DOI: 10.7910/dvn/gdth8w
title: Price Optimization - Raw Source Data

description: &lt;p&gt;This dataset contains raw, unprocessed data files pertaining to the management tool group focused on &lt;b&gt;'Price Optimization'&lt;/b&gt;, including related concepts like Dynamic Pricing and Price Optimization Models. The data originates from five distinct sources, each reflecting different facets of the tool's prominence and usage over time. Files preserve the original metrics and temporal granularity before any comparative normalization or harmonization.&lt;/p&gt;
&lt;p&gt;&lt;b&gt;Data Sources & File Details:&lt;/b&gt;&lt;/p&gt;
&lt;ul&gt;
 &lt;li&gt;&lt;b&gt;Google Trends File (Prefix: &lt;code&gt;GT_&lt;/code&gt;):&lt;/b&gt;
 &lt;ul&gt;
 &lt;li&gt;Metric: Relative Search Interest (RSI) Index (0-100 scale).&lt;/li&gt;
 &lt;li&gt;Keywords Used: &lt;code&gt;"price optimization" + "dynamic pricing" + "price optimization strategy"&lt;/code&gt;&lt;/li&gt;
 &lt;li&gt;Time Period: January 2004 - January 2025 (Native Monthly Resolution).&lt;/li&gt;
 &lt;li&gt;Scope: Global Web Search, broad categorization.&lt;/li&gt;
 &lt;li&gt;Extraction Date: Data extracted January 2025.&lt;/li&gt;
 &lt;li&gt;Notes: Index relative to peak interest within the period for these terms. Reflects public/professional search interest trends. Based on probabilistic sampling. Source URL: &lt;a href="https://trends.google.com/trends/explore?date=all&q=%22price%20optimization%22+%2B+%22dynamic%20pricing%22+%2B+%22price%20optimization%20strategy%22&hl=es" target="_blank"&gt;Google Trends Query&lt;/a&gt;&lt;/li&gt;
 &lt;/ul&gt;
 &lt;/li&gt;
 &lt;li&gt;&lt;b&gt;Google Books Ngram Viewer File (Prefix: &lt;code&gt;GB_&lt;/code&gt;):&lt;/b&gt;
 &lt;ul&gt;
 &lt;li&gt;Metric: Annual Relative Frequency (% of total n-grams in the corpus).&lt;/li&gt;
 &lt;li&gt;Keywords Used: &lt;code&gt;Price Optimization + Pricing Optimization + Dynamic Pricing Models + Optimal Pricing + Dynamic Pricing&lt;/code&gt;&lt;/li&gt;
 &lt;li&gt;Time Period: 1950 - 2022 (Annual Resolution).&lt;/li&gt;
 &lt;li&gt;Corpus: English. Parameters: Case Insensitive OFF, Smoothing 0.&lt;/li&gt;
 &lt;li&gt;Extraction Date: Data extracted January 2025.&lt;/li&gt;
 &lt;li&gt;Notes: Reflects term usage frequency in Google's digitized book corpus. Subject to corpus limitations (English bias, coverage). Source URL: &lt;a href="https://books.google.com/ngrams/graph?content=Price+Optimization+%2B+Pricing+Optimization+%2B+Dynamic+Pricing+Models+%2B+Optimal+Pricing+%2B+Dynamic+Pricing&year_start=1950&year_end=2022&corpus=en&smoothing=0" target="_blank"&gt;Ngram Viewer Query&lt;/a&gt;&lt;/li&gt;
 &lt;/ul&gt;
 &lt;/li&gt;
 &lt;li&gt;&lt;b&gt;Crossref.org File (Prefix: &lt;code&gt;CR_&lt;/code&gt;):&lt;/b&gt;
 &lt;ul&gt;
 &lt;li&gt;Metric: Absolute count of publications per month matching keywords.&lt;/li&gt;
 &lt;li&gt;Keywords Used: &lt;code&gt;("price optimization" OR "pricing optimization" OR "dynamic pricing" OR "optimal pricing" OR "dynamic pricing models") AND ("management" OR "strategy" OR "revenue" OR "pricing" OR "model" OR "analysis")&lt;/code&gt;&lt;/li&gt;
 &lt;li&gt;Time Period: 1950 - 2025 (Queried for monthly counts based on publication date metadata).&lt;/li&gt;
 &lt;li&gt;Search Fields: Title, Abstract.&lt;/li&gt;
 &lt;li&gt;Extraction Date: Data extracted January 2025.&lt;/li&gt;
 &lt;li&gt;Notes: Reflects volume of relevant academic publications indexed by Crossref. Deduplicated using DOIs; records without DOIs omitted. Source URL: &lt;a href="https://search.crossref.org/search/works?q=(%22price+optimization%22+OR+%22pricing+optimization%22+OR+%22dynamic+pricing%22+OR+%22optimal+pricing%22)+AND+(%22management%22+OR+%22strategy%22+OR+%22revenue%22)&from_ui=yes" target="_blank"&gt;Crossref Search Query&lt;/a&gt;&lt;/li&gt;
 &lt;/ul&gt;
 &lt;/li&gt;
 &lt;li&gt;&lt;b&gt;Bain & Co. Survey - Usability File (Prefix: &lt;code&gt;BU_&lt;/code&gt;):&lt;/b&gt;
 &lt;ul&gt;
 &lt;li&gt;Metric: Original Percentage (%) of executives reporting tool usage.&lt;/li&gt;
 &lt;li&gt;Tool Names/Years Included: Price Optimization Models (2004, 2008, 2010, 2012, 2014, 2017).&lt;/li&gt;
 &lt;li&gt;Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector.&lt;/li&gt;
 &lt;li&gt;Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., et al., various years: 2003, 2007, 2009, 2011, 2013, 2015, 2017). Note: Tool potentially not surveyed before 2004 or after 2017 under this specific name.&lt;/li&gt;
 &lt;li&gt;Data Compilation Period: July 2024 - January 2025.&lt;/li&gt;
 &lt;li&gt;Notes: Data points correspond to specific survey years. Sample sizes: 2004/960; 2008/1430; 2010/1230; 2012/1208; 2014/1067; 2017/1268.&lt;/li&gt;
 &lt;/ul&gt;
 &lt;/li&gt;
 &lt;li&gt;&lt;b&gt;Bain & Co. Survey - Satisfaction File (Prefix: &lt;code&gt;BS_&lt;/code&gt;):&lt;/b&gt;
 &lt;ul&gt;
 &lt;li&gt;Metric: Original Average Satisfaction Score (Scale 0-5).&lt;/li&gt;
 &lt;li&gt;Tool Names/Years Included: Price Optimization Models (2004, 2008, 2010, 2012, 2014, 2017).&lt;/li&gt;
 &lt;li&gt;Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector.&lt;/li&gt;
 &lt;li&gt;Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., et al., various years: 2003, 2007, 2009, 2011, 2013, 2015, 2017). Note: Tool potentially not surveyed before 2004 or after 2017 under this specific name.&lt;/li&gt;
 &lt;li&gt;Data Compilation Period: July 2024 - January 2025.&lt;/li&gt;
 &lt;li&gt;Notes: Data points correspond to specific survey years. Sample sizes: 2004/960; 2008/1430; 2010/1230; 2012/1208; 2014/1067; 2017/1268. Reflects subjective executive perception of utility.&lt;/li&gt;
 &lt;/ul&gt;
 &lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;&lt;b&gt;File Naming Convention:&lt;/b&gt; Files generally follow the pattern: &lt;code&gt;PREFIX_Tool.csv&lt;/code&gt;, where the PREFIX indicates the data source:&lt;/p&gt;
&lt;ul&gt;
 &lt;li&gt;&lt;code&gt;GT_&lt;/code&gt;: Google Trends&lt;/li&gt;
 &lt;li&gt;&lt;code&gt;GB_&lt;/code&gt;: Google Books Ngram&lt;/li&gt;
 &lt;li&gt;&lt;code&gt;CR_&lt;/code&gt;: Crossref.org (Count Data for this Raw Dataset)&lt;/li&gt;
 &lt;li&gt;&lt;code&gt;BU_&lt;/code&gt;: Bain & Company Survey (Usability)&lt;/li&gt;
 &lt;li&gt;&lt;code&gt;BS_&lt;/code&gt;: Bain & Company Survey (Satisfaction)&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;The essential identification comes from the PREFIX and the Tool Name segment.&lt;/p&gt;
&lt;p&gt;This dataset resides within the '&lt;a href="https://dataverse.harvard.edu/dataverse/mgmt_raw_data" target="_blank"&gt;Management Tool Source Data (Raw Extracts)&lt;/a&gt;' Dataverse.&lt;/p&gt;
description: <strong>Note on the Dataset: Management Tools Lifecycle Analysis</strong>
<strong>Overview and Context</strong><br>This dataset provides raw empirical data for the Management Tools Lifecycle Analysis project, an interdisciplinary study examining the longitudinal evolution of 23 management tool groups through bibliometric and statistical analysis of heterogeneous data sources: Google Trends, Google Books Ngram Viewer, Crossref.org, and Bain & Company’s usability and satisfaction surveys. Hosted at https://github.com/Wise-Connex/Management-Tools-Analysis/tree/main/Informes, the dataset underpins a series of 138 technical reports spanning over 20,000 pages of quantitative analysis, systematically characterizing patterns of adoption, diffusion, and decline of these tools within organizational contexts.
The project is framed within Diomar Añez’s doctoral research, titled "Ontological Dichotomy in 'Management Fads': A Perspective from the Paradoxes of the Transorganizational Ecosystem". This inquiry investigates the emergence, diffusion, and persistence/obsolescence of technological-administrative innovations—conceptualized as tools, techniques, philosophies, or management approaches exhibiting potentially cyclical trajectories. Moving beyond phenomenological descriptions of these cycles, the research probes their causal underpinnings, exploring onto-anthropological, microeconomic, and organizational dimensions. It posits that a management tool’s longevity depends not solely on its intrinsic efficacy but on its capacity to mediate inherent organizational paradoxes, such as stability versus innovation or continuity versus disruption.
<strong>Purpose and Contributions</strong><br>The dataset and associated technical reports aim to detect and characterize systematic patterns in the adoption and use of management tools, assessing whether their behavior aligns with the construct of “management fads” (marked by boom-and-bust cycles) or conforms to alternative models of diffusion and persistence. This exploratory and descriptive phase, grounded in quantitative methods, delivers an unprecedented empirical foundation in its scope and granularity, contributing to management sciences with a longitudinal data repository and advanced statistical analyses. These encompass time-series modeling (ARIMA), comparative trend analysis, trend means, cycle analysis, Fourier transform, correlation maps, linear regression, principal component analysis (PCA), and visualization of PC1 vs. PC2 loading plots, alongside trend visualizations that elucidate complex patterns.
Complementing this, the doctoral dissertation employs a qualitative approach to investigate underlying causal mechanisms and social processes, examining how contextual factors, power structures, social networks, and legitimation processes shape perceptions of a tool’s value and utility. The integration of quantitative (reports/data) and qualitative (dissertation) methodologies offers a holistic and dialectical understanding of management innovation dynamics, positioning the project as a novel contribution to the study of organizational paradoxes and innovation diffusion.
<strong>Transparency and Reproducibility</strong><br>The raw dataset is made publicly available to ensure transparency, enable reproducibility of the analyses presented in the technical reports, and foster further quantitative research by scholars, students, consultants, and practitioners interested in management tool trends, innovation diffusion, and strategic management. The technical reports provide descriptive and exploratory analyses, while the doctoral dissertation delves into qualitative factors driving tool adoption or abandonment, collectively laying a robust foundation for future research.
<strong>Authorship and Interdisciplinary Contributions</strong><br>The project is led by:

<strong>Diomar Añez (Principal Investigator):</strong> Doctoral candidate in Management Sciences, with training in Philosophy, Communication, Business Valuation, Financial Planning, and Economics, and over 25 years of experience in organizational consulting. Añez designed the conceptual framework, selected the management tools, developed the theoretical foundation, and provided interpretive insights within the context of organizational dynamics and management theory.
<strong>Dimar Añez (Data Analysis and Programming):</strong> With studies in Computer and Electronic Engineering, and expertise in data science, machine learning, and big data. Dimar led automated data extraction, preprocessing, cleaning, statistical modeling (including time-series analysis, linear regression, PCA, and forecasting techniques), and visualization, ensuring analytical rigor and scalability using Python and advanced software tools.