Sample Socio Economic Aspect for Feasibility Studies
Socio economic status of Migrated workers in Perumbavoor Municipality
Bidi workers are often the most vulnerable group in society, most of which depend on Bidi rolling in rural India. They are struggling to survive even though low wages and contractors are in constant exploitation, lack of education and medical facilit
Nalco New Approach for MacrofoulingFull description
Nalco New Approach for Macrofouling
Socio Economic Aspect in Feasibility Study
Feasibility Study
Professional article by Douglas Monroe on the Nielsen Concerto for Bb ClarinetFull description
Professional article by Douglas Monroe on the Nielsen Concerto for Bb ClarinetFull description
New Economic Geography Theory and aplication
Professional article by Douglas Monroe on the Nielsen Concerto for Bb Clarinet
status pasienDeskripsi lengkap
A New Approach to Sight Singing - BerkowitzDescripción completa
• Current SES definition was adopted since 1970s, using one single measure: Routine Monthly Household Expenditure • Nielsen received inputs from client and industry on the need of revamping the SES to be more relevant to their current needs • While this measure was best at its time, recent development of society and consumer behavior require an updated approach to segment consumers based on their socio-economic status
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Needs Assessment Objectives • What we are aiming, to have relevant measure that well discriminate the Indonesian consumers in terms of social economic:
• Work well in differentiating consumer behavior: purchasing power, product and brand purchase/usage, lifestyle • Valid for urban (metro, rest of urban) and rural • Consistent implementation across Nielsen’s products (consumer, and Media) • Using existing robust establishment survey • Relevant across areas/widely available
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ConsideraBon: BPS data • To meet the above requirement of robust sample, we used BPS data i.e. SUSENAS. • SUSENAS also known as Economic Census was conducted by BPS once every two years. The study we used was the 2010 measurement
• Using BPS data will provide continuity when data need to be updated due to change in society condition • BPS data is also an official source adopted by many parties
• Latent Class Segmentation was applied to SUSENAS data in order to come up with respondents groups/ segments as well as to identify the relevant attributes forming the segments
Scoring System Development
• As opposed to use a single measurement, scoring system was adopted as better SES indicator
Validation
• Results is then validated through Nielsen Media Index and Nielsen Home Panel
• Order importance of each attribute (resulted from Latent Class Segmentation) was used to develop the scoring system
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Framework Attribute Selection
Scoring System Development
Validation
• Selected attributes resulted from Latent Class Segmentation • Source of drinking water • Electricity power • Type of fuel (for cooking) • Ownership of Refrigerator
• Ownership of LPG Tube 12 KG • Ownership of Personal Computer/ Laptop • Head of HH Education level • Monthly household expenditure (Total of Food and Non Food)
Segment size in total and at area level (urban – metro/ non metro vs rural)
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Profile of each segment (cross-tabbing)
• Final Scoring Criteria
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Framework Attribute Selection
Scoring System Development
Validation
• Validation was conducted through Nielsen Media Index and Nielsen Home Panel to assess how this new SES align with other measurement on consumer behavior such as product and brand usage • Nielsen Media Index is a large scale survey among n = 15,700 with respondents age 10+ years old
