DS Methodology

Generative AI Chatbot

Case Study: Generative AI Chatbot for Annuity Customer Service Representatives

AI, datascience

Case Study: Generative AI Chatbot for Annuity Customer Service Representatives Author: Tony Ojeda This case study explores the development and implementation of a Generative AI chatbot designed to assist customer service representatives (CSRs) at a nationwide annuities provider. The chatbot aimed to improve CSR efficiency and effectiveness when addressing complex customer inquiries regarding annuity products. […]

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AI icon, logos for Open AI, Google, and Meta's LLMs

Revolutionizing AI: The Power of Large Language Models

AI, big-data, datascience

Revolutionizing AI: The Power of Large Language Models Author: Konstantin Galperin Large Language Models (LLMs) have revolutionized the field of artificial intelligence (AI) by demonstrating remarkable capabilities in understanding and generating human language. These models, such as OpenAI’s GPT-4, are built upon deep learning techniques and vast amounts of training data, enabling them to perform

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A Hybrid Approach to Customer Segmentation: Combining Machine Learning and Rules-Based Methodologies

datascience, market-research, Uncategorized

A Hybrid Approach to Customer Segmentation: Combining Machine Learning and Rules-Based Methodologies Author: Evie Fowler Customer segmentation refers to the process of dividing customers into subgroups with similar buying habits and needs. It helps businesses understand their customers better so that they can market existing products more effectively and even develop new products to meet

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Segmentation Models Compared

datascience, market-research

Segmentation Models Compared:  What’s best suited to your needs? Author: Yifei Zheng Customer segmentation is a means of organizing and managing a company’s relationship with its customers and can improve things like personalized marketing communications, customer service and support efforts, customer loyalty, identifying the most valuable customers, and identifying new product or upsell opportunities. This

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Incorporating Decision Science into Data Science

datascience, market-research

Author: Igor Pshenychny Machine Learning and mathematical rigor are tools at data scientists’ disposal. But we all know that having a better calculator won’t exactly result in better test scores, and having a more powerful computer won’t necessarily make you a better programmer. To develop a strategy that doesn’t underperform, a data scientist will need

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Marketing Analytics Techniques Compared: Marketing Mix Modeling vs. Attribution

datascience, market-research, retailviewer, website-analytics

While marketing in general is capable of bringing additional sales, effectiveness of any particular campaign, channel, touch point etc. may vary substantially. To evaluate performance, historically there have been two approaches – Marketing Mix Modeling (MMM) and Attribution modeling. Here we examine differences, advantages and disadvantages of both approaches. Marketing Mix Modeling: Cost and Sales

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Don’t Throw Away The Distribution: The Importance of Probability Distributions

big-data, datascience

As data science becomes a key factor in the decision making process of society, along with that comes the democratization of data analysis allowing anyone, not just statisticians, to draw conclusions from data. There is an undeniable ease to summary statistics that makes them appealing, but here at Fulcrum, we often encounter clients throwing away

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