September 11, 2023

Estimating worldwide adoption potential for innovative tobacco products

Building a market model that compile all information sources into reliable historical demand data.

Estimating worldwide adoption potential for innovative tobacco products

At a glance

Challenge

As global demand for reduced-risk tobacco products surged, country-specific differences in culture, product availability, and awareness posed challenges for accurate demand forecasting. Compiling reliable historical market data was further complicated by conflicting sources. The goal was to develop a market model that consolidated all available information into reliable historical demand data, enabling long-term country-level demand forecasts to support global strategic planning.

Solution

The methodology begins with a confidence data-mining algorithm to identify the most reliable data sources for compiling product-level historical demand by country. Surveys, third-party forecasts, demographic data, and similar-country adoption rates are then utilized to forecast the global evolution of reduced-risk products, including markets where these products are yet to launch. This is achieved by applying and fitting (Bass) diffusion curves using machine learning techniques.

Results

The implemented model offers reliable estimates for both historical and future demand for reduced-risk products. Through an intuitive web interface, stakeholders can validate forecasts, input new data, and simulate the impact of market drivers like inflation, regulation, and taxation on demand. This project empowered the company with user-friendly analytical demand planning tools while consolidating comprehensive market insights into a single company-wide platform.

Challenge

As global demand for reduced-risk tobacco products surged, country-specific differences in culture, product availability, and awareness posed challenges for accurate demand forecasting. Compiling reliable historical market data was further complicated by conflicting sources. The goal was to develop a market model that consolidated all available information into reliable historical demand data, enabling long-term country-level demand forecasts to support global strategic planning.

Approach

Solution

The methodology begins with a confidence data-mining algorithm to identify the most reliable data sources for compiling product-level historical demand by country. Surveys, third-party forecasts, demographic data, and similar-country adoption rates are then utilized to forecast the global evolution of reduced-risk products, including markets where these products are yet to launch. This is achieved by applying and fitting (Bass) diffusion curves using machine learning techniques.

Results

The implemented model offers reliable estimates for both historical and future demand for reduced-risk products. Through an intuitive web interface, stakeholders can validate forecasts, input new data, and simulate the impact of market drivers like inflation, regulation, and taxation on demand. This project empowered the company with user-friendly analytical demand planning tools while consolidating comprehensive market insights into a single company-wide platform.

Our
AI-generated
summary

Our AI-generated summary

Our AI-generated summary

With customers increasingly aware of the hazards of smoking, the global demand for reduced-risk tobacco products augmented massively. However, country-level differences in culture, product availability, and awareness make demand projections for each product a difficult challenge. Moreover, the existence of conflicting (historical) market size information means compiling reliable historical data is a tough task.

Our goal was to build a market model that would compile all information sources into reliable historical demand data, which could then be leveraged into long-term country-level demand forecasts to help worldwide strategic planning.

The first step of the developed methodology is a confidence data-mining algorithm that identifies the most reliable data sources for each country to compile product-level demand for historical years.

Our AI-generated summary

Our AI-generated summary

Distinct sources of information, such as surveys, third-party forecasts, demographic data, and similar-country adoption rates are then leveraged to forecast the evolution of reduced-risk products worldwide, including countries in which these products are yet to be launched. To do so, (bass) diffusion curves are applied and fitted through machine learning techniques.

The implemented model provides the organization with unique most-reliable estimates for both historical and future demand for reduced-risk products.

By compiling these figures within an intuitive web interface, different stakeholders can validate the forecasts, provide additional inputs whenever they become available, and simulate the impact of market drivers, such as inflation, regulation, and taxation, on the expected demand of each product.

The project enabled the company to feed each market with easy-to-use analytical demand planning capabilities while gathering in-depth market knowledge into a single company-wide platform.

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