Data Science & Big Data Practice. Location Analytics. Enable Better Location Decision Making Through Effective use of Data

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1 Data Science & Big Data Practice INSIGHTS ANALYTICS INNOVATIONS Location Analytics Enable Better Location Decision Making Through Effective use of Data

2 Location Analytics Selecting the best location Introduction: 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. Overall Business Potential Rank Business Potential Index Demographic Rank Economic Rank 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. *Source: Gartner References Highest Ranks Between 8-15 Ranks Between Ranks Below 30 Ranks Most Probable Spots

3 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 *As per ESRI research

4 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 Analysis Micro Analysis Grid Analysis Macro/Micro Analysis 1. Rank the sites based on the Business Potential index using DSI Location framework & technology Grid Analysis & Location Identity 1. Rank individual spot for a particular location via the DSI Grid Analysis technology 2. Prioritize your goals accordingly

5 DSI Business Potential Index (DBPI) Which are my most potential locations? 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 Infrastructure Competition Brief Gauge readiness of infrastructure like realty space, hospitals, population, sanitation, parking etc. Identify the strength and intensity of the competition Cost Feasibility Ingestion Identify score for associated infrastructure like realty cost, human resource cost, distribution cost, utility cost etc. Identify score in terms of the composite consumption of diverse commodity categories Socio-economic Identify business potential based on economic and demographic variables

6 DSI Location Selection Framework (DLSF) Why is Site A better then Site B? 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 Approachability & Traffic flow Exposure Cost Catchment Area Sales Triggers Competition Brief Accessibility factor of the location & Population flow (static + moving) in the concern location Visual reach of the site/location Approximate cost (infrastructure, manpower, realty etc.) and margin Socio-economic sketch of the location How and Where sales will be generated in the site Based on availability of competitors and their strong holds

7 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? 5-4 KM 4-2 KM 2-0 KM

8 Store Network Optimization Top Concerns DSI Approach Result How to optimise the store count in a current location? Conduct feasibility of existing store and identify optimal business per store How many more stores required Which spot offers the richest dividend among the current selection? Identifying the Location Business Potential through multiple attractiveness analysis Precise location of augment/add Which stores should be shut? Performance evaluation followed by justification towards cannibalisation Which store to close

9 Summary Forecast business potential for existing and new stores Determine attractiveness index for existing locations and for potential growth areas Compare catchment areas for existing and new stores STEP 2 Minimize cannibalization impact of new stores on existing stores STEP 3 Rationalize existing store STEP 4 STEP 5 STEP 1

10 Data Sources Digital Maps (ward, city, district etc.) Satellite Images Socio-economic data Shadow analysis Day time images Night light images Govt. sources Other private sources Misc. data (crime, weather etc.) POI data Enterprise data

11 Transforming your Data Chromosome DSI Case Studies on Location Analytics

12 Analytics ocation detects Analytics epidemic to select indicators store location 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. Business Questions 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? Area in focus Catchment Area Analysis at Store Level Solutions 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 Impact 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

13 Analytics ocation detects Analytics epidemic to select indicators potential vendors 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. Business Questions 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 Solutions 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 Impact 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 1.Overall Ranking 7 2.Business Potential Index 12 3.Demographic Rank 15 4.Economic Rank Proximity to Factory 100 km

14 INSIGHTS ANALYTICS INNOVATIONS