METHODOLOGY FRAMEWORK · EPIGNOSIS INSIGHTS

How We Build a Number worth Trusting

Every market estimate published by Epignosis Insights is triangulated from three independent streams  secondary intelligence, primary fieldwork, and expert validation  before it is released. This document sets out that framework end to end, phase by phase, with the tables, formulas, and diagrams that underpin it.

Research Design Framework

The design is chosen by what the question needs, not by default. Each engagement is classified into one of three research designs, often combined in sequence within a single study.

Exploratory

Used when the problem itself is loosely defined new category entry, whitespace mapping, early trend scanning.

•      Expert / KOL interviews

•      Literature & patent scan

•      Focus groups, unstructured

Descriptive

Used to size, segment, and profile a defined market or audience with statistical confidence.

•      Structured surveys (CATI/CAWI)

•      Syndicated data mining

•      Cross-sectional studies

Causal

Used to isolate cause-effect relationships  pricing elasticity, campaign lift, product-attribute impact on choice.

•      Conjoint / MaxDiff

•      A/B and test-control designs

•      Regression & driver analysis

Secondary Research the Intelligence Base

Desk research establishes the scaffolding: market boundaries, historical trends, competitive structure, and regulatory context sourced in a strict tier hierarchy.

Tier 3 sources are never used as a standalone basis for a market number  they are corroborating signals only, cross-checked against Tier 1/2.

Primary Research Quantitative & Qualitative

Quantitative

Structured instruments run at statistically defensible sample sizes to produce measurable, projectable results.

•      CATI / CAWI structured surveys

•      Online panels (proprietary & third-party)

•      Conjoint, MaxDiff, Van Westendorp pricing

•      Retail audits & point-of-sale tracking

Qualitative

Unstructured or semi-structured engagement to surface the "why" behind quant patterns.

•      In-depth interviews (IDIs) with decision-makers

•      Focus group discussions (FGDs)

•      Expert / KOL consultations

•      Ethnography & shop-alongs

Sampling Methodology

Sample design follows a probability approach wherever a sampling frame exists (customer lists, registries, panels); stratified or quota sampling is used when the population is heterogeneous across known segments; snowball/purposive sampling is reserved for hard-to-reach expert populations.

n = (Z² × p × (1-p)) / e²      finite population correction: n′ = n / (1 + (n-1)/N)

Z = confidence coefficient · p = expected proportion (0.5 if unknown) · e = margin of error · N = population size

Figure 2  Required sample size grows sharply as margin of error tightens (95% confidence).

Market Sizing Approach

Every market number is built two ways independently, then reconciled  a discrepancy beyond ±8–10% triggers a fresh review of assumptions before publication.

Top-down

Start from a macro total (industry/GDP-linked base) and apply successive filters  relevant sub-segment share, geography, applicable use-cases  to isolate the addressable market.

Bottom-up

Build from the smallest unit up  unit sales × average price, or per-company revenue aggregated across all identified players  then gross up for coverage gaps.

Figure 3  Top-down and bottom-up estimates are reconciled before a triangulated size is published.

Data Validation & Triangulation

Source cross-check

Every Tier 1 figure is matched against at least one independent source before use.

Expert sanity check

Draft estimates are reviewed with 2–3 industry experts for directional plausibility.

Internal peer review

A second analyst not on the project re-derives the sizing logic independently.

Figure 4  Evidence weighting in a typical estimate.

Figure 5  Where the project timeline actually goes.

Analytical Frameworks Applied

PESTEL

Political, economic, social, technological, environmental, legal factors shaping category trajectory.

Porter's Five Forces

Supplier/buyer power, entry barriers, substitutes, competitive rivalry.

Value chain mapping

Raw material → processing → distribution → end-use, with margin pools at each stage.

SWOT / competitive benchmarking

Player-level strengths, weaknesses, and strategic moves (capacity, M&A, product launches).

Forecasting & Modelling

CAGR = (Ending Value / Beginning Value)^(1/n) − 1

Base forecasts use trend extrapolation and CAGR modelling anchored to historical data; complex categories layer in regression against macro drivers (GDP, capex cycles, regulatory shifts) and scenario modelling (base / optimistic / conservative) where volatility is high.

Quality Assurance & Research Ethics

✓  Informed consent & respondent confidentiality on every primary study

✓  Panel quality checks  speeders, straight-liners, attention traps removed

✓  Interviewer training & back-checks (min. 10% of CATI sample)

✓  Data cleaning: logic checks, outlier review, range validation

✓  Bias controls: question randomization, balanced scales

✓  Version-controlled data files with full audit trail

✓  Compliance with ESOMAR / MRS guidelines

✓  Independent second-analyst review before client delivery

End-to-End Research Process

Figure 6  The full engagement runs scoping through delivery in six phases.

Typical timeline: 6–10 weeks end to end for a standard syndicated study; 10–16 weeks for custom multi-market primary programs.