Segmentation of Indian Households - Targeting the relevant consumer

Indicus Consumer Spectrum has been designed to help marketers understand the essence of 33 Consumer Segments, which together represent the entire urban spectrum of India. It highlights the key features which make each segment distinctive.
By: Amit Sinha
 
May 31, 2009 - PRLog -- New Delhi, India - The Indicus (http://indicus.net/Products/Home/Product%20Briefs/Urban%20Consumer%20Spectrum/) Urban Consumer Groups have been created using methods similar to methods used internationally, but India focused accounting for the differences both in terms of the characteristics of the Indian consumers and the availability of large scale household level raw data sets.

Indicus Consumer Spectrum has been designed to help marketers understand the essence of 33 Consumer Segments, which together represent the entire urban spectrum of India. It highlights the key features which make each segment distinctive. The purpose of this work is to help marketers devise advertising and communications strategies and fine tune their sales strategies, targeting and execution.

The segments have been derived based on two key dimensions - lifestyle stages of chief wage earners of households and their skill levels. The first defines their needs and the second defines their earning abilities.

The (http://indicus.net/Products/Home/Product%20Details/Urban%...) Grouping Mechanism reflects differences in Lifestyles, Preferences, Needs and Characteristics (Social, Household and Individual). For segmentation to be effective and manageable we needed to ensure that there were neither too many segments nor too few. Too many is unmanageable and two few is too broad for any strategic application. We settled for a systematic method, which was not merely conceptual and had strong empirical underpinnings. The groups derived reflect propensities to earn, spend and save depending on the skills and life stage.

A Two tier approach was followed - First tier: Conceptual, Second Tier: Empirical The objective was that the end result should be Simple to visualize and use and yet should account for consumer heterogeneity

Underlying Data – The Characteristics of raw data are such that they are widely accepted, Well sampled and use transparent methods. These data sets have been sourced from relevant data sets of National Sample Survey and Indicus Premium Databases. The depth of the raw data is 120,000 households, Sampled during and after mid 2000s. These households are representative of 70 million urban Indian households, and 350 million urban Indian citizens

First Tier Segmentation
Conceptual: Consumption pattern depends On Ability to Spend (Socio Economic Status) which is a function of Occupation, Education and Hierarchical position AND Requirements and Needs (Life Stage) which is a function of Marital Status, Family Type and Dependent’s Life Stage. Based on this framework About 100 consumer groups were derived.

Second Tier Segmentation
100 groups are too many to be of practical value. Many have similar incomes & propensities (Share spent on food, entertainment, education, medical, durable goods etc.). Using reduction methods, 33 segments derived from the first 100 based on Statistical analysis and Neural networks. These Derived Segments are the final consumer segments presented.

For each Segment, the following parameters have been presented:
•   Demographic (Total Population, Household population, Household Size, Age of all the individual, Gender Ratio)
•   Socio Economic (Education, Occupation, Industry)
•   Assets (House Ownership, Four Wheeler Ownership, Financial Assets, Household Assets)
•   Expenditure (Basic Food, High value food, Medical, Education, Durable goods, Clothing and Footwear)

Each segment is represented by an alpha numeric code – A1, A2, A3, A4, A5, B1, B2, etc.
A sample segment:

Segment F3
Profile – Married couples with young children living in a joint family. The majority of the Chief Wage Earners have school level education and are skilled workers. The median age of the Chief Wage Earners is 34 years, 98% of them are married, 45% have middle school level education, 28% of them are in wholesale and retail trade, and 46% are regular salaried employees. 83% of the spouses are home makers and 30% of them have middle school level education. On an average one adult member (apart from the chief wage earner) of the family is employed and 29% of the adult members have middle school level education. 48% of these households have more than 5 members, 68% have 1-2 minors, and 62% have no senior citizen members. The gender ratio in these households is 1062 females per 1000 males.

The median annual household income is Rs 213,000 and the median annual household expenditure is Rs 174,000. 60% of these households own a house, 1% own a four wheeler, 82% own a TV set and 67% reside in independent houses (as opposed to apartment blocks)

The above is a brief snapshot of a segment. The product provides further details on various aspects of the household income, expenditures, assets and savings, for each of the segments.  

About Indicus Analytics
Indicus Analytics is a premier (http://indicus.net/Index.aspx) economics research firm of India. We service data and policy analysis requirements of research oriented entities, which include the most respected academic institutions and international organizations, key policymakers, and top decision makers in industry. Constant interaction with internationally recognized experts and continuing non-funded research activities, have ensured national and international recognition for our research services and data products.

We help our customers cut through the maze that is India to unravel the various facets of the Indian consumers. Our products provide information about the economy and the consumers at extremely granular levels - at city, district levels, neighbourhood levels. Within the granular geographic levels, the information is further segmented into various income groups. The data is estimated using various techniques – large data sets are processed, proprietary surveys are done, Econometric and mathematical modelling is applied and finally the processed estimates are published.

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