Online Supermarket Market Outlook

The global online supermarket market size reached approximately $250 billion in 2023 and is projected to reach around $700 billion by 2032, with a compound annual growth rate (CAGR) of 12%. This impressive growth can be attributed to the increasing consumer preference for the convenience and efficiency of online shopping, coupled with technological advancements in e-commerce platforms.

One of the primary growth factors for the online supermarket market is the rising penetration of smartphones and high-speed internet. With more people gaining access to the internet and smartphones becoming more affordable, consumers can easily browse, compare, and purchase groceries and other supermarket items online. This convenience is a significant driver for the market, as it saves time and effort compared to traditional shopping methods. Furthermore, the COVID-19 pandemic has further accelerated the adoption of online grocery shopping, as consumers seek to minimize their exposure to crowded physical stores.

Another crucial growth factor is the increasing urbanization and changing lifestyle patterns. As more people move to urban areas and lead busier lives, the demand for convenient shopping solutions has surged. Online supermarkets provide an effective solution to this demand by offering a wide variety of products that can be delivered directly to consumers' doorsteps. This trend is particularly noticeable among younger generations who are more inclined towards digital solutions and value their time highly. Additionally, the availability of a broader range of products, including international brands and specialty items, on online platforms is attracting more consumers.

Technological advancements in e-commerce platforms have also played a significant role in the growth of the online supermarket market. Innovations such as artificial intelligence (AI), machine learning, and big data analytics have enhanced the shopping experience by providing personalized recommendations, efficient inventory management, and superior customer service. These technologies help online supermarkets to better understand consumer preferences and optimize their operations, thereby driving sales and customer satisfaction. Moreover, the integration of secure payment gateways and various payment options has increased consumer trust in online transactions, further boosting market growth.

Regionally, the online supermarket market is experiencing significant growth across various regions, with Asia Pacific expected to dominate the market during the forecast period. The region's large population, rapid urbanization, and increasing disposable incomes are key factors driving this growth. Moreover, the presence of major e-commerce players and their continuous efforts to expand their market reach through strategic partnerships and investments are contributing to the market's expansion in this region. North America and Europe are also witnessing substantial growth, driven by high internet penetration rates, advanced technological infrastructure, and a strong preference for online shopping among consumers.

The concept of e-grocery has revolutionized the way consumers approach their shopping habits. As part of the broader online supermarket market, e-grocery specifically refers to the purchase of grocery items through digital platforms, offering unparalleled convenience and variety. This segment has gained significant traction due to the busy lifestyles of modern consumers who seek efficient solutions for their daily needs. E-grocery platforms often provide features such as personalized shopping lists, subscription services, and same-day delivery, enhancing the overall consumer experience. The rise of e-grocery is also supported by the increasing trust in online transactions and the availability of secure payment options, making it a preferred choice for many households.

Product Type Analysis

The online supermarket market can be segmented into various product types, including groceries, personal care products, household items, fresh produce, beverages, and others. Groceries constitute a significant portion of the market, driven by the essential nature of these items and the frequent need for replenishment. Consumers appreciate the convenience of purchasing groceries online, which often come with subscription services and same-day delivery options, making it easier to manage household supplies efficiently.

Personal

The average for 2021 based on 165 countries was 105.854 index points. The highest value was in South Korea: 208.84 index points and the lowest value was in India: 58.17 index points. The indicator is available from 2017 to 2021. Below is a chart for all countries where data are available.

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

Online Food and Grocery Retail Market Outlook

The global online food and grocery retail market size is expected to witness substantial growth, from approximately USD 250 billion in 2023 to an estimated USD 650 billion by 2032, achieving a CAGR of 11.5%. This remarkable growth is driven by factors such as increasing internet penetration, rising consumer preference for online shopping, and advancements in digital payment methods.

A key growth factor for the online food and grocery retail market is the convenience it offers to consumers. The ability to purchase groceries from the comfort of one's home, without the need to visit physical stores, appeals to a large demographic. This convenience is particularly relevant in urban areas where busy lifestyles and long working hours make frequent trips to the supermarket challenging. Additionally, the COVID-19 pandemic has accelerated the shift towards online grocery shopping as consumers sought to minimize their exposure to crowded places.

Another significant growth factor is the increasing use of smartphones and mobile applications. With the proliferation of affordable smartphones and improved mobile internet connectivity, consumers are increasingly using mobile apps for grocery shopping. These apps often provide user-friendly interfaces, personalized recommendations, and exclusive discounts, further incentivizing online purchases. Moreover, the integration of advanced technologies such as AI and machine learning in these platforms enhances the shopping experience by predicting consumer preferences and offering customized deals.

The role of digital payment methods also cannot be underestimated. The availability of secure and efficient digital payment options such as credit/debit cards, digital wallets, and UPI has facilitated the growth of online grocery shopping. These payment methods not only provide convenience but also enhance the security of transactions, thereby building consumer trust. The rise of digital wallets and contactless payment options has been particularly influential, as they offer a seamless checkout experience.

The rise of Fresh Food E-commerce is a notable trend within the online food and grocery retail market. This segment focuses on delivering fresh produce, meats, dairy, and other perishable items directly to consumers' doorsteps. The demand for fresh food online has surged as consumers prioritize convenience and quality. Retailers are leveraging advanced logistics and cold chain technologies to ensure that fresh products maintain their quality during transit. This emphasis on freshness is also driving innovation in packaging and delivery methods, allowing retailers to cater to health-conscious consumers who seek farm-to-table experiences without leaving their homes. As a result, Fresh Food E-commerce is becoming an integral part of the broader online grocery landscape, offering consumers a diverse range of fresh options.

From a regional perspective, Asia Pacific is expected to be the fastest-growing market for online food and grocery retail. The rapid urbanization, rising disposable incomes, and a large base of tech-savvy consumers in countries like China and India are driving the growth in this region. Furthermore, the presence of major e-commerce players and the increasing adoption of digital payment methods contribute to the market's expansion. North America and Europe also represent significant market shares due to high internet penetration and well-established e-commerce infrastructures.

Product Type Analysis

The online food and grocery retail market can be segmented by product type into fresh produce, packaged food, beverages, household products, and others. Fresh produce, encompassing fruits and vegetables, meat, and dairy products, constitutes a vital segment. Consumers' increasing demand for fresh and organic produce delivered to their doorsteps has fueled the growth of this segment. Online retailers are investing in robust cold chain logistics to ensure the freshness and quality of these perishable items.

Packaged food represents another significant segment in the online grocery market. This includes ready-to-eat meals, snacks, canned goods, and other packaged consumables. The busy lifestyles of modern consumers have led to a preference for convenient meal solutions, driving the demand for packaged foods. Moreover, the availability of a wide range of international and specialty foods online has expand

This layer shows which parts of the United States and Puerto Rico fall within ten minutes' walk of one or more grocery stores. It is estimated that 20% of U.S. population live within a 10 minute walk of a grocery store, and 92% of the population live within a 10 minute drive of a grocery store. The layer is suitable for looking at access at a neighborhood scale.When you add this layer to your web map, along with the drivable access layer and the SafeGraph grocery store layer, it becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. Add the Census block points layer to show a popup with the count of stores within 10 minutes' walk and drive. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or don't own a car?How to Use This Layer in a Web MapUse this layer in a web map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying. See this example web map which you can use in your projects, storymaps, apps and dashboards.The layer was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access.Lastly, this layer can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population's grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples).The layer is a useful visual resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved.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

Monthly average retail prices for selected products, for Canada and provinces. Prices are presented for the current month and the previous four months. Prices are based on transaction data from Canadian retailers, and are presented in Canadian current dollars.

Supermarkets and grocery store outcomes have been a tale of dealing with volatile prices at their purchase and sales points. The continued expansion of Aldi and Amazon has forced the two established industry giants, Woolworths and Coles, to remain price-competitive on both the physical store and online service fronts. To differentiate themselves from low-cost supermarkets, Coles and Woolworths have leant into attracting customers with convenient locations and expanded online shopping capabilities. These supermarket giants also rely on loyalty programs and promotions. Coles and Woolworths have displayed interest in data analytics, strengthening their relationships with analytics firms like Palantir to optimise their marketing and operational processes. The ACCC and Treasury have taken the lead on addressing supplier and customer concerns relating to deceptive discounting practices and supplier contract bargaining exploitation. Supermarket and grocer revenue rose significantly following the COVID-19 outbreak. Household expenditure shifted towards retail industries amid restrictions on many services industries, with this imbalance remaining as high costs limit eating out. A combination of panic buying, along with the suspension of many specials and promotions in supermarkets, boosted grocery turnover at the beginning of the period, spiking revenue for 2019-20. This high benchmark at the start of the period has resulted in an industry correction and an annualised revenue decline of 0.6% to $148.7 billion over the five years to 2024-25. However, stores have largely managed to pass on upstream costs to customers, steadying their profit margins while suppliers and consumers bear the brunt of inflation-driven costs. Revenue is estimated to climb by 0.2% in 2024-25, reflecting the price-driven industry growth more indicative of the overall revenue trend that was drowned out by the pandemic revenue spike and correction. Supermarkets and grocery stores are set to continue performing well with industry revenue slated to climb at an annualised 0.4% over the five years through 2029-30 to $142.8 billion. Population growth and stubborn inflationary pressures, despite rate hikes, are set to keep store prices inching upwards. The results of the Treasury and the ACCC's investigations will shine a light on new regulations and potential penalties in store for large supermarkets. Eventually, when inflationary pressures subside and consumer sentiment returns to a positive level, supermarkets and grocers will be well-positioned to take advantage of consumer appetite for value-added and premium goods. Strong growth in online sales is set to continue.

According to Cognitive Market Research, the global Grocery Store market size will be USD XX million in 2024. It will expand at a compound annual growth rate (CAGR) of 8.80% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.0% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.3% from 2024 to 2031.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 10.8% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.2% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.5% from 2024 to 2031.
The Retail Chain category is the fastest growing segment of the Grocery Store industry

Market Dynamics of Grocery Store Market

Key Drivers for Grocery Store Market

Growing Consumer Interest in Organic and Fresh Foods to Boost Market Growth

The market for grocery stores is significantly influenced by consumers' increased interest in fresh and organic foods. Organic vegetables, dairy, meats, and other minimally processed foods are becoming more and more popular among health-conscious consumers because of their perceived quality, environmental impact, and health benefits. Growing consumer awareness of pesticide use, genetically modified organisms, and additives has led them to favour products with organic labels and those supplied locally. In response, grocery stores have increased the variety of organic products they provide and established special areas, which draw in a devoted clientele and strengthen their brand identity. This change further supports the expansion of the grocery business by being in line with trends toward eco-friendly and sustainable practices.

Growth of Private Label Goods to Drive Market Growth

As private label items provide merchants with larger profit margins and competitive pricing, their expansion is a key factor in the grocery store industry. Store-branded private labels are popular among budget-conscious shoppers seeking high-quality substitutes for national brands. More control over pricing and production allows supermarkets and grocery stores to customize products to consumer tastes and new trends, such as organic or gluten-free options. Because consumers are drawn to unique products that aren't found anywhere else, private labels also encourage customer loyalty, which eventually improves store distinction and boosts total grocery sector sales.

Restraint Factor for the Grocery Store Market

Increasing Online Retailer Competition Will Limit Market Growth

The grocery store sector is being restrained by the growing competition from online retailers, as customers are attracted to the ease of online shopping and doorstep delivery. With their extensive assortment, affordable costs, and easy-to-use platforms that simplify shopping, e-commerce behemoths and grocery delivery services are alluring substitutes for conventional grocery stores. Due to this change, fewer people are visiting physical businesses, particularly younger, tech-savvy shoppers. Online merchants also frequently provide subscription-based discounts and tailored promotions, which help them gain market share. Digital trends are difficult for traditional grocery stores to adopt, which may hinder their expansion and financial success.

Key Trends for

Grocery Stores

Sustainability and Eco-Friendly Initiatives: Consumers are increasingly favoring retailers that are dedicated to minimizing plastic consumption, food waste, and carbon emissions. Grocery stores are embracing sustainable packaging, providing locally sourced produce, and establishing recycling initiatives to resonate with eco-conscious consumer values and ESG (Environmental, Social, Governance) criteria.

Personalization Through Data Analytics and Loyalty Programs: Retailers are utilizing purchase hist...

As of May 2025, New South Wales was home to the highest number of Woolworths Supermarkets locations in Australia, with around *** stores. In total, almost ***** Woolworths grocery stores were in operation across Australia. Woolworths: Australia’s grocery retail market leader Headquartered in Sydney, Australia, Woolworths Group holds the largest share of Australia’s grocery retail market, followed by supermarket rival Coles Group, with the top two competitors holding a market share of over ** percent. German supermarket chain Aldi and Metcash, which operates Independent Grocers of Australia (IGA) and Foodland, are the next largest players. Woolworths’ food sales in Australia have trended upward over the past decade, exceeding ** billion Australian dollars in the 2024 financial year. The grocery giant has also enjoyed a growing profit margin over the past years, despite household budgets tightening due to rising grocery bills reflected in the country’s growing food and non-alcoholic beverages CPI. Grocery prices: how are Australian consumers trying to make savings? Price increases were observed across almost all grocery product categories in Australia in the year to March 2025, with fruit and vegetables seeing an annual increase of around *** percent. With the cost of groceries becoming a more prevalent concern in recent years, consumers are employing various strategies to save money on grocery products. These money-saving tactics include cutting back on non-essentials, reducing overall spending, switching to cheaper brands, and shopping across multiple stores.

This layer shows which parts of the United States and Puerto Rico fall within ten minutes' walk of one or more grocery stores. It is estimated that 20% of U.S. population live within a 10 minute walk of a grocery store, and 92% of the population live within a 10 minute drive of a grocery store. The layer is suitable for looking at access at a neighborhood scale.When you add this layer to your web map, along with the drivable access layer and the SafeGraph grocery store layer, it becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. Add the Census block points layer to show a popup with the count of stores within 10 minutes' walk and drive. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or don't own a car?How to Use This Layer in a Web MapUse this layer in a web map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying. See this example web map which you can use in your projects, storymaps, apps and dashboards.The layer was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access.Lastly, this layer can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population's grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples).The layer is a useful visual resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved.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

Data Source: Open Data DC and American Community Survey (ACS) 1-Year Estimates.

Why This Matters

Living further from full-service grocery stores can force residents to shop for food at locations that are more expensive or have fewer healthy options, leading to worse health outcomes for conditions such as obesity or diabetes.

Beyond basic nutrition, food is an integral part of culture. Having access to a wide array of culturally relevant foods has been shown to improve well-being among Black, Indigenous, and people of color communities.

Across the United States, predominantly-Black communities have fewer supermarkets than predominantly white and Hispanic communities. A pattern of disinvestment limits the availability of fresh and healthy foods.

The District Response

The Food Access Fund (FAF) Grant increases equitable access to fresh, healthy, and affordable food by supporting the opening of new grocery stores in areas with low food access, with priority given to locations in Ward 7 or Ward 8. The Produce Plus Program provides financial support for residents with low access to fresh foods to spend at local farmers markets.

The SUN Bucks program provides additional grocery-buying benefits to income-eligible families when schools are closed for the summer and children no longer have access to free or reduced-cost meals at school.

The DC Food Policy Council convenes six working groups, including the Food Access & Equity working group that aims to communicate and collaborate with residents to increase awareness of District food benefit programs and healthy food retail.

This layer shows which parts of the United States and Puerto Rico fall within ten minutes' walk of one or more grocery stores. It is estimated that 20% of U.S. population live within a 10 minute walk of a grocery store, and 92% of the population live within a 10 minute drive of a grocery store. The layer is suitable for looking at access at a neighborhood scale.When you add this layer to your web map, along with the drivable access layer and the SafeGraph grocery store layer, it becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. Add the Census block points layer to show a popup with the count of stores within 10 minutes' walk and drive. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or don't own a car?How to Use This Layer in a Web MapUse this layer in a web map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying. See this example web map which you can use in your projects, storymaps, apps and dashboards.The layer was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access.Lastly, this layer can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population's grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples).The layer is a useful visual resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved.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

Data Source: Open Data DC and American Community Survey (ACS) 1-Year Estimates.

Why This Matters

Living further from full-service grocery stores can force residents to shop for food at locations that are more expensive or have fewer healthy options, leading to worse health outcomes for conditions such as obesity or diabetes.

Beyond basic nutrition, food is an integral part of culture. Having access to a wide array of culturally relevant foods has been shown to improve well-being among Black, Indigenous, and people of color communities.

Across the United States, predominantly-Black communities have fewer supermarkets than predominantly white and Hispanic communities. A pattern of disinvestment limits the availability of fresh and healthy foods.

The District Response

The Food Access Fund (FAF) Grant increases equitable access to fresh, healthy, and affordable food by supporting the opening of new grocery stores in areas with low food access, with priority given to locations in Ward 7 or Ward 8. The Produce Plus Program provides financial support for residents with low access to fresh foods to spend at local farmers markets.

The SUN Bucks program provides additional grocery-buying benefits to income-eligible families when schools are closed for the summer and children no longer have access to free or reduced-cost meals at school.

The DC Food Policy Council convenes six working groups, including the Food Access & Equity working group that aims to communicate and collaborate with residents to increase awareness of District food benefit programs and healthy food retail.

Learn about the cheapest ground beef available in popular grocery stores and retailers, and how to find affordable options for delicious meals without sacrificing quality or taste.

​Find out where you can access free or low cost food programs including meals, grocery hampers, food bank programs, low cost groceries, or grocery vouchers in the City of Vancouver. Note​City staff updates this data on a regular basis in consultation wit​h organizations operating food programs. We do our best to keep the program as accurate as possible, but recommend contacting organizations directly to verify the program information. If you notice data that is inaccurate, please contact staff at foodpolicy@vancouver.ca so that an update can be made.​ Data currency​City staff update this data on a regular basis in consultation with organizations operating food programs, however priorities and resources determine how fast program changes are reflected in the database. Records include a last updated date. This dataset is refreshed daily. Websites for further information Help get and give food City of Vancouver Food Policy ​

Close proximity is defined as living within a half mile for urban populations and within ten miles for rural populations.Living near a grocery store or supermarket can directly impact food security, be cost-effective, and lead to a healthier diet. Poor diet has contributed to our current obesity epidemic and is a major risk factor for heart disease, diabetes, cancer, and many other chronic health conditions. It can be very challenging for people to have a healthy diet if they have limited access to nutritious and affordable food options.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

This layer represents USDA Food Access Research Atlas data at the census tract geography. Low Income is defined as tracts with a poverty rate of 20% or higher, or tracts with median family income less than 80% of median family income of the state or metropolitan area. Low Access is defined as tracts where a significant number or share of residents is more than 1 mile (urban) or 10 miles (rural) from the nearest supermarket.http://www.ers.usda.gov/data-products/food-access-research-atlas/go-to-the-atlas.aspxFood accessLimited access to supermarkets, supercenters, grocery stores, or other sources of healthy and affordable food may make it harder for some Americans to eat a healthy diet. There are many ways to measure food store access for individuals and for neighborhoods, and many ways to define which areas are food deserts—neighborhoods that lack healthy food sources. Most measures and definitions take into account at least some of the following indicators of access:Accessibility to sources of healthy food, as measured by distance to a store or by the number of stores in an area.Individual-level resources that may affect accessibility, such as family income or vehicle availability.Neighborhood-level indicators of resources, such as the average income of the neighborhood and the availability of public transportation.In the Food Access Research Atlas, several indicators are available to measure food access along these dimensions. For example, users can choose alternative distance markers to measure low access in a neighborhood, such as the number and share of people more than half a mile to a supermarket or 1 mile to a supermarket. Users can also view other census-tract-level characteristics that provide context on food access in neighborhoods, such as whether the tract has a high percentage of households far from supermarkets and without vehicles, individuals with low income, or people residing in group quarters.Low-income neighborhoodsThe criteria for identifying a census tract as low income are from the Department of Treasury’s New Markets Tax Credit (NMTC) program. This program defines a low-income census tract as any tract where:The tract’s poverty rate is 20 percent or greater; orThe tract’s median family income is less than or equal to 80 percent of the State-wide median family income; orThe tract is in a metropolitan area and has a median family income less than or equal to 80 percent of the metropolitan area's median family income.Low-access census tractsIn the Food Access Research Atlas, low access to healthy food is defined as being far from a supermarket, supercenter, or large grocery store ("supermarket" for short). A census tract is considered to have low access if a significant number or share of individuals in the tract is far from a supermarket.In the original Food Desert Locator, low access was measured as living far from a supermarket, where 1 mile was used in urban areas and 10 miles was used in rural areas to demarcate those who are far from a supermarket. In urban areas, about 70 percent of the population was within 1 mile of a supermarket, while in rural areas over 90 percent of the population was within 10 miles (see Access to Affordable and Nutritious Food: Updated Estimates of Distance to Supermarkets Using 2010 Data). Updating the original 1- and 10-mile low-access measure shows that an estimated 18.3 million people in these low-income and low-access census tracts were far from a supermarket in 2010.Three additional measures of food access based on distance to a supermarket are provided in the Atlas:One additional measure applies a 0.5-mile demarcation in urban areas and a 10-mile distance in rural areas. Using this measure, an estimated 52.5 million people, or 17 percent of the U.S. population, have low access to a supermarket;A second measure applies a 1.0-mile demarcation in urban areas and a 20-mile distance in rural areas. Under this measure, an estimated 16.5 million people, or 5.3 percent of the U.S. population, have low access to a supermarket; andA slightly more complex measure incorporates vehicle access directly into the measure, delineating low-income tracts in which a significant number of households are located far from a supermarket and do not have access to a vehicle. This measure also includes census tracts with populations that are so remote, that, even with a vehicle, driving to a supermarket may be considered a burden due to the great distance. Using this measure, an estimated 2.1 million households, or 1.8 percent of all households, in low-income census tracts are far from a supermarket and do not have a vehicle. An additional 0.3 million people are more than 20 miles from a supermarket.For each of the first three measures that are based solely on distance, a tract is designated as low access if the aggregate number of people in the census tract with low access is at least 500 or the percentage of people in the census tract with low access is at least 33 percent. For the final measure using vehicle availability, a tract is designated as having low vehicle access if at least one of the following is true:at least 100 households are more than ½ mile from the nearest supermarket and have no access to a vehicle; orat least 500 people or 33 percent of the population live more than 20 miles from the nearest supermarket, regardless of vehicle access.Methods used to assess distance to the nearest supermarket are the same for each of these measures. First, the entire country is divided into ½-km square grids, and data on the population are aerially allocated to these grids (see Access to Affordable and Nutritious Food: Updated Estimates of Distance to Supermarkets Using 2010 Data). Then, distance to the nearest supermarket is measured for each grid cell by calculating the distance between the geographic center of the ½-km square grid that contains estimates of the population (number of people and other subgroup characteristics) and the center of the grid with the nearest supermarket.Once the distance to the nearest supermarket is calculated for each grid cell, the estimated number of people or housing units that are more than 1 mile from a supermarket in urban tracts, or 10 miles in rural census tracts, is aggregated at the census-tract level (and similarly for the alternative distance markers). A census tract is considered rural if the population-weighted centroid of that tract is located in an area with a population of less than 2,500; all other tracts are considered urban tracts.Food desertsThe Food Access Research Atlas maps census tracts that are both low income (li) and low access (la), as measured by the different distance demarcations. This tool provides researchers and other users multiple ways to understand the characteristics that can contribute to food deserts, including income level, distance to supermarkets, and vehicle access.Additional tract-level indicators of accessVehicle availabilityA tract is identified as having low vehicle availability if more than 100 households in the tract report having no vehicle available and are more than 0.5 miles from the nearest supermarket. This corresponds closely to the 80th percentile of the distribution of the number of housing units in a census tract without vehicles at least 0.5 miles from a supermarket (the 80th percentile value was 106 housing units). This means that about 20 percent of all census tracts had more than 100 housing units that were 0.5 miles from a supermarket and without a vehicle. This indicator was applied to both urban and rural census tracts.Overall, 8.8 percent of all housing units in the United States do not have a vehicle, and 4.2 percent of all housing units are at least 0.5 mile from a store and without a vehicle. Vehicle availability is defined in the American Community Survey as the number of passenger cars, vans, or trucks with a capacity of 1-ton or less kept at the home and available for use by household members. The number of available vehicles includes those vehicles leased or rented for at least 1 month, as well as company, police, or government vehicles that are kept at home and available for non-business use.Whether a vehicle is available to a household for private use is an important additional indicator of access to healthy and affordable food. For households living far from a supermarket or large grocery store, access to a private vehicle may make accessing these retailers easier than relying on public or alternative means of transportation.Group quarters populationUsers may be interested in highlighting tracts with large shares of people living in group quarters. Group quarters are residential arrangements where an entity or organization owns and provides housing (and often services) for individuals residing in these buildings. This includes college dormitories, military quarters, correctional facilities, homeless shelters, residential treatment centers, and assisted living or skilled nursing facilities. These living arrangements frequently provide dining and food retail solely for their residents. While individuals living in these areas may appear to be far from a supermarket or grocery store, they may not truly experience difficulty accessing healthy and affordable food. Tracts in which 67 percent of individuals or more live in group quarters are highlighted.General tract characteristicsPopulation, tract totalGeographic level: census tractYear of data: 2010Definition: Total number of individuals residing in a tract.Data sources: Data are from the 2012 report, Access to Affordable and Nutritious Food: Updated Estimates of Distances to Supermarkets Using 2010 Data. Population data are reported at the block level from the 2010 Census of Population and Housing. These data were aerially allocated down to ½-kilometer-square grids across the United States.Low-income tractGeographic level: census tractYear of data: 2010Definition: A tract with either a poverty rate of 20

AI-Driven Grocery Demand Planning Market Outlook

According to our latest research, the AI-Driven Grocery Demand Planning market size reached USD 2.15 billion in 2024, reflecting a robust adoption of artificial intelligence solutions across the global grocery sector. The market is projected to expand at a CAGR of 24.6% between 2025 and 2033, and is expected to achieve a value of USD 17.6 billion by 2033. The primary growth factor driving this remarkable expansion is the increasing necessity for precise inventory management and demand forecasting as grocery retailers strive to reduce waste, optimize supply chains, and meet evolving consumer expectations in a dynamic retail environment.

The growth of the AI-Driven Grocery Demand Planning market is fueled by several key factors, with the most significant being the rapid digital transformation within the grocery industry. The proliferation of e-commerce platforms and omnichannel retailing has radically altered the way consumers interact with grocery stores, leading to more complex and unpredictable demand patterns. AI-powered solutions are increasingly being leveraged to analyze vast datasets in real-time, enabling retailers to anticipate shifts in consumer preferences, respond swiftly to supply chain disruptions, and minimize stockouts or overstock situations. The integration of AI not only enhances operational efficiency but also supports sustainability initiatives by reducing food waste and optimizing resource allocation.

Another critical growth driver is the growing pressure on grocery retailers to deliver superior customer experiences while maintaining profitability. In today's highly competitive landscape, consumers expect product availability, personalized promotions, and seamless shopping experiences across all channels. AI-driven demand planning tools empower retailers to fine-tune pricing strategies, forecast demand with higher accuracy, and automate replenishment processes. This leads to improved shelf availability, reduced markdowns, and increased customer loyalty. The ability of AI to process and interpret complex variables—such as weather patterns, local events, and social media trends—further strengthens its value proposition for grocery retailers seeking to stay ahead of the competition.

Furthermore, the ongoing advancements in machine learning algorithms and data analytics are making AI-driven solutions more accessible and cost-effective for a broader range of grocery businesses, including small and medium enterprises. Cloud-based deployment models, in particular, have lowered barriers to entry by providing scalable, flexible, and affordable options for retailers of all sizes. As regulatory requirements for food safety and traceability become more stringent, AI-driven platforms are also being adopted to ensure compliance and enhance transparency across the supply chain. These factors collectively contribute to the sustained growth and widespread adoption of AI-driven grocery demand planning solutions.

From a regional perspective, North America currently dominates the AI-Driven Grocery Demand Planning market, accounting for the largest revenue share in 2024. This leadership position is attributed to the region's advanced retail infrastructure, high adoption rate of digital technologies, and the presence of major technology providers. Europe follows closely, driven by strong regulatory frameworks and a focus on sustainability. The Asia Pacific region is emerging as the fastest-growing market, propelled by rapid urbanization, expanding middle-class populations, and increasing investments in digital transformation by grocery retailers. Latin America and the Middle East & Africa are also witnessing gradual growth as awareness of AI benefits spreads and infrastructural improvements are made.

Component Analysis

The AI-Driven Grocery Demand Planning market is segmented by component into software and services, each playing a pivotal role in the broader adoption of AI technologies within the grocery sector. The software segment encompasses a range of AI-powered platforms designed for demand forecasting, inventory optimization, and supply chain management. These platforms leverage advanced algorithms and machine learning models to process historical sales data, real-time inventory levels, and external factors such as weather or local events, providing actionable insights that drive efficient decision-making. The continuous innovation in software solutions

The figure shows the most affordable stores when it comes to discount products in Italy as of 2024. The cheapest was Lidl, thus scoring 100 points*, followed by Eurospin, at *** points. Carrefour Market was the last in the list with *** points, meaning it was on average ** percent more expensive than the cheapest store.

The private label food and beverage market is experiencing robust growth, driven by increasing consumer demand for affordable, high-quality products. Consumers are becoming more price-conscious, particularly in the face of persistent inflation, leading to a significant shift towards private label options. This trend is further fueled by the rising popularity of discount retailers and warehouse clubs like Aldi, Costco, and Walmart, which often feature their own strong private label brands. These retailers leverage economies of scale and efficient supply chains to offer competitive pricing, attracting a broader consumer base and driving market expansion. Furthermore, improvements in product quality and innovation within private label offerings are blurring the lines between store brands and national brands, eroding brand loyalty and expanding the appeal of private label products across various demographics. The market is segmented by product category (e.g., dairy, snacks, beverages), distribution channel (e.g., supermarkets, hypermarkets, online retailers), and geographic region. Competition is intense, with established players and emerging brands vying for market share. However, challenges persist, including potential fluctuations in raw material costs and maintaining consistent product quality across a diverse range of offerings. The projected Compound Annual Growth Rate (CAGR) indicates continued expansion of the market throughout the forecast period (2025-2033). While precise figures are not provided, a reasonable estimate considering industry trends and the competitive landscape suggests a moderate CAGR of around 5-7% is likely. This growth will be influenced by factors like evolving consumer preferences, innovative product development by retailers, and the continued expansion of discount retail formats. Geographic variations in growth will likely exist, with regions experiencing higher disposable income growth and a greater adoption of private label products demonstrating faster expansion. Maintaining a strong focus on quality, innovation, and effective marketing strategies will be crucial for success within this dynamic market. Future growth will likely be further fueled by technological advancements within the food and beverage industry and the growing adoption of e-commerce platforms for grocery shopping.