Market Demand Consulting

Demand Sizing and Forecasting

Demand sizing and forecasting is the core of market demand consulting. We conduct comprehensive demand assessments that quantify current market demand, identify growth trends, and project future demand under different scenarios. Our approach combines bottom-up analysis of customer segments and usage patterns with top-down analysis of macroeconomic and industry trends. We use multiple forecasting methodologies — including time series analysis, leading indicator models, and scenario planning — to provide a range of demand projections that account for uncertainty. The result is a robust demand model that consultants can use to support investment decisions, capacity planning, and strategic planning.

Customer Willingness-to-Pay Analysis

Understanding what customers are willing to pay is essential for accurate demand forecasting and pricing strategy. We conduct willingness-to-pay research using methodologies including conjoint analysis, Van Westendorp price sensitivity measurement, Gabor-Granger direct price questioning, and Becker-DeGroot-Marschak incentive-aligned techniques. Our research captures willingness to pay across different customer segments, purchase contexts, and competitive scenarios. The insights enable consultants to build demand models that reflect the relationship between price and demand — the demand curve — and to identify the price points that optimise revenue, volume, or profit depending on the client's objectives.

Demand Driver Identification

Demand does not exist in a vacuum — it is driven by a complex set of factors including economic conditions, demographic trends, technological change, regulatory shifts, competitive actions, and social and cultural factors. We conduct demand driver analysis that identifies and quantifies the factors that most strongly influence demand for a specific product or service. Our approach combines statistical analysis of historical data with qualitative research with customers, industry experts, and market observers. We build demand driver models that show how changes in key drivers will affect demand, enabling consultants to forecast demand more accurately and to identify which drivers the client can influence through strategic action.

Seasonal and Cyclical Demand Patterns

Many markets exhibit seasonal or cyclical demand patterns that must be understood for effective forecasting and planning. We analyse historical demand data to identify seasonal patterns, cyclical trends, and other temporal variations in demand. Our research identifies the underlying causes of seasonal and cyclical patterns — weather, holidays, annual budgeting cycles, industry events, economic cycles — and assesses whether these patterns are stable or changing. We also analyse how seasonal and cyclical patterns vary across geographies, customer segments, and product categories. The insights enable consultants to build forecasting models that account for temporal demand variation and to develop strategies for managing demand volatility.

What is the difference between demand sizing and demand forecasting?

Demand sizing measures the current level of demand in a market — how many units are purchased, by whom, at what price, and in which channels. Demand forecasting projects how demand will evolve in the future, typically over a 1-10 year horizon, based on historical trends, demand drivers, and anticipated changes in market conditions. Sizing provides the baseline; forecasting provides the forward view. Both are essential components of market demand consulting engagements.

How do you measure customer willingness to pay?

Willingness to pay is measured through several research techniques. Conjoint analysis presents respondents with product profiles at different price points and infers price sensitivity from their trade-off decisions. Van Westendorp analysis asks respondents to identify price thresholds. Gabor-Granger presents specific prices and asks purchase likelihood. The most appropriate method depends on the product category, market context, and the specific decisions the research will support. We often use multiple methods to triangulate willingness to pay estimates.

How do you identify the drivers of market demand?

Demand driver identification combines quantitative analysis of historical data with qualitative research. Statistically, we use regression analysis, correlation analysis, and causal modelling to identify relationships between potential demand drivers and actual demand. Qualitatively, we conduct interviews with customers, industry experts, and company stakeholders to understand what factors influence purchase decisions. The combined approach provides a comprehensive understanding of which drivers matter most and how they interact.

How do you forecast demand in new or emerging markets?

Forecasting demand in new markets is challenging because historical data may not exist. In these contexts, we use analogy-based forecasting — drawing on demand patterns in comparable markets — combined with expert input through Delphi panels and scenario planning. We also conduct primary research with potential customers to assess purchase intentions and willingness to pay. The forecast is typically presented as a range rather than a single point estimate, with different scenarios reflecting different assumptions about market development.

What statistical methods do you use for demand forecasting?

We use a range of statistical methods depending on the data available and the forecasting context. Common methods include time series analysis, exponential smoothing, ARIMA modelling, regression analysis, and structural equation modelling. For more complex forecasting problems, we use machine learning techniques including random forests and neural networks. The choice of method depends on factors including data availability, forecast horizon, the stability of demand patterns, and the specific decisions the forecast will support.