We addressed this challenge by developing an analytical framework based on socio-economic groups to forecast demand. Multiple sources of data including client reports, census and commercial third party data, our analysts determined the market-level opportunity and likely product share over the next five years.
Socio-economic groups were further split by rural or urban residence. These sub-tiers were profiled based on mobile device usage, attitudes, and purchase priorities. For example, consumers in the two most affluent urban groups consider mobile devices a necessity and placed a higher priority on features than price.
Finally, product profiles were mapped to each profile to forecast product demand.
Our consultants quantified the opportunities based on market size and revenue potential according to region and audience composition. This information guided the client’s planning and supply chain decisions. Additionally the social status profiles provided insight regarding digital engagement preferences, product attitudes and spending flexibility, leading to actionable marketing recommendations to price, features and tailor messaging across social classes to maximize mobile device market share.
It’s important to remember that the primary value from big data comes not from the data in its raw form, but from the processing and analysis of it and the products, services that emerge analysis.
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We adopted a bottom-up approach based on socio-economic status to address the mobile market opportunity:
Distribute household population across respective socio-economic tiers based on government reports
Position device and features across social classes, based on multiple factors such as perceived need and cost
Identify target social tiers and demographic distribution for device and feature demand
Estimate populations in target social tiers currently using or projected to use a particular mobile device or feature