Leveraging ChatGPT to Talk to Your Data: Unveiling the Magic of NLP to SQL in CRM Systems

Let’s start this blog with a real-world scenario.

Traditionally, when developing a software product, we take what engineers would refer to as an imperative approach.

It is a step-by-step process where the user clicks, drags, touches, and swipes – to get the outcome they are looking for.

But now with the power of generative AI and NLP, the industry started to adopt a declarative approach.

Here, the user only needs to describe their desired outcome (just like we give prompts to ChatGPT), and AI will take care of the rest.

This way, the user can skip multiple clicks and swipes, and get the outcome they are looking for.

The reason for providing you with this information is because of our recent project experience.

Where we adopted a declarative approach while assisting a client in implementing chat like experience to see the data in their CRM system as their end-user will type in.

And in this blog post, we will explore everything regardless of it.

So, without any ado, let’s get started.

The Issue at Hand: How Challenging is Data Querying Complexity in CRM Systems?

Our client, who runs a large-sized enterprise, was dealing with a widespread problem.

Their CRM system contained a wealth of customer and sales data stored in relational databases.

While the data was valuable, accessing it required expertise in query language like SQL, which is a standard language for manipulating and accessing data from a relational database.

This resulted in several significant challenges for our client such as,

SQL is a powerful language that requires a certain level of technical expertise to write queries effectively.

This means that only individuals with a technical background can formulate and execute queries.

As a result, non-technical staff, such as sales representatives and customer support agents, were reliant on these specialists to retrieve the data they needed.

The process of requesting, formulating, and receiving the results of a query involved multiple steps.

Besides, non-technical staff often had to wait for the data or IT team to address their query, leading to delays in accessing valuable data.

This delay had a significant impact on their decision-making abilities and efficient customer interactions.

Formulating and executing SQL queries requires careful attention to detail because a single typo or syntax error could lead to inaccurate results.

This challenge affected non-technical users who were not familiar with SQL, which increased the risk of unintentional errors in their queries.

The dependency on technical experts for data retrieval limited the autonomy of various teams across the organization.

For instance, the sales team had to depend on the IT or data team to extract sales performance metrics.

This was impacting their pace to identify trends and opportunities in real-time.

How NLP to SQL Reshaping Data Querying in CRM?

After carefully analyzing our client’s challenges, our team proposed the integration of NLP to SQL capabilities into the existing CRM system.

The primary goal was to enable users to interact with the relational database using natural language queries, without the need to write complex SQL code.

The following is a detailed breakdown of the implementation steps.

From Writing Codes to Using Natural Language: The Remarkable Outcome of NLP to SQL in CRM

For our client, NLP to SQL has emerged as a game-changer.

Because it enabled them to bridge the gap between human language and machine-understandable commands, making the data retrieval process faster and more efficient for all.

Below are a few advantages our client experienced after text to SQL in CRM.

Now, Let’s Look at the Real-World Scenario with Text to SQL in CRM

Consider a scenario where a sales manager is using a CRM system.

The aim is to track sales performance and retrieve information about the total sales made by a specific salesperson during the last quarter.

With standard SQL, the sales manager must write a query as shown in the image below:

Now, let’s see how NLP to SQL can simplify this process.

Since the NLP to SQL is integrated with the CRM, the sales manager can use natural language (like plain English) to express the same query.

For example, “Show me the total sales made by salesperson XYZ in the last quarter.”

And this is what takes place behind the scenes.

➡️ Step 1: The NLP engine will interpret the query.

➡️ Step 2: The system will translate the natural language query into a valid SQL code.

➡️ Step 3: The translated SQL query is then executed on the relational database in order to retrieve the requested data.

➡️ Step 4: The sales manager can now perform the necessary analysis of the extracted data.

You can see that NLP to SQL has not only saved time for the sales manager but also eliminated the need to formulate and execute the specific SQL query.

Where Else Can NLP to SQL Be Used Besides CRM Searches and Queries?

Certainly, here is a list of possible use cases for applying text to SQL within CRM systems.

• Reporting and analytics
• Automated insights and trends
• Data exploration
• Customer sentiment analysis and feedback
• Personalization and segmentation
• Lead identification
• Workflow automation

• Voice-enabled CRM
• Predictive analysis
• Identifying cross-selling and upselling
• Compliance and data security
• Customer insights for sales
• Market research
• Performance monitoring