Fruit Juice Retail Mapping – In-Store Product Availability, Pricing, and Shelf Visibility

This dataset offers granular, on-the-ground intelligence on the presence, pricing, shelf positioning, and availability of packaged fruit juice brands across various retail outlets. Captured by field agents directly from stores, the data includes structured inputs such as outlet attributes, product barcodes, pricing, shelf photos, and product availability checks. It is designed to help FMCG teams track in-store performance, benchmark competitors, and optimize retail execution strategies in real time.

Core Value Proposition Retail environments are dynamic, and winning at the shelf requires timely, accurate data on how products are being positioned and priced across thousands of locations. This dataset bridges that gap by providing a real-world, store-level view into the execution of fruit juice products—across both modern and traditional retail formats.

It enables stakeholders to move beyond assumptions and market averages, offering visibility into specific brands, SKUs, and store types. Teams can assess the effectiveness of distribution strategies, monitor compliance with planograms or promotional campaigns, and uncover competitive gaps across different regions.

Use Cases by Role Trade Marketing Teams

Verify on-shelf product presence and identify visibility gaps across retail partners

Monitor planogram compliance with real photo documentation

Compare pricing vs. competitors in-store to ensure promotional pricing is effective

Track availability of new SKUs or promotional bundles

Sales & Field Operations

Prioritize store visits based on stockout frequency or missing SKUs

Identify retailers not carrying key products or brands and target them for onboarding

Validate retail execution of in-market activations or price drops

Map payment method availability for potential POS integrations or retail enablement

Brand & Category Managers

Measure retail footprint and market penetration at the brand level

Benchmark share of shelf and price positioning versus competitors across retail types

Identify regional pricing inconsistencies or availability issues

Understand consumer-facing shelf experience using storefront and shelf photos

Insights & Strategy Teams

Segment retail environments by outlet type, city, or region for performance benchmarking

Identify trends in availability, pricing, and product assortment

Support business cases for expanding into underserved channels or cities

Feed data into forecasting or market sizing models using actual in-store coverage

Revenue Growth & Pricing Teams

Monitor how price strategies are being executed in the field

Identify unauthorized discounting or pricing inconsistencies by outlet

Evaluate price sensitivity by comparing prices across similar store types

Use competitor pricing benchmarks to refine promotional calendars

Key Data Components Outlet Details

Outlet Name, Type, Address, City, Country, Latitude, Longitude These fields provide context around where the product data was captured, supporting regional and channel segmentation.

Storefront & Section Photos

Storefront Photo, Juice Section Photo Visual confirmation of retail execution and visibility, allowing internal teams to audit displays and merchandising quality.

Product Availability & Pricing

Is [Brand] Available? fields for each juice brand (e.g., Chivita, Capri-sun, Ribena, etc.)

Price, Barcode, and Shelf Photo for each product These fields allow for detailed, SKU-level tracking of which products are available, at what price, and how they appear on the shelf.

Additional Retail Attributes

Payment Methods, Products Offered, Additional Attributes These help teams understand store-level characteristics that may influence sales strategy, such as whether the outlet supports mobile payments or carries complementary categories.

Competitive Tracking Brands included in the dataset (e.g., Chivita Orange, Happy Hour, Active, Capri-sun, Ribena, 5Alive, Frudi, LaCasera, Sosa, Wilson’s Lemonade, etc.) are all tracked for:

On-shelf presence (yes/no)

Price

Barcode

Shelf-level photo capture

This makes the dataset a strong foundation for competitive audits, pricing analysis, and retail presence benchmarking across brands and territories.

Summary The Fruit Juice Retail Mapping dataset provides the ground truth for how fruit juice products are presented, priced, and positioned at the point of sale. It’s built to enable smarter decision-making across marketing, sales, trade, and insights functions—helping teams move faster, identify gaps, and act on opportunities with precision. Whether the goal is to improve coverage, enforce pricing policy, design promotions, or win more shelf space, this data offers the visibility needed to execute with confidence.

To create this layer, OCTO staff used ABCA's definition of “Full-Service Grocery Stores” (https://abca.dc.gov/page/full-service-grocery-store#gsc.tab=0)– pulled from the Food System Assessment below), and using those criteria, determined locations that fulfilled the categories in section 1 of the definition.Then, staff reviewed the Office of Planning’s Food System Assessment (https://dcfoodpolicycouncilorg.files.wordpress.com/2019/06/2018-food-system-assessment-final-6.13.pdf) list in Appendix D, comparing that to the created from the ABCA definition, which led to the addition of a additional examples that meet, or come very close to, the full-service grocery store criteria. The explanation from Office of Planning regarding how the agency created their list:“To determine the number of grocery stores in the District, we analyzed existing business licenses in the Department of Consumer and Regulatory Affairs (2018) Business License Verification system (located at https://eservices.dcra.dc.gov/BBLV/Default.aspx). To distinguish grocery stores from convenience stores, we applied the Alcohol Beverage and Cannabis Administration’s (ABCA) definition of a full-service grocery store. This definition requires a store to be licensed as a grocery store, sell at least six different food categories, dedicate either 50% of the store’s total square feet or 6,000 square feet to selling food, and dedicate at least 5% of the selling area to each food category. This definition can be found at https://abca.dc.gov/page/full-service-grocery-store#gsc.tab=0. To distinguish small grocery stores from large grocery stores, we categorized large grocery stores as those 10,000 square feet or more. This analysis was conducted using data from the WDCEP’s Retail and Restaurants webpage (located at https://wdcep.com/dc-industries/retail/) and using ARCGIS Spatial Analysis tools when existing data was not available. Our final numbers differ slightly from existing reports like the DC Hunger Solutions’ Closing the Grocery Store Gap and WDCEP’s Grocery Store Opportunities Map; this difference likely comes from differences in our methodology and our exclusion of stores that have closed.”Staff also conducted a visual analysis of locations and relied on personal experience of visits to locations to determine whether they should be included in the list.

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This is a collection of maps, layers, apps and dashboards that show population access to essential retail locations, such as grocery stores. Data sourcesPopulation data is from the 2010 U.S. Census blocks. Each census block has a count of stores within a 10 minute walk, and a count of stores within a ten minute drive. Census blocks known to be unpopulated are given a score of 0. The layer is available as a hosted feature layer.Grocery store locations are from SafeGraph, reflecting what was in the data as of October 2020. Access to the layer was obtained from the SafeGraph offering in ArcGIS Marketplace. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it already has the necessary attributes on each street segment to identify which streets are considered walkable, and supports a wide variety of driving parameters.The walkable access layer and drivable access layers are rasters, whose colors were chosen to allow the drivable access layer to serve as backdrop to the walkable access layer. Alternative versions of these layers are available. These pairs use different colors but are otherwise identical in content.Data PreparationArcGIS Network Analyst was used to set up a network street layer for analysis. ArcGIS StreetMap Premium was installed to a local hard drive and selected in the Origin-Destination workflow as the network data source. This allows the origins (Census block centroids) and destinations (SafeGraph grocery stores) to be connected to that network, to allow origin-destination analysis.The Census blocks layer contains the centroid of each Census block. The data allows a simple popup to be created. This layer's block figures can be summarized further, to tract, county and state levels.The SafeGraph grocery store locations were created by querying the SafeGraph source layer based on primary NAICS code. After connecting to the layer in ArcGIS Pro, a definition query was set to only show records with NAICS code 445110 as an initial screening. The layer was exported to a local disk drive for further definition query refinement, to eliminate any records that were obviously not grocery stores. The final layer used in the analysis had approximately 53,600 records. In this map, this layer is included as a vector tile layer.MethodologyEvery census block in the U.S. was assigned two access scores, whose numbers are simply how many grocery stores are within a 10 minute walk and a 10 minute drive of that census block. Every census block has a score of 0 (no stores), 1, 2 or more stores. The count of accessible stores was determined using Origin-Destination Analysis in ArcGIS Network Analyst, in ArcGIS Pro. A set of Tools in this ArcGIS Pro package allow a similar analysis to be conducted for any city or other area. The Tools step through the data prep and analysis steps. Download the Pro package, open it and substitute your own layers for Origins and Destinations. Parcel centroids are a suggested option for Origins, for example. Origin-Destination analysis was configured, using ArcGIS StreetMap Premium as the network data source. Census block centroids with population greater than zero were used as the Origins, and grocery store locations were used as the Destinations. A cutoff of 10 minutes was used with the Walk Time option. Only one restriction was applied to the street network: Walkable, which means Interstates and other non-walkable street segments were treated appropriately. You see the results in the map: wherever freeway overpasses and underpasses are present near a grocery store, the walkable area extends across/through that pass, but not along the freeway.A cutoff of 10 minutes was used with the Drive Time option. The default restrictions were applied to the street network, which means a typical vehicle's access to all types of roads was factored in.The results for each analysis were captured in the Lines layer, which shows which origins are within the cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle).The Lines layer was then summarized by census block ID to capture the Maximum value of the Destination_Rank field. A census block within 10 minutes of 3 stores would have 3 records in the Lines layer, but only one value in the summarized table, with a MAX_Destination_Rank field value of 3. This is the number of stores accessible to that census block in the 10 minutes measured, for walking and driving. These data were joined to the block centroids layer and given unique names. At this point, all blocks with zero population or null values in the MAX_Destination_Rank fields were given a store count of 0, to help the next step.Walkable and Drivable areas are calculated into a raster layer, using Nearest Neighbor geoprocessing tool on the count of stores within a 10 minute walk, and a count of stores within a ten minute drive, respectively. This tool uses a 200 meter grid and interpolates the values between each census block. A census tracts layer containing all water polygons "erased" from the census tract boundaries was used as an environment setting, to help constrain interpolation into/across bodies of water. The same layer use used to "shoreline" the Nearest Neighbor results, to eliminate any interpolation into the ocean or Great Lakes. This helped but was not perfect.Notes and LimitationsThe map provides a baseline for discussing access to grocery stores in a city. It does not presume local population has the desire or means to walk or drive to obtain groceries. It does not take elevation gain or loss into account. It does not factor time of day nor weather, seasons, or other variables that affect a person's commute choices. Walking and driving are just two ways people get to a grocery store. Some people ride a bike, others take public transit, have groceries delivered, or rely on a friend with a vehicle. Thank you to Melinda Morang on the Network Analyst team for guidance and suggestions at key moments along the way; to Emily Meriam for reviewing the previous version of this map and creating new color palettes and marker symbols specific to this project. Additional ReadingThe methods by which access to food is measured and reported have improved in the past decade or so, as has the uses of such measurements. Some relevant papers and articles are provided below as a starting point.Measuring Food Insecurity Using the Food Abundance Index: Implications for Economic, Health and Social Well-BeingHow to Identify Food Deserts: Measuring Physical and Economic Access to Supermarkets in King County, WashingtonAccess to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their ConsequencesDifferent Measures of Food Access Inform Different SolutionsThe time cost of access to food – Distance to the grocery store as measured in minutes

With the retail market getting more and more competitive by the day, there has never been anything more important than the ability for optimizing service business processes when trying to satisfy the expectations of customers. Channelizing and managing data with the aim of working in favor of the customer as well as generating profits is very significant for survival. Ideally, a retailer’s customer data reflects the company’s success in reaching and nurturing its customers. Retailers built reports summarizing customer behavior using metrics such as conversion rate, average order value, recency of purchase and total amount spent in recent transactions. These measurements provided general insight into the behavioral tendencies of customers. Customer intelligence is the practice of determining and delivering data-driven insights into past and predicted future customer behavior.To be effective, customer intelligence must combine raw transactional and behavioral data to generate derived measures. In a nutshell, for big retail players all over the world, data analytics is applied more these days at all stages of the retail process – taking track of popular products that are emerging, doing forecasts of sales and future demand via predictive simulation, optimizing placements of products and offers through heat-mapping of customers and many others.

DATA AVAILABILITY:  Retail Data.xlsx o This book has three sheets (Customer, Transaction, Product Heirarchy) o Customer: Customers information including demographics o Transaction: Transactions of customers o Product Heirarchy: Product information (cateogry, sub category etc...) BUSINESS PROBLEM: A Retail store is required to analyze the day-to-day transactions and keep a track of its customers spread across various locations along with their purchases/returns across various categories. Create a report and display the various calculated metrics, reports and inferences.

Field Data Interactive Geostatistical Dashboard.

Business location data for Maptitude mapping software are from Caliper Corporation and contain point locations for businesses.

This map is an internal planning resource and should only be used by MTC and its partner agenciesThis map shows sales at Clipper retail locations by month from January 2020 through April 2022. It also shows the location of MTC's Equity Priority Communities, for context.Clipper Retail Locations are marked by a dot indicating how many sales they had in a specific month. There are three layers that show different types of sales: Card Sales, Product Sales (i.e. passes), and Cash-Value Sales. Card Sales is enabled by default. To look at another type of sales, hide the Card Sales layer and unhide the layer you want to look at. To see change in sales over time, use the time slider at the bottom. You can push play to advance automatically or drag the slider to look at a particular month. If you click on a location, you will get a pop-up with a detailed description of the sales at that location during that month. Please reach out to Sarah Doggett (sdoggett@bayareametro.gov) if you have any questions!

Map outlines representing retail maps available for the North Country Trail.

Sourcing accurate and up-to-date demographics GIS data across Asia and MENA has historically been difficult for retail brands looking to expand their store networks in these regions. Either the data does not exist or it isn't readily accessible or updated regularly.

GapMaps uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent geodemographic datasets across Asia and MENA at 150m x 150m grid levels in major cities and 1km grids outside of major cities.

With this information, brands can get a detailed understanding of who lives in a catchment, where they work and their spending potential which allows you to:

• Better understand your customers
• Identify optimal locations to expand your retail footprint
• Define sales territories for franchisees
• Run targeted marketing campaigns.

Premium demographics GIS data for Asia and MENA includes the latest estimates (updated annually) on:

1. Population (how many people live in your local catchment)
2. Demographics (who lives within your local catchment)
3. Worker population (how many people work within your local catchment)
4. Consuming Class and Premium Consuming Class (who can can afford to buy goods & services beyond their basic needs and /or shop at premium retailers)
5. Retail Spending (Food & Beverage, Grocery, Apparel, Other). How much are consumers spending on retail goods and services by category.

Primary Use Cases for GapMaps Demographics GIS Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular 150m x 150m grid levels using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate GapMaps demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

8. Tenant Recruitment

9. Target Marketing

10. Market Potential / Gap Analysis

11. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

12. Customer Profiling

13. Target Marketing

14. Market Share Analysis

Dades d'indicadors comercials de les principals ciutats europees. Per tal d'elaborar els indicadors a escala de ciutat, han estat visitats a peu de carrer, geolocalitzats i classificats prop de 250mil locals comercials a les ciutats analitzades. Les ciutats incloses en el mapa són: Barcelona, Lisboa, Londres, Madrid, Milà, Munic, i París.

This map is a listing of active retail tobacco vendors.This data includes the name, subcategory, and location of active retail tobacco vendors operating in New York State. Active retail tobacco vendors include only vendors that were operating during some or all of the program year or measurement period selected. Subcategory includes the type of retail tobacco vendor, such as a convenience store or a grocery supermarket. Address includes the street address, city, state, zip code, municipality, and county where the vendor is located. For more information, check out https://www.health.ny.gov/prevention/tobacco_control/program_components.htm, or click on the "About" tab.

This dataset provides information on 11 in Massachusetts, United States as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

This map layer is a subset of the Columbus Points of Interest layer and shows retail facilities in the City of Columbus. Retail facilities include department stores, gas stations, grocery stores, hotels, restaurants, service facilities, and others. This layer is maintained through a cooperative effort by multiple departments of the City of Columbus using first-hand knowledge of the area as well as a variety of authoritative data sources. While significant effort is made to ensure the data is as accurate and comprehensive as possible, some points of interest may be excluded and included points may not be immediately updated as change occurs.

A listing of all retail food stores which are licensed by the Department of Agriculture and Markets.

This dataset provides information on 9 in Michigan, United States as of May, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

Dades de 2022 d'establiments i de locals comercials tancats o buits de les ciutats catalanes de més de 20mil habitants i de les capitals de comarca.

This map shows how commercial activity is distributed within urban areas and the impact of commercial services on the urban landscape, by mapping what proportion of stores (hence jobs) in an urban area that are found in shopping centres. Shopping centres are designed, built and managed as a single unit, primarily for retail purposes; they are therefore easy to identify. As a relatively modern innovation, introduced to most cities in the 1960s or later, they are usually located at the edge of the city closer to the suburbs. Most cities have about 10% of their stores in shopping centres; this value is slightly higher in larger cities and in cities with a high growth rate.

This dataset provides information on 2 in Iowa, United States as of June, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.