New Approach for Indonesia Socio Economic Status

New Approach for Indonesia Socio Economic Status

Needs Assessment Background 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 2

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 3

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 4

Framework Attribute Selection Scoring System Development Validation 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 As opposed to use a single measurement, scoring system was adopted as better SES indicator Order importance of each attribute (resulted from Latent Class Segmentation) was used to develop the scoring system Results is then validated through Nielsen Media Index and Nielsen Home Panel 5

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) 6

Framework Attribute Selection Scoring System Development Validation Determining the segment considering: Number of segments Segment size in total and at area level (urban metro/ non metro vs rural) Profile of each segment (cross-tabbing) 7

Framework Attribute Selection Scoring System Development Validation Attribute Scoring Calculate Total Scoring from 8 attribute selected and come up with the range of score from 1 to 32 Determining the segment considering: Number of segments Segment size in total and at area level (urban metro/ non metro vs rural) Profile of each segment (cross-tabbing) Final Scoring Criteria 8

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 Nielsen Home Panel 9

Framework Attribute Selection Scoring System Development Validation Profile of each segment (cross-tabbing) Based on Media Index Data August 2011 Based on SUSENAS DATA in ALL AREAS (INDONESIA) Based on SUSENAS DATA in METRO AREAS 10

Why is it beqer? Beyond Consumer Spending Taken into account other attributes beyond consumer spending: More accurate attributes Reflecting lifestyle Wide Coverage Survey covered national Attributes are relevant to all areas/work for varied areas Better Differentiating This new approach is able to differentiate not only mass consumer but also the upper class segment 11

New Consumer SegmentaBon 12