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

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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,

1. Skill Barrier

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.

2. Time Consumption

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.

3. Error-Prone Queries

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.

4. Limited Autonomy

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.

1. Data Analysis

We analyzed common queries made by different user roles within the organization.

The aim was to better understand all of their requirements in order to design the NLP models (plugins/backend/application/service) that can handle a wide range of queries.

2. NLP Model Training

To understand and interpret natural language queries, we trained an NLP model (ChatGPT).

The model was fine-tuned using a database of annotated queries and corresponding SQL queries.

3. SQL Query Generation

Upon understanding the natural language query, the NLP model formulated a relevant SQL query using predefined patterns.

This capability enabled the model to handle queries regarding sales performance, customer details, order history, and various other metrics.

4. User Interface Integration

After successful SQL query generation, we seamlessly integrated the NLP-powered query box into the user interface of the CRM system.

This enabled users to input queries directly into CRM using natural language, eliminating the need to write SQL code.

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.

NLP to SQL makes database querying more accessible to users who lack expertise in SQL programming.

This helped non-technical staff, such as sales representatives or customer support agents, to retrieve valuable data independently.

With NLP to SQL, querying databases becomes faster and more efficient.

Users can now access the information they need in real-time without having to consult technical experts for learning complex SQL commands.

Since NLP queries are expressed in natural language, the chances of typos or syntax errors in SQL queries are significantly reduced.

This resulted in more accurate outcomes and less frustration for users.

By integrating NLP capabilities into CRM systems, our client was able to enhance the overall user experience.

This led them to higher user adoption rates and increased productivity across various teams.

Since NLP to SQL removes the need for reliance on a technical team, our client’s managers and executives were able to make data-driven decisions without any delays.

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

Want to Leverage Your Enterprise Database?

In the dynamic landscape of modern business, having efficient data management is crucial.

Our journey through the integration of NLP to SQL in CRM showcased the power of modern technologies in simplifying data retrieval and productivity.

At Azilen, we believe NLP is an attitude and a methodology, not the trail of techniques it leaves behind.

Thus, with our 14+ years of software product engineering expertise, we are here to drive your data management success.

Through our tailored approach to NLP model training and UI integration, we create systems that bridge the gap between technical complexity and user-friendly interfaces.

As your dedicated software product engineering partner, we are committed to streamlining data retrieval, enhancing user experience, and simplifying data-driven decision-making that accelerates your business growth.

Together, let’s turn complexity into simplicity, and aspirations into achievements!

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