.

Can LLMs like ChatGPT create sales forecasts?

1.Generalization vs. specialization
LLMs are generalists that can handle broad language tasks. They cannot make precise predictions based on specific data sets like specialized machine learning models can. For example, an LLM can do a great job of reproducing the theory of forecasting models, even naming specific formulas or answering general questions about sales strategies. Still, it is not optimized for complex, structured analysis.

2.Data structure
LLMs work mainly with text data. As required in sales forecasts, they are not directly designed for tabular or numerical data. Converting structural data into text form would be cumbersome and inefficient.

In addition, LLM systems are extremely error-prone when working with structured and non-aggregated data (such as tables of historical sales transactions)! In principle, you would have to check every single result. With several 5,000 customers and 10,000 products, this is almost impossible.

3.Computational effort
Executing an LLM is extremely computationally intensive, even for simple tasks. This is not only costly but also impractical for large-scale, continuous forecasts.

4. Explainability
LLMs are black box models. Their predictions are difficult to understand, which poses a significant risk in business-critical applications. However, companies need models that are easy to understand and whose results they can explain to stakeholders.

5.Precision
As mentioned above, accuracy is the “be-all and end-all” of sales forecasting. Sales forecasting requires recognizing the smallest patterns and correlations in structured data. LLMs are not optimized for this and often deliver inaccurate or unusable results.

What makes specialized models better?

This is where specialized solutions such as Qymatix Predictive Sales Software come into play. This software was developed specifically for the requirements of B2B sales and uses machine learning and predictive analytics to create precise sales forecasts. Compared to LLMs, these models offer significant advantages:

1. Optimization for structured data
Qymatix analyses sales data directly from ERP systems. The software recognizes patterns, such as the purchasing behavior of specific customer segments, and provides actionable recommendations.

2. Domain-specific customization
The software is specially optimized for B2B sales. It considers industry-specific requirements such as price individualization, cross-selling potential, and churn risks.

3. Efficiency
In contrast to LLMs, Qymatix is resource-efficient and delivers results in real time. The software can be easily integrated into existing systems and does not require any complex data conversion.

4. Economic benefit
Qymatix customers report significant improvements, such as reduced customer churn and increased cross-selling revenue.

The right choice: LLM or specialized software?

The choice between an LLM and a specialized model depends on the task. Here is a simple rule of thumb:

LLMs are great for general language tasks, brainstorming, and report writing.

Specialized models like Qymatix are the best for accurate sales forecasting, data-based decisions, and specific business applications.

CALCULATE NOW THE ROI OF QYMATIX PREDICTIVE SALES SOFTWARE

Conclusion: expertise and technology for success

Your addressing this topic shows your willingness to invest in your company’s future. Choosing the right technology can make the difference between a good sales strategy and an outstanding one.

LLMs like ChatGPT are potent tools, but specialized software like Qymatix is unbeatable for accurate sales forecasting. It provides not only accurate predictions but also comprehensible, commercially viable results.

Your interest in this topic shows that you are ready to make data-based decisions and use the best tools to succeed. Focus on the future – and make your company even more successful!

I WANT PREDICTIVE ANALYTICS FOR B2B SALES.
——- Explanation of terms ——–
* The Transformer architecture is a neural network design specifically developed for natural language processing (NLP) tasks. Its main feature is the self-attention mechanism, which enables the model to efficiently recognize relationships between different text parts, regardless of how far apart these words or tokens are.