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Why It Matters

Companies across industries are increasingly implementing AI solutions to boost revenue and drive sales growth, creating opportunities for both technology providers and early adopters to gain competitive advantages in revenue optimization. Tracking these developments across fragmented industry coverage is challenging for investment decisions and market intelligence, making it difficult to identify which companies are leading AI revenue generation innovation and which are successfully implementing these technologies to enhance their profitability.

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What It Does

Using the functions available in the bigdata-research-tools package, you can create a comprehensive AI revenue generation ecosystem analysis tool that identifies technology providers, tracks customer adoption patterns, and maps provider-customer relationships using unstructured data from news sources. These functions are suitable for technology analysts, investors, and industry professionals to create tools that transform scattered AI signals into quantified market intelligence and identify investment opportunities in the AI revenue generation value chain.

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How It Works

This workflow combines dual-role classification, network analysis, and temporal tracking to deliver:
  • Provider vs. User identification distinguishing companies developing AI revenue generation solutions from those implementing them
  • Technology ecosystem mapping revealing relationships between solution providers and corporate users
  • Adoption timeline tracking showing how AI revenue generation implementation evolves across different companies
  • Market positioning analysis quantifying each company’s role and exposure in the AI revenue generation ecosystem

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A Real-World Use Case

This cookbook demonstrates the complete workflow through analyzing AI revenue generation technology dynamics across various industries using news data, showing how the system automatically identifies technology leaders, tracks customer adoption patterns, and reveals provider-customer networks through automated relationship analysis. Ready to get started? Let’s dive in! Open in GitHub

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Prerequisites

To run the AI Revenue Generation Market Analysis workflow, you can choose between two options:
  • 💻 GitHub cookbook
    • Use this if you prefer working locally or in a custom environment.
    • Follow the setup and execution instructions in the README.md.
    • API keys are required:
      • Option 1: Follow the key setup process described in the README.md
      • Option 2: Refer to this guide: How to initialise environment variables
        • ❗ When using this method, you must manually add the OpenAI API key: 
          # OpenAI credentials
OPENAI_API_KEY = "<YOUR_OPENAI_API_KEY>"
  • 🐳 Docker Installation
    • Docker installation is available for containerized deployment.
    • Provides an alternative setup method with containerized deployment, simplifying the environment configuration for those preferring Docker-based solutions.

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Setup and Imports

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Async Compatibility Setup

Run this cell first - Required for Google Colab, Jupyter Notebooks, and VS Code with Jupyter extension: 
try:
    import asyncio
    asyncio.get_running_loop()
    import nest_asyncio; nest_asyncio.apply()
    print("✅ nest_asyncio applied")
except (RuntimeError, ImportError):
    print("✅ nest_asyncio not needed")
Below is the Python code required for setting up our environment and importing necessary libraries. 
import pandas as pd
import matplotlib.pyplot as plt

from src.tool import plot_top_companies_by_sector, transform_to_reference_format, identify_basket_and_plot_confidence, top_companies_time
from src.search import search_by_any  
from src.labeling import run_ai_revenue_generation_prompt
from src.network_analysis import provider_adopter_net

from bigdata_client import Bigdata
from bigdata_client.models.search import DocumentType
from bigdata_research_tools.excel import ExcelManager

# Setup for Plotly
import plotly.io as pio
import plotly.graph_objects as go

try:
    import os
    if 'JUPYTERHUB_SERVICE_PREFIX' in os.environ or 'JPY_SESSION_NAME' in os.environ:
        pio.renderers.default = 'jupyterlab'
        print("✅ Plotly configured for JupyterLab")
    else:
        pio.renderers.default = 'plotly_mimetype+notebook'
        print("✅ Plotly configured for Jupyter/VS Code")
except:
    pio.renderers.default = 'notebook'
    print("✅ Plotly configured with fallback renderer")

interactive_plots = True  # Set to False to generate static plots

# Define output file paths for our results
output_dir = "output"
os.makedirs(output_dir, exist_ok=True)

export_path = f"{output_dir}/ai_revenue_generation_analysis_results.xlsx"

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Defining your AI Revenue Generation Analysis Parameters

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Fixed Parameters

  • Provider Theme (provider_theme): The central concept to explore for AI revenue generation technology providers
  • User Theme (user_theme): The concept to explore for companies using AI for revenue generation in their operations
  • Provider Sentences (provider_sentences_list): Sentences used to identify companies providing AI revenue generation solutions
  • User Sentences (user_sentences_list): Sentences used to identify companies using AI revenue generation technology
  • Document Type (document_type): Specify which documents to search over (transcripts, filings, news)
# ===== Fixed Parameters =====

# AI Revenue Generation Provider Theme
provider_theme = "Companies providing AI solutions for revenue generation"

# AI Revenue Generation User Theme  
user_theme = "Companies using AI to increase their own revenue"

# Sentences expressing AI revenue generation provider narratives
provider_sentences_list = [
    'Company develops AI solutions for revenue generation',
    'Firm provides AI-powered revenue optimization tools',
    'Organization offers AI automation for sales growth',
    'Corporation supplies AI-driven revenue enhancement solutions',
    'Company creates AI platforms for revenue increase',
    'Business develops artificial intelligence revenue optimization',
    'Enterprise manufactures AI sales enhancement systems',
    'Firm provides AI-powered marketing automation for revenue growth',
    'Company offers AI solutions for sales optimization',
    'Organization develops AI tools for revenue enhancement',
    'Corporation creates AI software for sales management',
    'Business supplies AI automation for revenue generation',
    'Enterprise offers AI-driven sales optimization',
    'Company develops AI platforms for revenue growth',
    'Firm creates artificial intelligence sales tools',
    'Organization provides AI solutions for marketing optimization',
    'Corporation develops AI systems for revenue management',
    'Business offers AI automation for sales efficiency',
    'Enterprise creates AI tools for revenue optimization',
    'Company supplies AI-powered revenue generation solutions'
]

# Sentences expressing AI revenue generation user narratives
user_sentences_list = [
    'Company implements AI for revenue generation',
    'Firm adopts AI automation to boost sales',
    'Organization uses AI to increase revenue',
    'Corporation deploys AI for sales growth',
    'Business implements artificial intelligence for revenue enhancement',
    'Enterprise adopts AI to improve sales performance',
    'Company deploys AI solutions to boost revenue',
    'Organization implements AI for sales optimization',
    'Corporation uses AI to enhance sales and increase revenue',
    'Business adopts AI for revenue management',
    'Organization uses AI to optimize sales processes for revenue generation',
    'Corporation adopts AI for revenue optimization',
    'Enterprise uses AI automation for revenue enhancement',
    'Industries integrating AI for financial gain',
    'Companies adopting AI technologies for revenue generation',
    'Organizations utilizing AI to drive revenue',
    'Corporations utilizing AI to increase profits',
    'Organizations using AI to drive financial performance',
    'Enterprises utilizing AI solutions for profit increase',
    'Companies harnessing AI for revenue improvement'
]

# Document Configuration
document_type = DocumentType.NEWS

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Customizable Parameters

  • Watchlist (my_watchlist_id): The set of companies to analyze. This is the ID of your watchlist on the watchlist section of the app. If set to None, each company referenced in the retrieved chunks will be analyzed.
  • Model Selection (llm_model): The LLM model used to label search result document chunks and generate summaries
  • Frequency (freq): The frequency of the date ranges to search over. Supported values:
    • Y: Yearly intervals.
    • M: Monthly intervals.
    • W: Weekly intervals.
    • D: Daily intervals. Defaults to M.
  • Time Period (start_date and end_date): The date range over which to run the analysis
  • Document Sources (sources): Specify set of sources within a document type, for example which news outlets (available via Bigdata API) you wish to search over
  • Rerank Threshold (rerank_threshold): By setting this value, you’re enabling the cross-encoder which reranks the results and selects those whose relevance is above the percentile you specify (0.7 being the 70th percentile). More information on the re-ranker can be found here.
  • Document Limit (document_limit): The maximum number of documents to return per query to Bigdata API
  • Batch Size (batch_size): The number of entities to include in a single batched query
# ===== Customizable Parameters =====

# Company Universe (from Watchlist)
my_watchlist_id = None  # Replace with your watchlist ID
if my_watchlist_id is not None:  
    watchlist = bigdata.watchlists.get(my_watchlist_id)
    companies = bigdata.knowledge_graph.get_entities(watchlist.items)
else:
    companies = None

# LLM Specification
llm_model = "openai::gpt-4o-mini"

# Search Frequency
search_frequency = 'M'

# Enable/Disable Reranker
rerank_threshold = None

# Specify Time Range
start_date = "2024-01-01"
end_date = "2024-06-30"

# Document Limits
document_limit = 100

# Others
batch_size = 10

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Retrieve Content using Bigdata’s Search Capabilities

With the AI revenue generation narratives and analysis parameters, you can leverage the Bigdata API to run a search on company news for both provider and user indicators. 
df_sentences_provider = search_by_any(
    sentences=provider_sentences_list,
    start_date=start_date,
    end_date=end_date,
    scope=document_type,
    rerank_threshold=rerank_threshold,
    freq=search_frequency,
    document_limit=document_limit,
    batch_size=batch_size,
)

df_sentences_user = search_by_any(
    sentences=user_sentences_list,
    start_date=start_date,
    end_date=end_date,
    scope=document_type,
    rerank_threshold=rerank_threshold,
    freq=search_frequency,
    document_limit=document_limit,
    batch_size=batch_size,
)

# Combine and process all data
df_all = pd.concat([df_sentences_provider, df_sentences_user], ignore_index=True)
df_all = df_all.drop_duplicates(subset=['document_id', 'sentence_id', 'headline', 'entity_id', 'entity_name', 'text'])

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Label the Results

Use an LLM to analyze each document chunk and determine its relevance to AI revenue generation technology, classifying companies as either providers (P), users (A), or unrelated (U). 
df_all = df_all.rename(columns={
    'entity_id': 'rp_entity_id',
    'document_id': 'rp_document_id',
})

df_all_labeled = run_ai_revenue_generation_prompt(
    sentences=df_all,
    masked_2=True,
    batch_size=10, 
    open_ai_credentials=OPENAI_API_KEY
)

# Transform and separate by role
df_all_labeled_transform = transform_to_reference_format(df_all_labeled)
df_providers = df_all_labeled_transform[df_all_labeled_transform["label"] == "P"].copy()
df_providers.reset_index(drop=True, inplace=True)
df_users = df_all_labeled_transform[df_all_labeled_transform["label"] == "A"].copy()
df_users.reset_index(drop=True, inplace=True)

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Visualizations

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Sector-Based AI Revenue Generation Technology Visualization

The following visualizations provide a sector-by-sector breakdown of companies involved in AI revenue generation technology. These charts help identify:
  • Industry Patterns: Which sectors demonstrate the highest AI revenue generation activity
  • Technology Leaders: Companies most frequently mentioned in AI revenue generation contexts within each sector
  • Story Context: Detailed hover information reveals the specific headlines, motivations, and news excerpts driving each company’s positioning
Each bar represents the frequency of AI revenue generation mentions, with hover details providing the underlying news narrative that shaped each company’s role in the AI revenue generation ecosystem.

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Companies Providing AI Revenue Generation Solutions

This chart displays companies that have been most frequently mentioned as providers of AI revenue generation technology, organized by sector and ranked by total mention volume. The visualization reveals which companies and industries are leading the development and supply of AI solutions for sales optimization, revenue enhancement, and profit generation. 
plot_top_companies_by_sector(df_providers, min_companies=1, title_suffix="(AI Revenue Generation Providers)", top_sectors=4, interactive=interactive_plots)

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Companies Using AI for Revenue Generation

This complementary chart shows companies most frequently mentioned as users of AI revenue generation technology in their operations, organized by sector and ranked by total mention volume. It identifies firms investing in AI-powered sales optimization, implementing revenue enhancement systems, or boosting their profitability through AI-driven marketing and customer engagement strategies. 
plot_top_companies_by_sector(df_users, min_companies=1, title_suffix="(AI Revenue Generation Users)", top_sectors=4, interactive=interactive_plots)

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AI Revenue Generation Ecosystem Analysis

This comprehensive assessment combines both provider and user signals to create a complete picture of the AI revenue generation ecosystem. The analysis provides:
  • Total Exposure: Overall volume of AI revenue generation related news coverage for each company
  • Role Classification: Relative proportion of provider versus user mentions
  • Market Positioning: How companies rank in terms of their involvement in the AI revenue generation market
The stacked bar chart shows the percentage breakdown of provider (green) versus user (red) mentions for the top 40 most-mentioned companies. Companies with predominantly green bars are primarily technology providers, while those with red bars are mainly end-users implementing AI revenue generation solutions. 
companies_basket = identify_basket_and_plot_confidence(
    df_providers,
    df_users, 
    basket_size=40, 
    theme_name='AI Revenue Generation', 
    positive_label='Providers', 
    negative_label='Users', 
    interactive=interactive_plots
)

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Top Companies Time Tracking

This visualization tracks the newsflow arrival across time for the top 4 companies most mentioned as users in AI revenue generation contexts, providing transparency about news content and media attention over time. The weekly aggregation reveals temporal patterns in AI revenue generation adoption and implementation. 
top_companies_time(df_users, interactive=interactive_plots)

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Provider-User Network Analysis

This network visualization reveals relationships between AI revenue generation technology providers and users through co-mentions in news articles. The analysis identifies:
  • Technology Partnerships: Companies that frequently appear together in AI revenue generation contexts
  • Customer-Supplier Relationships: Connections between solution providers and implementing organizations
  • Market Clusters: Groups of companies operating in similar segments of the AI revenue generation ecosystem
Notable partnerships that may emerge include relationships between AI platform providers and enterprise users, marketing automation vendors and retail companies, and sales optimization technology developers working with early adopters to enhance their revenue generation capabilities. 
fig = provider_adopter_net(
    df_filtered_adopters=df_users,
    df_filtered_providers=df_providers,
    bigdata_cred=None,
    min_occurrences=3,  # Change this if you want to see fewer companies
    dash=False,
    interactive=interactive_plots
)

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Key Insights and Technology Stories

The AI revenue generation analysis reveals distinct sector-specific adoption patterns and strategic technology partnerships:

Technology Sector Split Leadership

Alphabet Inc. and NVIDIA Corp. tie as top providers, with Alphabet offering AI-powered advertising and cloud AI services while NVIDIA provides the GPU infrastructure powering AI revenue optimization, while Microsoft leads as the primary user, implementing AI across its Azure, Office, and enterprise sales platforms.

Amazon's Consumer Services Dominance

Amazon achieves unique dual leadership as both top provider and user in Consumer Services, leveraging its AWS AI services for other businesses while simultaneously using AI for its own marketplace optimization and customer targeting.

Financial Sector User Leadership

Wishpond Technologies Ltd. leads as the top user in the Financial sector, implementing AI for marketing automation and lead generation to enhance their revenue conversion processes.

Industrial Sector Diversity

Accenture and Spotify tie as top providers, with Accenture offering AI consulting services and Spotify developing AI-driven content personalization solutions, while Mindtickle Inc. leads as the primary user, implementing AI in their sales training and enablement platform.

Strategic Ecosystem Partnerships

The network analysis reveals sophisticated interconnections where Microsoft connects to specialized AI providers including Salesforce Inc. (CRM AI), HubSpot Inc. (marketing automation AI), and Oracle Corp. (enterprise AI applications). Infrastructure players NVIDIA and Dell Tech Inc. provide the computational backbone, while Shopify Systems Inc. integrates AI technologies from IBM Corp (enterprise AI), Alphabet Inc (machine learning APIs), and SalesLoft Inc (sales engagement AI). These partnerships demonstrate how AI revenue generation thrives through strategic collaboration across the technology stack.

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Export the Results

Export the data as Excel files for further analysis or to share with the team. 
try:
    # Create the Excel manager
    excel_manager = ExcelManager()

    # Define the dataframes and their sheet configurations
    df_args = [
        (df_providers, "AI Revenue Generation Providers", (0, 0)),
        (df_users, "AI Revenue Generation Users", (0, 0)),
        (companies_basket, "Technology Ecosystem Basket", (2, 4))
    ]

    # Save the workbook
    excel_manager.save_workbook(df_args, export_path)
    print(f"✅ Results exported to {export_path}")

except Exception as e:
    print(f"Warning while exporting to excel: {e}")

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Conclusion

The AI Revenue Generation Market Analysis provides a comprehensive automated framework for analyzing the AI revenue generation ecosystem across multiple industries. By systematically combining advanced information retrieval with LLM-powered classification, this workflow transforms unstructured news data into actionable intelligence for strategic decision-making. Through the automated analysis of AI revenue generation technology dynamics, you can:
  1. Identify technology leaders - Discover companies developing cutting-edge AI revenue generation solutions and those implementing these technologies in their operations
  2. Map ecosystem relationships - Reveal partnerships and customer-supplier relationships between solution providers and enterprise users through network analysis
  3. Track adoption patterns - Monitor how AI revenue generation implementation evolves across different sectors and time periods, identifying emerging trends and market momentum
  4. Analyze competitive positioning - Compare companies’ roles in the AI revenue generation ecosystem, distinguishing between technology developers and end-users
  5. Monitor sector-specific trends - Track how different industries approach AI revenue generation, from financial services automation to retail personalization strategies
Whether you’re conducting technology due diligence, building AI-focused investment strategies, or identifying revenue generation opportunities, this analysis automates complex research while delivering comprehensive insights. The dual-classification approach clearly separates solution providers from users, making it essential for systematic market assessment in the fast-moving AI revenue generation landscape.