Marketing potential of analytics based on socio-economic data

Indicus Analytics Pvt. Ltd. 1
Marketing potential of analytics based on socio-economic data
Indicus Analytics
11/24/2014
Authors:
Shrinath V, Product Consultant, Indicus Analytics Pvt. Ltd.
Dr. Laveesh Bhandari, Founder, Indicus Analytics Pvt. Ltd.

Table of Contents
EXECUTIVE SUMMARY
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3

BUSINESS APPLICATIONS OF SOCIO-ECONOMICS – AN INTRODUCTION
.....................
4

SOCIO-ECONOMICS & LOCATION BASED ANALYTICS
...........................................................
6

INTRODUCTION TO SPATIAL ANALYSIS
....................................................................................................................
6

LOCATION BASED ANALYTICS – A USE CASE OF SPATIAL ANALYTICS
..........................................................
7

BENEFITS OF SOCIO-ECONOMIC DATA TO LOCATION ANALYTICS
...................................................................
7

CHALLENGES FOR LOCATION-BASED ANALYSIS IN INDIA
.................................................................................
8

CELLGRID – A SPATIAL FRAMEWORK FOR SOCIO-ECONOMIC ANALYSIS
..............
10

INTRODUCTION TO THE CELLGRID FRAMEWORK
..............................................................................................
10

HOW CELLGRID CAN POWER MARKETING
..............................................................................
12

LOCATION-BASED ANALYSIS POSSIBLE USING CELLGRID
.............................................................................
12

ABOUT INDICUS ANALYTICS
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16

REFERENCES
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16

Executive summary
Retail analysis can be anchored to people or places.
By focusing on people, one could be to create a ‘person profile’ of the person using data on where they are
currently located, where they live, where they travel, how far do they travel on a regular day, what they buy,
how much they spend, and other factors. By understanding users better, brands could tailor their marketing
offers to specific user segments.
At the other hand, the focus could be to create a ‘place profile’ using both static & dynamic data. Static data
includes information like name, location, type of place, nearby places, road networks, locality/neighborhood
information, etc. while dynamic data includes heat maps of visitors, time of activity, profile of visitors,
transactions at places – ticket sizes and numbers, etc. By combining these two, brands can build powerful
mechanisms to study localities or places. This would help plan campaigns around locations based on where
people stay, where they work and where they hang out.
Granular socio-economic data can add insights to both kinds of analyses. Indicus has done early research on
how socio-economic data can be used for marketing to help brands build:
§ A probabilistic ‘person profile’ based on where people stay, work and travel, including elements
like income band, share-of-wallet spends, SEC profile, etc.
§ A richer ‘place profile’ based on location, profile of catchment areas surrounding the location,
likely income profile of visitors & an idea of their share-of-wallet spends, etc.
This data is complementary to other Big Data sources like transaction/POS analysis, social media analysis,
geographic analysis, etc. and can be blended to enrich understanding of both people & places.
In a world where analytics is becoming increasingly important for effective marketing, this socio-economic
data and models, blended with other data sources can have significant impacts on:
§ Retail catchment analysis
§ Improving CRM databases by adding income profile info to users
§ BTL campaign analysis
§ New product introduction, etc.
This paper introduces some of the early stage research on applications of socio-economic data to marketing
analytics around enriching understanding of people & places with multiple applications for marketers.

Business applications of socio-economics – an introduction
With India rapidly changing over the last couple of decades, many businesses struggle with questions like:
• How are urban and rural incomes and spending patterns changing?
• In which regions are consumers spending the most in my category of business?
• Which are potential growth areas for my business?
• Which markets should I consider beyond the top metros?
• How do I estimate demand for my product in a city or area within a city?
• and more.
Answers for many of these are currently based on past history, identification of markers (eg: Puma may
track cities where Honda opens showrooms to decide where to open franchise outlets), on-ground
knowledge collected from sales teams, consumption surveys, retail audits, secondary research, etc.
Socio-economics brings a more quantitative approach to the current methods.
Socio-economics is broadly defined as the use of economics in the study of society. Initial applications of
socio-economics were primarily in policy research & design. However, a deeper look at this data indicates
that there is a wealth of information that can aid business and marketing decisions.
Socio-economic analysis tracks various metrics of populations using surveys, sampling techniques, study of
public & syndicated research and modeling to cull data like:
• Income ranges of households
• Age distribution of population
• Share-of-wallet expenditure
• GDP of regions
• Asset ownership, etc.
By tracking metrics across regions, and across time, businesses can understand important indicators that
help them make better decisions.
Some examples of uses of socio-economic data include:
• Income distribution: This relates to information about household & population income levels
across different regions. Using this, one can build estimates on income ranges across regions, track
change in income levels across time, build affluence/poverty indices to classify households,
understand income inequality, etc.

By understanding income distribution across different regions, business can identify ‘zones’ that
have consumers similar to their target group.
By tracking this information over time, businesses can identify change in affluence patterns, and
which areas they could focus on.
• Age distribution: This includes age ranges for population, using which one can build estimates on
dependency ratio (non-earning members to earning members) for regions, ratio of different age
range spreads, etc.
Taken alone, this helps identify areas with a larger number of people in the target group.
Taken in conjunction with income distribution, this helps build a multi-dimensional framework
around income/age and SEC characteristics.
• Spending habits across various income groups: Socio-economic analysis can offer insights on
spending patterns across categories like clothing, food, FMCG, durables, etc.
Taken for an entire region, this helps build estimates of market size.
When split by income groups, this helps identify how different income groups allocate their money.
• Asset ownership: Track household ownership of assets like cars, two-wheelers, refrigerators, ACs,
etc.
For effective use, this data needs to be available in a form that can be compared across different regions,
across time, and that can be blended with other data sources (eg: history of sales data, competitive info, etc.)

Socio-economics & location based analytics
Introduction to spatial analysis
Geo-spatial analysis (or spatial analysis) relates to the science of analyzing locations, data sets and
relationships in a spatial context. This analysis is often represented visually using maps, charts or other
means.
Spatial analysis needs a referencing system that is comprehensive, stays the same across time and that is
easily comparable.
Geo-spatial data is arranged in a spatial context. This data may be referenced using a combination of
latitude & longitude, using boundaries made of combinations of latitudes & longitudes, or through
referencing systems commonly used in the real world (eg: country, state, district, etc.)
By organizing data in this format, spatial analysis helps understand relationships between data like:
• How far is a city from another city?
• How large is a particular state/district?
• Which is the nearest warehouse for a given location?
• How do states rank by population density?
• and much more.
Data in this format can also be visually represented on maps to let users understand and infer relationships.
Location-based analysis requires information to be arranged in a spatially addressable database. As
mentioned earlier, there are various means of reading this data:
• Lat/long: This is a standard format that relates to a point. By specifying a lat/long combination, the
spatial system could return data related to that point. Examples of this include name of a place,
whether it falls in a green area/water body, etc.
Lat/long combinations also allow comparisons of point data, and calculation of distances.
• Data within a boundary: In this case, the system aggregates values within a boundary. The
boundary is usually a set of lat/long coordinates joined together.
Boundaries are usually constructed around known entities like cities, states, etc.

Location based analytics – a use case of spatial analytics
Location based analytics involves understanding various parameters about locations, including, but not
limited to:
• Name(s)
• Connectivity by road networks
• Commercial establishments within a certain radius
• Residential areas within a certain radius
Location characteristics do not change frequently, and can serve as references for analysis of other
marketing data.
Traditionally, location based analytics are used for displaying characteristics on maps, GIS projects, etc.
Retail marketing often uses location analytics to determine catchment areas for stores. This may be done on
an ad-hoc basis or through calculations of estimates of time it takes to drive to a location, competition in the
area and trade potential.
Benefits of socio-economic data to location analytics
Socio-economic data adds a rich layer of additional information for analytics such as this. Using socio-
economic data, one can analyze not just location parameters, but also the kinds of people staying within the
catchment area, their income levels, age profile, etc.
The image below shows socio-economic data in use in the US.

Figure 1: Sample socio-economic data around a location point. Source: www.factual.com
Socio-economic data is organized around official boundaries for areas. For instance, socio-economic data
would be organized around a ward boundary, a city, a district, a state, etc. While this is fine for aggregate
calculations, it is often problematic for location-based analytics, especially in India.
Challenges for location-based analysis in India
However, analyses using boundaries for states, districts, towns, etc. present unique problems in India. For
instance:
• States/districts vary by orders of magnitude in size. This makes data inference by comparison
difficult
• District status is often not updated based on on-ground realities (eg: major parts of Gurgaon are
classified as rural, despite offering urban amenities)
• There have been new states/districts carved out of existing ones, which causes problems in times-
series analysis

• There is no consistent hierarchy between cities and districts (eg: Hyderabad is split across 3 districts
while Delhi comprises 9 districts. Many other cities are contained within a district or span two
districts)
• City boundaries keep changing as cities grow. Analysis of data over time becomes difficult
• There is no common definition of a ward or neighborhood across cities
• There are often multiple names/spellings for the same city/area that makes data collection and
comparison difficult
This brings many problems to location-based analytics. Take the case of catchment analysis mentioned
earlier.
A specific catchment area for a store may overlap multiple ward boundaries. These wards are often of
different sizes, and overlap multiple socio-economic zones.
For instance, the following image shows the wards in Mumbai city, shaded by average income levels
(darker indicates higher total income).
Figure 3: Ward boundaries of Mumbai. Source: Indicus Analytics
A store that falls in a smaller ward may have a catchment area that spills into part of the neighboring ward.
However, there is no means to calculate data for a part of the ward that relates to the catchment area.

CellGrid – a spatial framework for socio-economic analysis
Introduction to the CellGrid framework
To address the various problems with spatial analytics in India, Indicus has designed a geo-spatial
framework named CellGrid.
CellGrid is a grid structure that comprises 1 sq km regions across India. Each element is uniquely
addressable in the grid. There are a total of 4 million grid cells covering the expanse of India.
Using these as building blocks, other addressable elements can then be created. For example, data from
multiple cells can be aggregated to derive data for a city, district, state, etc.
The following image shows a representation of how the Cell-grid structure operates related to other
boundaries in use.
Figure 4: CellGrid relation to other boundaries. Source: Indicus Analytics

Using proprietary techniques, Indicus has refined socio-economic data collected at multiple geographic
levels to provide estimates at the CellGrid level. Some elements of the data in CellGrid include:
• Income distribution
• Age distribution
• Asset ownership
• Household expenditure and share-of-wallet
• SEC classification of households, and more
The data in the grid is structured such that it can be aggregated in any combination of cells. The following
example shows the use of CellGrid in building poverty maps for the state of Kerala, published recently in
the Mint.
Figure 5: Spatial analysis of poverty in Kerala. Source: www.livemint.com

How CellGrid can power marketing
Indicus is in the process of making available CellGrid data using APIs that can interface with other
analytics systems, as well as in a form usable in traditional Business Intelligence systems.
Based on multiple industry discussions and its own research, Indicus foresees various use cases for CellGrid
data.
Location-based analysis possible using CellGrid
Creating place profiles involve adding geo-enriched data to various places. Typically, these include data
like name, category of place, nearby places, road connectivity, etc. in a spatial format.
Cellgrid offers a standard, flexible and extensible means to serve as the location analytics platform.
Cellgrid uses its core database to create a socio-economic profile for every cell. This allows queries at a cell
or at an aggregate of cells (city, state, etc. or even user defined structures) like:
• Find one or more cells in a city with the highest median household income
• Find all cells with a median income greater than a particular level
• For a given cell, find all similar cells across the city, or across India
• Find highest density residential areas in a city
• Given a lat/long, find all other areas in India that show a similar gender ratio
• Get the total population of a city
• Get the number of senior citizens in a city
• Find areas in a city with the highest income growth in the last year
• Find number of SEC A households in a city
• Find top ten areas in urban towns in South India with the highest percentage of SEC A households
• Given a lat/long, find the probability of a person being in the age group 15-30
• Find areas where the share of expenditure on food is the highest
• and more
In addition to the data Indicus provides, other place related data can also be added to Cellgrid structure.
Cellgrid is designed to take in data that has:
- Lat/long
- Addresses (these can be geocoded and mapped to cellgrid)

- Cell-id information from cell towers (can be mapped to a geocode and read in cellgrid)
- Pin code information
Taken together, these allow for sophisticated analysis like:
• Customer segment analysis: See where customer segments (defined by age and/or SEC profile)
are concentrated based on your category.
For this, you could run a query against CellGrid asking for all regions that have a high proportion of
your specified age and SEC profile. CellGrid could return the lat/long combinations of these areas
or display these on a map view
• Location identification: Pick the best area for your store by studying income of residential areas
surrounding locations, drive time analysis, aggregate spends, property rates and more.
Figure 5: Sample data showing household spends across categories aggregated at ward level.
Source: Indicus Analytics
• Catchment analysis: Plot various stores on cellgrid and conduct catchment analysis
In the example quoted in the earlier section, a catchment analysis was difficult to perform due to
data being arranged around ward boundaries. With CellGrid, this is no longer a limitation, and data
can be analyzed at a 1 sq km nearest cell level.
• Demand analysis: Combine information from Cellgrid with your sales data and industry reports to
forecast demand. Cellgrid can give you share-of-wallet of expenditure, number of households
across income ranges, and ownership details for consumer durables. Since the addressing format is
standard and open, firms could analyse Point Of Sale data against Cellgrid to analyze store
performance.

• Store comparison: Plot different stores on CellGrid and study variances in sell-through and stocks
against parameters like income of catchment area, age profiles, etc.
Many apps today record a location footprint for different activities. This information is either anonymized
to observe aggregate patterns, or individual markers are stored to infer patterns.
For instance, tools like Google Now anticipate the user’s movement based on past history and context and
suggest news/places nearby/transit info/etc.
There are multiple applications of having location-based markers:
• Maps and traffic: Using locations of different users who use Google Maps and their movement
over time, Google aggregates info and infers traffic patterns that are depicted on maps
• Heat maps: There have been many attempts at using location footprints and time to construct heat
maps of activity – areas that are heavily visited at particular times of the day
• User connections: Many apps have tried connecting users to others nearby using proximity and
interest as vectors
• Check-ins and posts: These are the more ‘visible’ markers that users intentionally leave behind.
Facebook posts, tweets, Foursquare (now: Swarm), and others let people broadcast current location.
This wealth of identifiable and anonymized data can be mined to unearth patterns on people’s movements,
importance of locations and more.
In addition to these, there are other sources with details of users, or a unique identifier like:
• CRM: Many companies have address, age and other details for users
• Loyalty programs: These are either related to a store or brand, but often have details of address,
age and gender of participants
• Point of Sale: Newer stores are experimenting with adding details like phone numbers to uniquely
identify customers during billing
• Network operators: Operators use MSISDN/IMSI or other details to keep track of users and their
movement between cell towers
While a lot of the data mentioned above has privacy limitations on sharing details, firms can share parts of
the data that is not uniquely identifiable to enhance existing profiles.
Analysis on this data can be further enriched when combined with base data about locations and a spatial
platform for the analysis.

For instance, some of the use cases of this data against Cellgrid are:
• Enhancing CRM databases: By comparing residential address, age and gender against data in
Cellgrid, a reasonable economic ‘score’ can be attributed to profiles in a CRM
• Facebook targeting: Facebook already allows firms to upload email data to create ‘custom
audiences’ to show ads to. By adding an economic score to CRM data, firms can further filter their
email lists to create probability clusters around purchase intention, brand relevance, etc.
• Network based profiles: By analyzing where users spend their office hours and residential hours,
and tracing movements against Cellgrid, profiles can be attributed with an economic ‘score’ and
benchmarked into clusters.
• Business intelligence integration: Cellgrid data can be added to company BI solutions to help
study purchase history against factors like income score, age profile, share-of-wallet expenditure,
etc.
• Ad retargeting online: Online advertising has moved from basic banners to sophisticated
retargeting platforms that trace user history online to create clusters. These are then used to display
relevant ads when the user visits affiliate sites. Many of the websites also collect location/ISP data,
that can be used against Cellgrid data to give an income score that can enhance other data collected
online.
Note: Cellgrid is currently in prototype stage, and many of use cases are in various stages of
conception/testing.
The above mentioned use cases are plausible extensions to marketing analysis in the coming years. While
retail stores in developed markets are experimenting with various solutions around in-store analysis,
sentiment analysis, shelf analysis, etc. the market in India is still in early stages of using data driven analysis.
As retail competition intensifies, we foresee many of the larger chain stores move to more sophisticated
analysis before setting up stores, optimizing existing stores and in managing relationships with customers.
For many of these, CellGrid can provide a strong spatial analysis platform that can integrate with their
existing workflows, or serve standalone needs in the market.

About Indicus Analytics
Indicus Analytics is a premium economic research & data analytics company founded in 2000. Indicus
provides quantitative insights on Indian economy, consumers & markets.
Indicus offers client specific research & socio-economic analytical solutions. Over 300 firms, including
industry majors like McKinsey, BCG, HUL, ICICI Bank, Coca-Cola, The World Bank, Reserve Bank of
India, etc. are Indicus clients.
Indicus provides granular data on household income, expenditure, savings, household distribution by annual
income and SEC, etc. at multiple geographic levels – India stats, state level and district level stats, info on
towns and rural blocks, and at a neighborhood level for 52 leading towns in India.
More about Indicus Analytics at www.indicus.net
References
1. More on Factual: http://www.programmableweb.com/news/factual-expands-refines-and-rebrands-
api-geopulse-context/2013/04/04
2. Spatial poverty in Kerala, analysis by Indicus Analytics:
http://www.livemint.com/Politics/FJwyzCLIJU1DrOR00aFmDK/Spatial-poverty-in-kerala.html

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