Deep Learning Framework for Measuring the Digital Strategy of Companies from Earnings Calls

TL;DR

This paper employs advanced NLP models to analyze earnings calls and identify four distinct digital strategy patterns among Fortune 500 companies, providing insights into strategic adoption and its implications.

Contribution

It introduces a novel application of Transformer-based NLP models and clustering analysis to categorize digital strategies from unstructured earnings call data.

Findings

Identified four main digital strategy patterns: product led, customer experience led, service led, and efficiency led.

Demonstrated the effectiveness of Transformer models in understanding complex conversational data.

Provided an empirical baseline for future research and strategic assessment.

Abstract

Companies today are racing to leverage the latest digital technologies, such as artificial intelligence, blockchain, and cloud computing. However, many companies report that their strategies did not achieve the anticipated business results. This study is the first to apply state of the art NLP models on unstructured data to understand the different clusters of digital strategy patterns that companies are Adopting. We achieve this by analyzing earnings calls from Fortune Global 500 companies between 2015 and 2019. We use Transformer based architecture for text classification which show a better understanding of the conversation context. We then investigate digital strategy patterns by applying clustering analysis. Our findings suggest that Fortune 500 companies use four distinct strategies which are product led, customer experience led, service led, and efficiency led. This work provides an empirical baseline for companies and researchers to enhance our understanding of the field.