Enable Better Location Decision Making Through Effective
use of Data
INSIGHTS|ANALYTICS|INNOVATIONS
Data Science & Big Data Practice
Location Analytics – Selecting the best location
The Internet of Things (IoT) and digital business will produce an unprecedented amount of location-referenced data, particularly as 25 billion devices become connected by 2020, according to Gartner estimates.
1. Organizations that implement geospatial and location intelligence (GLI) capabilities will benefit from opportunities to analyse the spatial dimension across their strategic, tactical and operational analytics.
2. The choice of a location should be directed by predetermined objectives. The objective of right location selection is to generate additional sales
and therefore profit
To respond to specific market or customer segment needs, and To neutralize competitors’ choice of location
Identify long-term business potential
4. It uses multiple public and private data sources to assimilate data on socio economic parameters about the population living in a geographic unit (City, Ward) and estimate economic activity in a location (city, ward, neighbourhood, etc.) using statistical and mathematical modelling.
Introduction:
1. Overall Business Potential Rank – 43 2. Business Potential Index – 32 3. Demographic Rank – 17 4. Economic Rank– 72 Highest Ranks Between 8-15 Ranks Between 16-30 Ranks Below 30 Ranks References
How you gain
The location analytics helps an organization
To compare multiple locations and rank them according to business potential
To identify next location for expansion
To rationalize existing branch/store network
DSI Methodology – Macro to Nano
DSI incorporates data science with Geo-spatial data:
1. To conduct the feasibility of the existing locations
2. To recognize your next location
3. Forecast business potential
Macro/Micro Analysis
1. Rank the sites based on the Business Potential index using DSI Location framework & technology
Macro Analysis
Micro Analysis
Grid Analysis & Location Identity
1. Rank individual spot for a particular location via the DSI Grid Analysis technology
2. Prioritize your goals accordingly
DSI Business Potential Index (DBPI)
The DBPI architecture is built upon its intuitive algorithms that utilizes multivariate statistical classification technique using
geospatial and demographic prognosticators. The algorithms are created based upon the composite scores of multiple indictors
Indicators
Brief
Infrastructure
Gauge readiness of infrastructure like realty space, hospitals, population, sanitation,
parking etc.
Competition
Identify the strength and intensity of the competition
Cost Feasibility
Identify score for associated infrastructure like realty cost, human resource cost,
distribution cost, utility cost etc.
Ingestion
Identify score in terms of the composite consumption of diverse commodity categories
Socio-economic
Identify business potential based on economic and demographic variables
DSI Location Selection Framework (DLSF)
DSI location selection framework investigates numerous factors at the ground level to project the feasibility for a
precise location and benchmarking it against the closest comparable.
Indicators
Brief
Approachability & Traffic flow
Accessibility factor of the location & Population flow (static + moving) in the concern
location
Exposure
Visual reach of the site/location
Cost
Approximate cost (infrastructure, manpower, realty etc.) and margin
Catchment Area
Socio-economic sketch of the location
Sales Triggers
How and Where sales will be generated in the site
Competition
Based on availability of competitors and their strong holds
Catchment Area Analysis
What you learn?
1. Business Potential for certain store
category
2. Terrestrial impression of a store
3. Socio-economic
and
demographic
weightage
4. Points of Interests
5. Next Store potential?
2-0 KM
4-2 KM 5-4 KM
Store Network Optimization
Top Concerns
DSI Approach
Result
Conduct feasibility of
existing store and identify
optimal business per store
How many more
stores required
Identifying the Location
Business Potential through
multiple attractiveness
analysis
Precise location of
augment/add
Performance evaluation
followed by justification
towards cannibalisation
Which store to close
How to optimise the store
count in a current location?
Which spot offers the richest
dividend among the current
selection?
Which stores should
be shut?
Summary
Determine attractiveness
indexfor existing locations and for potential growth areas
Minimize cannibalization impact of new stores on existing stores
Forecast business potential for existing and new stores
Rationalize existing store
Compare catchment
areas for existing and
new stores
STEP 1
STEP 2
STEP 3
STEP 4
STEP 5
Data Sources
Digital Maps (ward, city, district etc.)
Satellite Images
Socio-economic data
Misc. data (crime, weather etc.)
POI data
Enterprise data
Shadow analysis
Day time images
Night light images
Govt. sources
DSI Case Studies on Location
Analytics
Transforming your Data Chromosome
0 1
0 1
1 1
Analytics detects epidemic indicators
Location Analytics to select store location
Business Questions
A leading retailer in APAC region was troubled choosing the right store location. They also wanted to understand the potential business attractiveness 3 years down the line. Besides, the client wanted to rationalize its stores to improve sales from existing stores. They decided to implement location analytics.
• Connection of Geo-spatial context with business rich data to deliver enhanced data visualization and business insight and improved decision making capabilities and predictive analytics
• Identified new locations for expansions
• Segmented stores based on performance in relation to market potential (Two stores having similar revenue are segmented separately based on market potential of the area)
• Created a watch list for underperforming stores
• Enabled a refined and deeper understanding of how to improve marketing and other store-level operations
• We created a composite attractive Index model to determine business potential at a macro and micro level locations. The index was converted into market potential using step down approach
• Visualized the attractiveness index at district and at ward level on a map for better comprehension.
• Plotted existing stores of the client and those of the competitors on the map
• Determined catchment area for major stores of the client based on revenue contribution
• Segmented stores under different categories based on current revenue and market potential
• Identified locations for setting up new store
• How to rationalize stores by segmenting best performers, worst performer, and under performers?
• What will the ideal location for next stores?
• What are the trends, relationships, and behaviour of customers located in the area?
Catchment Area Analysis at Store Level
Area in focus
Solutions
Analytics detects epidemic indicators
Location Analytics to select potential vendors
Solutions
Impact
Business Questions
A leading global steel manufacturer was planning to expand their dealer network but was unable to make a logical decision. They were dependent on their in-house team to identify locations with low presence of vendors and with the support of the nearest branch took the final call. They did not see a strong correlation in their action against business profits, on the contrary had increased their liability with additional nodes. They had approached us and decided to implement Location Analytics to solve their business challenge.
• Identified new locations for expansions of dealers
• Identified if new dealers are required or existing dealers can expand
• Created a watch list for underperforming dealers
• We created a composite Business Potential Index model to determine the need of dealers at various locations
• Visualized the attractiveness index by creating heat map by state/by district/by city/by site to identify market share and the conduct Gap Analysis
• Identified the distance of the spot from the plant location
• Identify the retail price in the area – Profit wise heat map
• Plotted existing of the potential spots and those of the competitors on the map
• Created a tool that can real-time identify the most potential spots to locate future dealers that will help in increasing reach and sustainable profits for the steel manufacturer
• How to identify the dealer gap in certain location?
• In which location do the company need the next vendor and with what value proposition?
Catchment Area Analysis for Potential Vendor Network
Low High
1.Overall Ranking – 7
2.Business Potential Index – 12 3.Demographic Rank – 15 4.Economic Rank - 28
