In today’s highly competitive landscape, accurately sizing your target market is critical to driving business growth and strategic decision-making. Many organizations struggle to consolidate diverse data sources, reconcile inconsistent datasets, and derive actionable insights that guide investment, product development, and go-to-market (GTM) decisions.
The challenge lies in combining external market data with proprietary internal data to deliver are liable, dynamic, and forward-looking view of market opportunities across customer segments, industries, and geographies.
The Market Sizing Project provided valuable insights to help prioritize and capture their target markets. This deployment integrated LLM powered analytics with advanced modelling techniques to ensure decisions are grounded in accurate, comprehensive, and predictive market intelligence.
We leveraged a Large Language Model (LLM) to identify and size the target market by synthesizing structured and unstructured data from multiple sources. The process involved:
Data Inputs Market Data (public sources)
Population, Employment, Occupation, Education, Company Size, Industry, Internet Usage, Demographics
In-House Data
Custom primary datasets including Customer Groups, Creative Category, Document Workflow, Use Cases, Device Types
1. Reconcile Data Sources
Integrate market and in-house data using LLM-based entity matching and normalization
Ensure consistency across regional and categorical classifications
2. Apply Data Fusion Techniques
Merge structured datasets with unstructured text data (e.g., survey responses, CRM notes) for richer insights
Use embeddings to map similar entities and fill information gaps
3. Estimate Unavailable Results
Apply predictive modeling to infer missing market metrics such as segment penetration or future demand
Use LLM reasoning to cross-validate assumptions and surface anomalies
4. Extrapolate Future Results
Forecast future market size based on trend analysis, macroeconomic data, and modeled customer adoption rates
Create scenario-based projections to inform strategic planning and risk management
The final deliverables provide a quantified view of customer segments and categories as defined by the client. Results are presented through:
Segment Size Estimates (TAM, SAM, SOM)
Category Growth Rates
Regional Opportunity Maps
Scenario Forecasts (base, optimistic, conservative)
By integrating LLM-powered analytics into the market sizing process, organizations can:
Accelerate Insights: Automate data synthesis and reduce manual research
Improve Accuracy: Reconcile inconsistencies and validate assumptions using AI-based cross-checking
Enable Dynamic Strategy: Update forecasts and segmentation models as new data becomes available
Enhance Decision-Making: Align strategy, financial planning, and product investment with real market opportunities
Financial Reporting: Align financial forecasts with market potential
Strategy & Planning: Build robust portfolio and category strategies including Geo Expansion and GTM
Marketing Campaigns: Target high-value segments and craft compelling messages
Product Development: Align features and innovation with market needs
This Market Sizing Case Study demonstrates how AI and LLMs can revolutionize traditional market research and strategic planning. By combining reliable data input, advanced modelling techniques, and AI-driven analytics, businesses gain a clear, data-backed view of where to play and how to SCALE in an evolving marketplace.
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