Chat Intelligence: When Chat Meets GenAI and NLP

CHAT INTELLIGENCE refers to our specialized technology and solutions that leverage NLP and GenAI capabilities. At its core, Chat Intelligence encompasses the use of advanced AI-driven algorithms to enhance chat and messaging systems. These systems can understand, interpret, and generate human-like text, based on natural language input, resulting in more sophisticated and valuable user interactions.

Chat intelligence helps drive incremental business opportunities by identifying:

• New leads for businesses
• Signals from existing customers for additional business opportunities
• Potential customer dissonance triggering retention measures
• Themes for personalized marketing campaigns
• Upsell or Cross-sell opportunities
• Indicators or patterns that lead to fraud
• Customer retention strategies
• Customer sentiments

From Chat Conversations to Business Insights

For businesses aiming to integrate chat intelligence into their operations, the significance of chat mining cannot be overstated. Chat mining, a fundamental aspect of chat intelligence, entails the extraction of valuable insights from chat data. This process involves analyzing text conversations to decipher customer preferences, behaviors, and sentiments, utilizing the extensive data generated from interactions between customers and chatbots or virtual assistants. By converting this data into actionable intelligence, chat mining becomes a critical tool for businesses focused on enhancing customer experience, optimizing operations, and making informed strategic decisions.

Despite its potential, chat mining faces several challenges, particularly when relying on traditional NLP techniques:

• Limited Contextual Understanding: Traditional approaches like TF-IDF and Word2Vec for feature extraction often struggle to grasp the full context of conversations. This can lead to misunderstandings of customer intent and sentiment, impacting the quality of insights derived from chat data.
• High Computational Requirements: Processing and analyzing large volumes of chat data require significant computational resources. Traditional models, while effective for simpler tasks, can become inefficient and costly at scale.
• Evolving Language and Slang: The dynamic nature of language, including the use of slang and new expressions in chat interactions, poses a challenge for static models that are not continuously updated.

Overcoming Challenges with Deep Learning and Ablation Analysis

To address these challenges, there has been a shift towards leveraging the power of deep learning. At Tiger Analytics we use models like the Universal Sentence Encoder (USE) and Bidirectional Encoder Representations from Transformers (BERT). These iterations represent a significant departure from traditional approaches, offering enhanced contextual understanding and reduced computational burdens.

• Deep Learning Iteration-1 (USE Embeddings + Classifier): The first iteration involves using USE embeddings, which provide a more nuanced capture of semantic information in chat conversations. This approach marks an improvement over TF-IDF by incorporating a broader context.
• Deep Learning Iteration-2 (Fine-tuned BERT Model): The second iteration advances further with the adoption of a fine-tuned BERT model. BERT’s ability to understand the bidirectional context of words in sentences significantly enhances the model’s performance in chat mining tasks.

The Crucial Role of Ablation Analysis

Ablation analysis is a methodical approach to improving chat intelligence systems by systematically removing components, such as layers, neurons, or specific features, to study their impact on the model’s performance. This process helps identify which elements are crucial for the success of the model and which might be redundant or detrimental. The analysis provides insights into how different NLP and AI techniques contribute to the system’s ability to understand and generate language, offering a deeper understanding of the underlying mechanisms.

Ablation analysis becomes particularly valuable in refining deep learning models for chat intelligence. By systematically removing or modifying components of these complex models, researchers and developers can:

• Identify Key Features: Determine which features or model components are most influential in understanding and generating chat-based interactions.
• Optimize Model Performance: Enhance the accuracy and efficiency of chat intelligence systems by focusing on essential elements.
• Reduce Computational Costs: Eliminate unnecessary or less impactful components, thereby streamlining the model for better scalability and reduced operational expenses.

Ablation Analysis illustrated through a series of examples

In the first example “Hi, I am considering moving all my accounts held at an outside firm to your firm.”, the indication of the movement of money from external firms is clear and all the three models are able to pick up the signal of an incoming transfer.

In the second example, “Hello, I am considering moving my account to a different firm.”, the TF-IDF model and the USE embeddings-based model were not able to understand the nuances of the sentence. These are the typical false positives that the model struggled to differentiate:

In the third example, “May I get some help. I am looking to open a new account and start contributing to it.”, the TF-IDF and USE model’s output probabilities are below the threshold and hence are lost opportunities. However, the BERT model’s fine-tuning helps rightly identify this as a valid lead. This leads to a higher volume of leads and minimizes missed opportunities.

The journey towards achieving excellence in Chat Intelligence is both challenging and rewarding. At Tiger Analytics, we are committed to leveraging the latest advancements in NLP and AI to offer solutions that meet the unique needs of our clients. Our expertise in chat mining and the strategic application of deep learning models and ablation analysis have enabled us to unlock new levels of efficiency, insight, and customer engagement. As we continue to innovate and explore the vast potential of chat intelligence, we invite you to delve deeper into our findings and methodologies.

For a more comprehensive understanding of we’ve used ablation analysis and fine-tuned BERT models to build a help extract chat intelligence from conversational data, read our whitepaper- How NLP and Gen AI are helping businesses derive strategic insights from chat conversations

The post Turning Conversational Data into Chat Intelligence with Ablation Analysis appeared first on Tiger Analytics.

More businesses are expected to lean into modern BI solutions to revolutionize their analytics strategy, making it easier for the business and the stakeholders to act in real-time.

In this article, we shall look at the trends, tools, and drivers of Modern BI through the lens of two Industry Use Cases.

A Single Source of Truth From a Scattered BI Stack

One of the most notable partners of Tiger’s is a large logistics company based out of the US. Geospatial information is a large part of their operations. However, their biggest issue was the fact that their BI stack was scattered across a multitude of applications.

Geospatial Analytics’ market share is expected to reach $209.47 billion 2030.
It is used in a variety of sectors like agriculture, utilities, healthcare, insurance, real estate, supply chain optimization etc.
Deployment of 5G network services is expected to create new opportunities for geospatial analytics vendors.

This situation comes with a number of drawbacks. A scattered BI stack means that data needs to be compiled for presentation and insight gathering, which requires a technically proficient team working on it. That is not an ideal state of any company’s data infrastructure, given the speed at which businesses need to operate with their data to make decisions. Easy adaptation by non-technical users is a key marker for a democratized and efficient BI environment. Therefore, a unified user-facing application was the appropriate solution for this project.

Embedded BI’s goal is to integrate data analytics, insights, and visualization within an application with clarity and ease of access. It was the key to integrating the client’s multi-BI environment into a single platform.

Tiger needed to build an integrated BI stack for a unified customer experience and eliminate data redundancies with a single source of truth. There were many sources of data within the organization since they had multiple departments. Using Tiger’s solution, all of their data were presented in a single, centralized application called the ‘Digital Dashboard.’ BI reports were embedded in the application with the use of an API interface.

All departments of the company had access to the application to obtain insights from a single source of truth, which enabled better network coverage and agile performance.

Big Data, Precise Action

Big Data has been a huge talking point in the last decade, but there has been very little conversation on modern BI handling Big Data. The last few years have seen a massive increase in the production of data. Legacy BI tools have very limited capability to handle this huge volume of data, and it impacts the user’s ability to derive actionable insights. However, modern BI tools have evolved to handle petabytes of data and provide data insights with ease.

One of the other esteemed partners of Tiger is a huge media house in India. They wanted to place precisely targeted ads in order to increase their ad revenue. They also wanted insights into viewership behavior for smarter programming. The data input they had at their disposal were user data, demographic data, and data from social media.

Considering the variety of data and data sources, we needed a BI tool that can handle the large volume of data and prepare reports. The solution was to integrate TBs of heterogeneous sources onto Data Lake and consume reports in live/direct quest mode through native Spark-based BI connectors to get close to real-time insights. This achieved personalized recommendations for ad placements and programming based on user activity and demographic inputs, and in the procurement of a great share of $5 billion in brand advertising dollars.

Tiger’s BI Framework

Tiger has a prebuilt framework to keep pace with the evolution of BI. It consists of code repositories, context templates, tech requirement templates, chart accommodations, and visuals that meet the requirement of any modern BI need. It also consists of a number of templates for embedded BI, Big Data, and geospatial BI.

The Dashboarding Program Guide helps in a comprehensive discovery of business requirements, and the development and deployment of impactful dashboards that can steer the users to success.

Metadata Extractor

The Metadata Extractor has been a part of traditional BI tools, where they had their own external metadata store, mostly in an XML format. However, it is not efficient for engineers and architects to go deep into metadata and get information. Tiger has gone a step ahead and built a Metadata Extractor tool to simplify and automate the process to collect metadata information of various reports and dashboards. The necessity of this tool is dictated by the prevalence of data redundancy in modern BI systems, which is quite common given that the abundance of data is only increasing by the day.

Conclusion

Data democratization is key to reaping the benefits of technological evolution, which is constantly putting technologies that are capable of gathering petabytes of data in the hands of users across the world. Modern BI tools need to be time and cost-efficient, extensive in terms of data processing yet precise in their synthesis, and most importantly, accessible to the non-technical user. Tiger is at the forefront of crafting such tools that gives its partners the ability to arrive at prompt, actionable insights with its extensive modern BI frameworks and an enriched, secure data culture in place.

To catch our full talk, click here.

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