553. Phil Bellaria: AI Project Case Study

Key takeaways

AI can effectively analyze unstructured data to extract insights about customer and employee sentiments.
Understanding the specific language and phrases that lead to successful sales can be analyzed through AI.
Integration with existing systems and real-time processing are vital for successful AI implementation.
Change management is crucial for gaining acceptance of AI tools among sales agents.
AI can significantly enhance training processes and improve performance across different levels of employees.

Summary

Using AI to Analyze Unstructured Data for Business Insights

Phil Balaria shares a case example of implementing AI for a large telecommunications company that needed to extract insights from vast amounts of unstructured data, including customer feedback, employee surveys, and millions of transcripts. He explains the process, which took place from 2018-2020, utilizing Google's BERT for natural language processing to identify topics, sentiment, and summaries from the data. The team iterated their algorithm and ensured accuracy through feedback from HR business partners and business leaders.

Improving Sales Pitches through AI

Phil discusses how they employed AI to analyze conversations between sales agents and customers to determine the effectiveness of pitch strategies for a particular product. By analyzing language, sentiments, and phrases associated with successful and unsuccessful sales, they provided sales agents with actionable insights and training on effective expressions to use when pitching. This process involved identifying the specific messaging that resonated with customers.

Implementing an AI-Powered Next Best Action Engine

Phil shares insights into a project where AI algorithms were used to recommend the next best actions for customer interactions in real-time. This required careful integration with existing systems, and the AI utilized reinforcement learning to improve recommendations based on past interactions. The discussion highlights the technical challenges such as building APIs, ensuring responsiveness, and maintaining data security.

Challenges of Integrating AI Solutions

The speaker emphasizes critical aspects of AI implementation, including defining the business problem clearly and engaging technical teams early in the process. Phil outlines a realistic timeline for deploying a recommendation algorithm within a corporate environment, emphasizing change management, limited pilot testing, and collecting data to refine the models. He points out the importance of aligning incentives for agents to use these AI tools.

The Impact of AI on Sales Performance

Phil reflects on the transformative power of AI in a sales environment, demonstrating how insights from AI models can elevate the performance of mid-tier employees and accelerate training for new agents. The discussion acknowledges that while not all recommendations outperform human intuition, the data-driven approach provides valuable learning opportunities, especially in identifying effective sales techniques and overcoming objections.

Show notes

Show Notes:

In this episode of Unleashed, Phil Bellaria shares a case example of building a Chat GPT using open-source large language models. The client was a large telecommunications company with an immense amount of unstructured data, including customer feedback, feedback from employees through surveys, and transcript transcripts from millions of phone conversations and text chats. The problem statement was to derive insights and understand the state of the business, identify trends and topics as quickly as possible. The process took place through 2018-2020.

Working with a data scientist, and using Google's BERT methodology for natural language processing, the team coded an algorithm that identified topics and classifiers from the unstructured data, scored each topic and phrase on sentiment (positive or negative comments) and created a short summary of customer or employee comments related to each topic.

The process of building and running the model was processing intensive, and the first step was testing and iterating the model on smaller samples of data. The company held employee surveys, which was processed through the test model, the data was reviewed by HR business partners and business leaders to check for accuracy. The model was trained on all the information in Wikipedia, but other specific information and words were added to refine it.

Over six to eight months, the model was able to accurately represent what employees were saying.

Using AI to Improve Sales Pitches

Phil discusses the use of AI in business applications and how it can be used to improve sales pitches. He explains that the problem was to understand why sales agents were not pitching a strategic product effectively. By feeding data from conversations with customers about the product, the algorithm was able to identify words and phrases associated with successful sales and non-successful sales. This information was then used to train sales agents on the right expressions and words to use when pitching the product. Phil shares some phrases that work well and those that don't, such as promoting a streaming product by associating it with popular shows. He also discusses the challenges of building AI models and securing and protecting data. He also addresses the cost of building an AI model.

Using AI for Next Best Customer Actions

Phil shares one example of AI-related projects which used AI algorithms to predetermine the next best action for a customer that can be used in real time to learn the best approach in customer interaction. The AI engine uses reinforcement learning to improve the power of the recommendations. The process involved building the right APIs into existing systems and ensuring SLAs in terms of responsiveness. The algorithm itself uses sophisticated statistical modeling techniques, but the main challenge was integration and timeliness.

Challenges Implementing AI

Phil talks about the challenges of implementing this process. He emphasizes the importance of defining the business problem and getting the technical team involved early in the process. He talks about time spent translating the problem into technical applications, allowing technical personnel to use their skills to solve the problem. He also shares a timeline for starting a recommendation algorithm. The process includes writing, pulling in data, creating a data environment, scoring, and algorithms. Another consideration is change management which involves limited pilots and controlled AB tests across the population, and time allotted to roll out and testing.

Phil discusses the power of AI in data analysis, stating that it can provide insights and interactions that are not always available before. The real power lies in bringing new agents to speed up the process and elevating the performance of middle-tier agents. The lower performing agents often wouldn't use the tool, so they don't see as much impact.

Timestamps:

00:02 Using AI to analyze unstructured data for business insights

03:23 Using AI to analyze customer feedback and sales data

08:14 AI-powered next best action engine for sales

12:16 Implementing AI-powered customer service tool

16:43 Using data and analytics in call centers

Links:

Company website: https://www.cdaopartners.com/

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