Deep Learning Framework for Measuring the Digital Strategy of Companies from Earnings Calls (2020)

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 ana-lyzing earnings calls from Fortune’s 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.

## introduction the use of digital technologies is transforming the modern economy with significant implications for businesses. as a result, organizations are perceiving digital technologies to present both growth opportunities and existential threats @xcite . despite efforts to adopt such technologies, research demonstrates that the success rate in improving business performance is very low due to the lack of a coherent digital strategy @xcite . a positive research step to address this challenge is enhancing the understanding of current approaches to digital strategy. corporate documents are increasingly being used to understand the performance of organizations for various purposes. examples are, predicting expected returns from financial reports @xcite and measuring compliance from sustainability reports @xcite . in the present study, we use earnings calls transcripts, as they provide rich insights into companies' activities, specifically on their digital strategy. we quantify the approach taken by companies based on the progress made on the following three components related to the digital strategy @xcite : ## related work there have been a few recommendations for the digital strategy to be customer focused, product focused @xcite , or business model focused @xcite . however, an empirical investigation of digital strategy archetypes is still needed @xcite . a few nlp-driven publications have investigated the digital strategy of companies. this included network analytics of website tags @xcite , document clustering of companies' description @xcite , and keyword analysis of financial reports @xcite . while all these studies revealed insights into the digital strategy of companies, they were exploratory in nature with limited quantitative evaluation of digital strategy components. earnings calls transcripts are known to be rich data sources and considered to be more informative and insightful than company filing @xcite . moreover, they can be leveraged for text classification to identify decisions and activities that companies take @xcite . therefore, we propose text classification of digital strategy related topics from earnings calls transcripts as detailed in section 3. ## methodology there is no generic framework to measure company performance, as it can be topic specific. measuring digital strategy retrospectively is accomplished by identifying the progress made on its components, also known as digital maturity @xcite . from an nlp perspective, this requires identifying topics of interest in the text, followed by assigning a maturity score to it. the closest text classification task to this is aspect-based sentiment analysis, which is proven to be effective for mining aspects related to customers' opinions @xcite . therefore, we adapt this task to measure companies' digital strategy and refer to it as aspect-based maturity analysis (abma). in this case, we propose aspects that refer to 17 coarse-grained topics from the three components of digital strategy presented in the introduction (business value [4 aspects], strategy management [2 aspects], and digital technology [11 aspects]). the design and selection on the 17 topics was based on extensive literature review of various publications on digital strategy components (al-ali, 2020). we find multi-label classification suitable as the labels are not mutually exclusive. maturity, on the other hand, refers to the progress made with a given aspect over four discrete steps, including (1) plan, (2) pilot, (3) release, and (4) pioneer @xcite . we propose the use of multi-class classification given that maturity runs on a discrete scale. model labels are shown in text: "we're putting most of our efforts right now-are continuing to-into our robotics program. we think it's been a great addition to our fulfillment capacity." {aspects: [robotics, operations], maturity: released (3)} task labels aspect business value: digital products, digital customer experience, digital operations, digital business model strategy management: strategy enablers, strategy practices digital technologies: ai, analytics, internet of things, blockchain, cloud computing, mobile, social media, robotics, augmented reality, virtual reality, 3d printing maturity plan, pilot, release, pioneer ## experiment setup the objective of the experiment is to demonstrate the utility of abma in measuring the digital strategy of fortune 500 companies using earnings calls transcripts. the process included pre-processing and ## clustering of results companies may adopt various digital strategies based on sector, digital maturity, and business scope. to identify the common digital strategy archetypes, we clustered the data. as k-means clustering assumes an equal mean and variance, we standardized the data by applying minmax scaling and log transformation. there were few sparse features, so we chose 12 dense features (non-zero @xmath0. we also applied t-sne 5 for dimensionality reduction, as it significantly improved clustering performance @xcite . plotting the silhouette score for clustering showed multiple peaks demonstrating inherent hierarchy in the data. upon visual inspection, we found 10 clusters to be meaningful representation. the cluster map is illustrated in figure 1 (a) . investigating the cluster map revealed two main insights. first, clusters generally included companies in the same or a related sector. this indicates that the digital strategy can be sector specific. second, we found a few companies from various sector clustered with predominantly digital native companies from the technology sector. some examples are abb and siemens from the industrial sector are in cluster 2 due to their capabilities in automation and robotics technologies while the majority of their peer companies are in cluster 6. this indicates that some companies have made significant progress in digitally transforming their business. we investigate the clusters by calculating the mean maturity value for each feature across the 10 clusters. we found four distinct digital strategies shared between eight clusters, whereas the remaining two had very limited digital maturity. the four digital strategy archetypes are product led (cluster 2), customer experience led (cluster 4), service led (clusters 0,7,8,9), and efficiency led (clusters 3,6), as illustrated by the radar charts in figure 1 (b) . we also found that while companies lead with a specific business value, such as customer experience, the vast majority demonstrate some level of progress across all areas. therefore, focusing on a single area, as some authors argue, might not be practical due to the interdependencies between business functions @xcite . our findings have two main implications. first, managers can use the identified archetypes as a baseline for digital strategy formulation and as a tool to benchmark the progress made against relevant companies. second, researchers can build on our findings to investigate digital strategy further. ## conclusion in this study, we present a deep learning framework for measuring the digital strategy of companies by using earnings calls transcripts. in the process, we demonstrate the practical value of state-of-the-art nlp models beyond inference. the results from our experiment enhance the understanding of digital strategy by identifying four distinct digital strategy archetypes. our findings may serve as a baseline for companies attempting to formulate or benchmark their digital strategy as well as an empirical foundation for further research. for future studies, we recommend investigating the causal relationship between aspect maturity and financial performance.