MARKET INTELLIGENCE FROM THE INTERNET: AN ILLUSTRATION USING THE BIOMASS HEATING INDUSTRY

Adee Athiyaman,Thebe Magapa

doi:10.34109/ijefs.201911101

Abstract

Extant research on marketing strategy suggests that most companies underuse web intelligence as publicly available data on the Internet are considered hard to access and analyse. This paper demonstrates how biomass home heating businesses can utilise the Internet for data collection and business insights. The market structure of the biomass heating industry was identified using the Google Correlate algorithm. The production rule ‘that newer the product the higher is consumer search for the product’ was operationalised using the correlations of the concept ‘home heating equipment’. Intra-industry competition was assessed using Google’s brand impression analysis and firm behaviour and performance were modelled using a differential equation relating product sales to marketing expenditures. Empirical analysis reveals that the product form “biomass home heating” is growing, pellet stoves and fireplace inserts top the lists of “stove” searches, there are two competitive clusters of biomass firms and the marketing spending for the industry is well below its optimum level needed to increase and maintain sales. Keywords: Market intelligence, Biomass home heating, US biomass markets, Marketing optimisation, Google tools

Highlights

IntroductionEconomic, social and technological domains are impacting businesses around the globe
Rapid changes in political, economic, social and technological domains are impacting businesses around the globe
This paper demonstrates how the Internet can be used to gather market intelligence for a business; the biomass heating industry in the US is the unit of analysis

Summary

Introduction

Economic, social and technological domains are impacting businesses around the globe. Global economic growth has slowed to an annual average of 2.1 percent, a 42 percent decline from the 2010 average (IMF Research Bulletin, 2016). In this competitive economy, winning companies utilise specific capabilities such as market research to understand and invest in pockets of growth. To identify competitors in the wood/ pellet stove category and wood/ pellet furnace category, we employed a distance-and-similarity measure. A subsequent hierarchical clustering of dij resulted in two competitive clusters each for the wood /pellet stove category and the wood/ pellet furnace category (Maddala & Lahiri, 1992). Competitive cluster 2 firms were larger than cluster 1 firms (Appendix 1)

Methods

Results

Discussion

Conclusion