MealMe provides comprehensive grocery and retail SKU-level product data, including real-time pricing, from the top 100 retailers in the USA and Canada. Our proprietary technology ensures accurate and up-to-date insights, empowering businesses to excel in competitive intelligence, pricing strategies, and market analysis.

Retailers Covered: MealMe’s database includes detailed SKU-level data and pricing from leading grocery and retail chains such as Walmart, Target, Costco, Kroger, Safeway, Publix, Whole Foods, Aldi, ShopRite, BJ’s Wholesale Club, Sprouts Farmers Market, Albertsons, Ralphs, Pavilions, Gelson’s, Vons, Shaw’s, Metro, and many more. Our coverage spans the most influential retailers across North America, ensuring businesses have the insights needed to stay competitive in dynamic markets.

Key Features: SKU-Level Granularity: Access detailed product-level data, including product descriptions, categories, brands, and variations. Real-Time Pricing: Monitor current pricing trends across major retailers for comprehensive market comparisons. Regional Insights: Analyze geographic price variations and inventory availability to identify trends and opportunities. Customizable Solutions: Tailored data delivery options to meet the specific needs of your business or industry. Use Cases: Competitive Intelligence: Gain visibility into pricing, product availability, and assortment strategies of top retailers like Walmart, Costco, and Target. Pricing Optimization: Use real-time data to create dynamic pricing models that respond to market conditions. Market Research: Identify trends, gaps, and consumer preferences by analyzing SKU-level data across leading retailers. Inventory Management: Streamline operations with accurate, real-time inventory availability. Retail Execution: Ensure on-shelf product availability and compliance with merchandising strategies. Industries Benefiting from Our Data CPG (Consumer Packaged Goods): Optimize product positioning, pricing, and distribution strategies. E-commerce Platforms: Enhance online catalogs with precise pricing and inventory information. Market Research Firms: Conduct detailed analyses to uncover industry trends and opportunities. Retailers: Benchmark against competitors like Kroger and Aldi to refine assortments and pricing. AI & Analytics Companies: Fuel predictive models and business intelligence with reliable SKU-level data. Data Delivery and Integration MealMe offers flexible integration options, including APIs and custom data exports, for seamless access to real-time data. Whether you need large-scale analysis or continuous updates, our solutions scale with your business needs.

Why Choose MealMe? Comprehensive Coverage: Data from the top 100 grocery and retail chains in North America, including Walmart, Target, and Costco. Real-Time Accuracy: Up-to-date pricing and product information ensures competitive edge. Customizable Insights: Tailored datasets align with your specific business objectives. Proven Expertise: Trusted by diverse industries for delivering actionable insights. MealMe empowers businesses to unlock their full potential with real-time, high-quality grocery and retail data. For more information or to schedule a demo, contact us today!

MealMe provides comprehensive grocery and retail POI and SKU-level product data, including real-time pricing, from the top 100 retailers in the USA and Canada. Our proprietary technology ensures accurate and up-to-date insights, empowering businesses to excel in competitive intelligence, pricing strategies, and market analysis.

Retailers Covered: MealMe’s database includes detailed SKU-level data and pricing from leading grocery and retail chains such as Walmart, Target, Costco, Kroger, Safeway, Publix, Whole Foods, Aldi, ShopRite, BJ’s Wholesale Club, Sprouts Farmers Market, Albertsons, Ralphs, Pavilions, Gelson’s, Vons, Shaw’s, Metro, and many more. Our coverage spans the most influential retailers across North America, ensuring businesses have the insights needed to stay competitive in dynamic markets.

Key Features: SKU-Level Granularity: Access detailed product-level data, including product descriptions, categories, brands, and variations. Real-Time Pricing: Monitor current pricing trends across major retailers for comprehensive market comparisons. Regional Insights: Analyze geographic price variations and inventory availability to identify trends and opportunities. Customizable Solutions: Tailored data delivery options to meet the specific needs of your business or industry. Use Cases: Competitive Intelligence: Gain visibility into pricing, product availability, and assortment strategies of top retailers like Walmart, Costco, and Target. Pricing Optimization: Use real-time data to create dynamic pricing models that respond to market conditions. Market Research: Identify trends, gaps, and consumer preferences by analyzing SKU-level data across leading retailers. Inventory Management: Streamline operations with accurate, real-time inventory availability. Retail Execution: Ensure on-shelf product availability and compliance with merchandising strategies. Industries Benefiting from Our Data CPG (Consumer Packaged Goods): Optimize product positioning, pricing, and distribution strategies. E-commerce Platforms: Enhance online catalogs with precise pricing and inventory information. Market Research Firms: Conduct detailed analyses to uncover industry trends and opportunities. Retailers: Benchmark against competitors like Kroger and Aldi to refine assortments and pricing. AI & Analytics Companies: Fuel predictive models and business intelligence with reliable SKU-level data. Data Delivery and Integration MealMe offers flexible integration options, including APIs and custom data exports, for seamless access to real-time data. Whether you need large-scale analysis or continuous updates, our solutions scale with your business needs.

Why Choose MealMe? Comprehensive Coverage: Data from the top 100 grocery and retail chains in North America, including Walmart, Target, and Costco. Real-Time Accuracy: Up-to-date pricing and product information ensures competitive edge. Customizable Insights: Tailored datasets align with your specific business objectives. Proven Expertise: Trusted by diverse industries for delivering actionable insights. MealMe empowers businesses to unlock their full potential with real-time, high-quality grocery and retail data. For more information or to schedule a demo, contact us today!

Analysis of ‘Volume Forecasting’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/utathya/future-volume-prediction on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Context

Country Beeristan, a high potential market, accounts for nearly 10% of Stallion & Co.’s global beer sales. Stallion & Co. has a large portfolio of products distributed to retailers through wholesalers (agencies). There are thousands of unique wholesaler-SKU/products combinations. In order to plan its production and distribution as well as help wholesalers with their planning, it is important for Stallion & Co. to have an accurate estimate of demand at SKU level for each wholesaler.

Currently demand is estimated by sales executives, who generally have a “feel” for the market and predict the net effect of forces of supply, demand and other external factors based on past experience. The more experienced a sales exec is in a particular market, the better a job he does at estimating. Joshua, the new Head of S&OP for Stallion & Co. just took an analytics course and realized he can do the forecasts in a much more effective way. He approaches you, the best data scientist at Stallion, to transform the exercise of demand forecasting.

Content

You are provided with the following data: price_sales_promotion.csv: ($/hectoliter) Holds the price, sales & promotion in dollar value per hectoliter at Agency-SKU-month level historical_volume.csv: (hectoliters) Holds sales data at Agency-SKU-month level from Jan 2013 to Dec 2017 weather.csv: (Degree Celsius) Holds average maximum temperature at Agency-month level industry_soda_sales.csv: (hectoliters) Holds industry level soda sales event_calendar.csv: Holds event details (sports, carnivals, etc.) industry_volume.csv: (hectoliters) Holds industry actual beer volume demographics.csv: Holds demographic details (Yearly income in $)

Test data Formats Volume_forecast.csv: You need to first forecast the demand volume for Jan’18 of all agency-SKU combination. sku_recommendation.csv: Secondly, you need to suggest 2 SKUs which can be sold by Agency06 & Agency14. These two agencies are new and company wants to find out which two products would be the best products for these two agencies.

Acknowledgements

Thanks to Analytics Vidya and AbinBev for making this data available for us.

Inspiration

Can anyone please forecast for Jan18? I also want to understand the analysis carried out. Thanks.

--- Original source retains full ownership of the source dataset ---

SHAPE - SHelf mAnagement Product datasEtSHAPE (SHelf mAnagement Product datasEt) contains ~46K images of ~16K different SKU (Stock Keeping Unit) belonging to 62 different categories, fine-grained labeled with their category and European Article Number (EAN). Category and EAN are anonymized, real values could be released under commercial agreement.Dataset is structured as follow:First level folders are categories (anonymized with numbers 1,2,3...), second level folders are SKU (EANs are anonymized with numbers 1,2,3).Please refer to the original publication for any detail. Also when using the data, please cite the original paper:https://doi.org/10.1016/j.eswa.2024.124635

Big-Box Home Improvement: Bulk Discount Sales Dataset

Unlock insights into big-box home improvement retail with a high-quality synthetic dataset centered on project types, departments, SKUs, and tiered bulk discounts. Designed for analytics, pricing optimization, and retail strategy modeling, this dataset captures realistic store transactions across major departments like Electrical, Plumbing, Lumber, Paint, Flooring, Appliances, and more. It includes true-to-market price bands, brand mix, pro program participation, and volume-based discount logic widely used by large home-improvement chains.

This dataset is ideal for: - Pricing analytics, bulk-discount simulations, and elasticity modeling - Customer segmentation (pro vs. retail), loyalty and rewards analytics - Forecasting demand by department, brand, or project type - Basket analysis and project-type purchasing patterns - Data cleaning, feature engineering, and retail ETL practice

Key features: - 2,341 rows and 14 columns - ~60% numerical data to support modeling and dashboards - Realistic price tiers, quantities, discount structures, and date ranges (last 18 months) - Nulls in non-critical fields to enable data quality and preprocessing workflows - Export-ready for CSV, XLSX, SQL (SQLite), and JSON use cases

What You Can Do With This Dataset

• Build a price optimization engine using unit_price, quantity, and discount tiers
• Train ML models for discount propensity, total revenue prediction, and churn risk
• Create executive dashboards: revenue by department, promo uplift, pro-program impact
• Engineer features such as seasonality, day-of-week effects, or brand-price clustering
• Run A/B-style simulations on bulk_discount_applied_% to optimize margin

Column Dictionary

Notes: - Flooring logic: unit_price is per-square-foot; quantity equals total sqft; totals reflect sqft × price. - Bulk tiers vary by template (e.g., 0/10/15%, 0/10/20%, 0/5/10/25%) to simulate volume pricing. - Occasional promotional boosts simulate seasonal or campaign-driven markdowns. - Null values are injected into non-critical fields (e.g., brand, project_type, location) for realistic data cleaning practice.

SEO-Rich Use Cases and Keywords

Use this dataset for price optimization, bulk discount modeling, retail analytics, pro customer segmentation, demand forecasting, SKU-level profit analysis, and department revenue dashboards. Keywords: home improvement dataset, retail bulk discounts, volume pricing data, pro loyalty analytics, department sales data, SKU pricing, project type retail analytics, synthetic retail dataset, big-box store data, pricing optimization dataset.

Example Analytics Ideas

• Compare discount elasticity across Tools & Hardware vs. Electrical
• Evaluate pro_program uplift on total_after_discount and average order value
• Cluster brands by price bands and discount sensitivity
• Time-series forecasting of department-level sales with purchase_date
• Identify top project types driving high-quantity, bulk-eligible purchases

Data Quality and Preparation Tips

• Impute or encode nulls for category features (brand, project_type, store_location)
• Normalize price and quantity outliers by department price bands
• For Flooring, engineer a “is_flooring” flag and compute deri...

Basketview Signal CPG Receipt & POS Consumer Data: A Fusion of Shopper Behavior Details

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. Basketview Signal CPG is aggregated CPG-tagged consumer purchase data that delivers the fastest daily data with 3-day lag, beating leading competitors by up to 20 days. Designed to meet the needs of financial services teams, enjoy a deeper level of market measurement of CPG product purchases and customer behavior with 5 years of data history combined with the ability to integrate with other CE datasets for more holistic analysis. Basketview Signal CPG offers CPG-tagged data on the world’s leading brands combined with CE’s proprietary attribution to product brand and parent company, unlocking insights into retail sales of both established and emerging brands by channel, geography, and more.

Consumer Edge’s Basketview Signal CPG with receipt and POS data offers insights into tracked retail channels including: • Mass • Grocery • Drug • Club • Dollar • Independent Convenience & Gas • Specialty Pet

Benefits • Visibility into CPG Sales: Understand online and offline wholesale sales for leading consumer packaged goods brands and companies, plus specialty channels • Understand Category Performance: Analyze performance by category and brand to inform investment theses. • Fine-Tune Competitive Analysis: Explore CPG retail performance compared to the category or top competitors with SKU-level data mapped to brand, company, and ticker. • See How Retail Pricing Drives Volume: Analyze how pricing affects product brand selection among CPG customers, calculate elasticity, and measure inflation of baskets. • Cut by Channel and Geography: See how trends differ across individual retail channels and subindustries as well as all 50 US states. • Daily Delivery and Granularity: Near real-time updates 365 days a year, with the ability to drill down into individual days to see the impact of promotions, holidays, and weather. Easy aggregation into company fiscal periods either from the day level or via CE aggregation. • Easier Comparisons: Data for different package sizes is equivalized into volumes (e.g., OUNCES) for better estimation of true demand.

Use Case: Pet Specialty Forecasting

Problem A public investor wants to refine their tools for company modeling on key investments in the pet specialty industry.

Solution The firm leveraged Consumer Edge Basketview Signal CPG data to: • Gain visibility into CPG sales for pet brands across mass, grocery, drug, club, dollar, convenience, and pet specialty channels – including both offline and e-commerce • Better understand overall performance of pet specialty categories and brands • Explore retail performance for key pet brands compared to the category with SKU-level data aggregated to brand, company, and ticker • See how retail pricing influences pet brand selection

Metrics Include: • Spend • Items • Volume • Transactions • Price Per Volume

Inquire about a Basketview subscription to perform more complex, near real-time analyses on public tickers and private brands as well as for industries beyond CPG like: • How changes in distribution and channels are affecting a brand’s overall sales • Understand consumer price elasticity • Analyze emerging trends in inflation

Consumer Edge offers a variety of datasets covering the US, Europe (UK, Austria, France, Germany, Italy, Spain), and across the globe, with subscription options serving a wide range of business needs.

Consumer Edge is the Leader in Data-Driven Insights Focused on the Global Consumer

1) Data Introduction • The Supply Chain DataSet includes a variety of information across the supply chain, including production, inventory, ordering, shipping, sales, suppliers, and transportation of products, which can be used to analyze supply chains and research efficiency in the manufacturing and distribution industries.

2) Data Utilization (1) Supply Chain DataSet has characteristics that: • This dataset includes a variety of characteristics needed to operate the supply chain, including product type, SKU, price, inventory level, sales, sales, lead time, manufacturing and transportation costs, defect rates, and customer characteristics. (2) Supply Chain DataSet can be used to: • Inventory and Demand Forecast: Data such as inventory, sales volume, and lead time can be used to forecast demand and develop inventory optimization models. • Supply chain efficiency analysis: By analyzing various indicators such as sales, cost, defect rate, and transportation by product, it can be used to improve supply chain operational efficiency and strategize.

Autonomous Retail SKU-Robot Restocker Market Outlook

According to our latest research, the global autonomous retail SKU-robot restocker market size reached USD 2.14 billion in 2024, with a robust year-on-year growth driven by the rapid adoption of automation technologies in the retail sector. The market is projected to expand at a CAGR of 18.7% from 2025 to 2033, reaching a forecasted market size of USD 11.47 billion by 2033. This impressive growth is primarily attributed to the increasing need for operational efficiency, reduction of labor costs, and the ongoing digital transformation within the retail industry, as per our latest research findings.

The primary growth factor propelling the autonomous retail SKU-robot restocker market is the surging demand for real-time inventory management and stock optimization in retail environments. Retailers are under immense pressure to maintain optimal stock levels, reduce out-of-stock situations, and enhance customer satisfaction. The integration of autonomous robots for SKU restocking addresses these challenges by providing continuous shelf monitoring, data-driven replenishment, and minimizing human error. As retailers strive to improve their supply chain visibility and agility, the deployment of SKU-robot restockers offers a scalable and cost-effective solution that aligns with the evolving expectations of modern consumers and the competitive landscape of the retail sector.

Another significant driver is the escalating labor shortages and rising wage costs across various regions, especially in developed markets. Retailers are increasingly challenged by high employee turnover rates, absenteeism, and the growing complexity of managing large inventories across multiple store formats. Autonomous SKU-robot restockers mitigate these challenges by automating repetitive and labor-intensive tasks, thereby freeing up human resources for higher-value activities such as customer engagement and strategic planning. This shift not only enhances productivity but also contributes to a safer and more ergonomic working environment, reducing workplace injuries associated with manual restocking.

Technological advancements in artificial intelligence, machine vision, and robotics are further accelerating market growth. Innovations in AI-based algorithms and sensor technology have enabled SKU-robot restockers to achieve higher levels of accuracy, adaptability, and learning capability. These robots can now navigate complex store layouts, recognize a wide range of products, and interact seamlessly with other in-store systems. The convergence of AI, IoT, and cloud computing is facilitating real-time data sharing and analytics, empowering retailers with actionable insights for demand forecasting, trend analysis, and inventory planning. As technology continues to mature, the cost of deployment is expected to decrease, making autonomous SKU-robot restockers accessible to a broader spectrum of retailers, including small and medium enterprises.

Robotic Replenishment Systems are revolutionizing the way retailers manage their inventory and restocking processes. These systems integrate seamlessly with existing retail operations, providing a robust solution for maintaining optimal stock levels and ensuring product availability. By leveraging advanced robotics and automation technologies, Robotic Replenishment Systems enable retailers to automate repetitive tasks, reduce human error, and enhance overall operational efficiency. The implementation of these systems is particularly beneficial in environments with high product turnover and complex inventory requirements, allowing retailers to respond swiftly to changing consumer demands and market conditions. As the retail industry continues to evolve, the adoption of Robotic Replenishment Systems is expected to become increasingly prevalent, offering a competitive edge to retailers who prioritize innovation and efficiency.

From a regional perspective, North America currently dominates the autonomous retail SKU-robot restocker market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The high adoption rate in North America is fueled by the presence of major retail chains, advanced technology infrastructure, and a strong focus on operational efficiency. Europe is witnessing significant growth due to stringent labor regulatio

This dataset provides detailed inventory turnover records by SKU and warehouse, including movement quantities, turnover rates, and slow-moving item flags for defined periods. It enables granular analysis of stock efficiency, helps optimize inventory levels, and supports cost reduction by identifying underperforming products. The data is ideal for supply chain optimization, warehouse management, and strategic purchasing decisions.

Understand customer behaviour, competitive benchmarking, market share etc. using Measurable AI's email receipt dataset. We own a proprietary consumer panel whereby we can access the email accounts of over 2 million users. We are GDPR compliant as we expressly receive consumer consent via our two consumer apps we built in-house: 1) Mailtime (YC2016; an email productivity app), and 2) RewardMe (cash back app that automatically rewards users with cash dollars for their real purchase data; no need to upload receipts).

We then build email parsers to parse through all the transactional data and then aggregate and anonymise the datasets to produce granular insights for our data savvy clientele.

We provide SKU-level transaction data with actual amount spent, discounts, purchase frequency, time, geolocation data.

The dataset contains historical data for inventory-active products from the previous 8 weeks of the week we would like to predict, captured as a photo of all inventory at the beginning of the week.

Attributes SKU: Unique material identifier; INV: Current inventory level of material; TIM: Registered transit time; FOR-: Forecast sales for the next 3, 6, and 9 months; SAL-: Sales quantity for the prior 1, 3, 5, and 9 months; MIN: Minimum recommended amount in stock (MIN); OVRP: Parts overdue from source; SUP-: Supplier performance in last 1 and 2 semesters; OVRA: Amount of stock orders overdue (OVRA); RSK-: General risk flags associated to the material; BO: Product went on backorder.

Evaluation Metrics We applied Area Under Receiver Operator Curve (AUROC) for primary evaluation, and Precision-Recall curves for post-analysis.

500 actual SKUs from an outdoor apparel brand's product catalog. It's somewhat rare to get real item level data in a real-world format. Very useful for testing things like recommendation engines. In fact...maybe I'll publish some code along with this :)

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.

🛒 Walmart Product Reviews Dataset (6.7K Records)

This dataset contains 6,700+ structured customer reviews from Walmart.com. Each entry includes product-level metadata along with review details, making it ideal for small-scale machine learning models, sentiment analysis, and ecommerce insights.

  📑 Dataset Fields

Column Description

url Direct product page URL

name Product name/title

sku Product SKU (Stock Keeping Unit)

price Product price (numeric, USD)… See the full description on the dataset page: https://huggingface.co/datasets/crawlfeeds/walmart-reviews-dataset.

ZARA UK Fashion Dataset offers an extensive collection of fashion product data from ZARA's UK online store, providing a detailed overview of available items. This dataset is valuable for analyzing the European fashion retail market, particularly in the UK, and includes fields such as product titles, URLs, SKUs, MPNs, brands, prices, currency, images, breadcrumbs, country, availability, unique IDs, and timestamps for when the data was scraped.

Key Features:

• Product Details: Includes title, URL, SKU (Stock Keeping Unit), MPN (Manufacturer Part Number), and brand for each product, helping to uniquely identify and differentiate items.
• Pricing Information: Features the price of each product along with the currency used (GBP) to understand the pricing strategies of ZARA in the UK market.
• Visual Data: High-quality images of each product, essential for visual merchandising analysis and online consumer behavior studies.
• Categorical Information: Breadcrumbs data provide context on the product's placement within ZARA's website structure, helping to analyze navigation and product hierarchy.
• Geographical Focus: Specific to the UK market, making it relevant for studies on British fashion retail and consumer trends.
• Availability Status: Includes real-time availability data, which is crucial for understanding stock levels, popular products, and restocking practices.
• Unique Identifiers: Each product is tagged with a uniq_id, ensuring data integrity and making it easier to track and analyze over time.
• Data Collection Timestamp: The scraped_at field records the exact time and date when the data was collected, aiding in time-based analysis of inventory and pricing.

Potential Use Cases:

• Market Research: Analyze UK and European fashion trends, consumer preferences, and competitive positioning within the fast fashion sector.
• E-commerce Analysis: Study ZARA's product placement, pricing, and availability to optimize online retail strategies.
• Stock Management: Use SKU and availability data to predict inventory needs and enhance supply chain efficiency.
• Brand Analysis: Examine the impact of brand identity on consumer choices and product performance in the UK market.
• Academic Research: Ideal for research projects focused on fashion retail, marketing strategies, and consumer behavior in Europe.

Data Sources:

The data is meticulously collected from ZARA's official UK website and other reliable retail databases, reflecting the latest product offerings and market dynamics specific to the UK and European fashion markets.

• ZARA US Retail Products Dataset: Explore over 10,000 product records from ZARA's USA online store, including titles, prices, images, and availability.
  
  
• Fashion Products Dataset from GAP.com: Access detailed product information from GAP's online store, featuring over 4,500 fashion items with attributes like price, brand, color, reviews, and images.
  
  
• Myntra Fashion Products Dataset: A comprehensive dataset from Myntra.com, offering over 12,000 fashion products with detailed attributes for in-depth analysis.

Context

Company ABC has a few thousand orders via their website on a daily basis and they have to deliver them as fast as they can. For delivering the goods ordered by the customers, ABC has tied up with multiple courier companies in India who charge them some amount per delivery.

The charges are dependent upon two factors: - Weight of the product - Distance between the warehouse (pickup location) and customer’s delivery address (destination location)

On average, the delivery charges are Rs. 100 per shipment. So if ABC ships 1,00,000 orders per month, they have to pay approximately Rs. 1 crore to the courier companies on a monthly basis as charges.

As the amount that ABC has to pay to the courier companies is very high, they want to verify if the charges levied by their partners per Order are correct.

Content

ABC’s internal data spread across three reports: 1. Website order report which will list Order IDs and various products (SKUs) part of each order. Order ID is a common identifier between ABC’s order report and courier company invoice. 2. SKU master with the gross weight of each product. This should be used to calculate the total weight of each order and during analysis compare it against one reported by the courier company in their CSV invoice per Order ID. The courier company calculates weight in slabs of 0.5 KG multiples, so first you have to figure out the total weight of the shipment and then figure out applicable weight slabs. For example: - If the total weight is 400 grams then the weight slab should be 0.5 - If the total weight is 950 grams then the weight slab should be 1. - If the total weight is 1 KG then the weight slab should be 1 - If the total weight is 2.2 KG then the weight slab should be 2.5

1. Warehouse Pincode to All India Pincode mapping (this should be used to figure out delivery zone (a/b/c/d/e) and during analysis compare against one reported by courier company in their CSV invoice per Order ID.

Courier company invoice in CSV file: - Invoice in CSV file mentioning AWB Number (courier company’s own internal ID), Order ID (company ABC’s order ID), the weight of the shipment, warehouse pickup Pincode, customer delivery Pincode, zone of delivery, charges per shipment, type of shipment - Courier charges rate card at weight slab and Pincode level. If the invoice mentions “Forward charges” then only forward charges (“fwd”) should be applicable as per the zone and fixed & additional weights based on weight slabs. - If the invoice mentions “Forward and RTO charges” then forward charges (“fwd”) and RTO charges (“RTO”) should be applicable as per the zone and fixed & additional weights based on weight slabs. - For the first 0.5 KG, a “fixed” rate as per the slab is applicable. - For each additional 0.5 KG, “additional” weight in the same proportion is applicable. Total charges will be “fixed” + “total additional” if any.

Ecommerce Inventory Management Software Market Outlook

In 2023, the global ecommerce inventory management software market size was valued at approximately USD 2.5 billion and is forecasted to reach USD 8.9 billion by 2032, growing at a compound annual growth rate (CAGR) of 15%. The rapid growth of ecommerce businesses and the increasing need for efficient inventory management solutions are key factors driving this market's expansion.

The ecommerce inventory management software market is poised for robust growth due to several compelling factors. Firstly, the surge in ecommerce activities, spurred by increased internet penetration and digital literacy, has exponentially driven the demand for sophisticated inventory management solutions. Businesses are under heightened pressure to maintain accurate inventory records to meet customer expectations and manage supply chain complexities efficiently. The integration of advanced technologies like artificial intelligence (AI) and machine learning (ML) into inventory management systems enhances forecasting accuracy, thereby reducing instances of overstocking and stockouts. These technological advancements are crucial in supporting real-time inventory tracking and demand forecasting, significantly contributing to the market's growth.

Secondly, the trend towards omnichannel retailing has necessitated the adoption of advanced inventory management systems. Omnichannel strategies require seamless integration of inventory data across physical stores, online platforms, and distribution centers to provide a unified shopping experience. As businesses adopt these strategies, the need for robust inventory management software that can synchronize data across various channels becomes paramount. This shift not only optimizes inventory levels but also improves operational efficiency and customer satisfaction, driving the market further.

Moreover, the increasing adoption of cloud-based solutions is another significant growth factor for the ecommerce inventory management software market. Cloud-based inventory management systems offer several advantages, including scalability, cost-effectiveness, and accessibility from any location. These features are particularly beneficial for small and medium-sized enterprises (SMEs) that may not have the resources to invest in extensive on-premises infrastructure. The flexibility offered by cloud solutions allows businesses to scale their operations as needed, thereby supporting growth and adaptation in a rapidly changing market environment.

The role of Apparel Inventory Software in the ecommerce landscape cannot be overstated. As fashion retailers increasingly move online, they face unique challenges in managing vast inventories of apparel items, each with multiple variations in size, color, and style. Apparel Inventory Software provides tailored solutions to these challenges by offering features such as SKU management, size and color matrix, and automated reordering. These capabilities ensure that retailers can maintain optimal stock levels, reduce excess inventory, and improve customer satisfaction by ensuring that popular items are always available. As the ecommerce apparel market continues to grow, the demand for specialized inventory management solutions is expected to rise, further driving the market's expansion.

From a regional perspective, North America and Asia Pacific are anticipated to hold significant shares in the market. North America's dominance can be attributed to the presence of numerous ecommerce giants and the early adoption of advanced technologies in the region. In contrast, Asia Pacific's rapid growth is driven by the burgeoning ecommerce sector, particularly in countries like China and India, where digital commerce continues to flourish. These regions not only lead in market share but also exhibit promising growth trajectories, making them focal points for market players.

Component Analysis

The ecommerce inventory management software market is segmented by component into software and services. The software component dominates the market, as it encompasses the primary solutions used for inventory management. This segment includes various types of software, such as standalone inventory management systems and integrated enterprise resource planning (ERP) systems with inventory management modules. The robust demand for comprehensive software solutions that offer real-time inventory tracking, automated reorder points, and detailed analyt

Dataweave's assortment intelligence solution helps retailers in planning a more curated and relevant product mix. This helps in driving more sales from new customers and in attracting a new customer base. Retailers can understand the strengths and gaps of their catalog at a category, sub-category, product type, and brand level.

Here are a few use cases for this project:

1. Consumer Goods Classification: The VendiGo v1.3 model can be used in retail inventory management systems to automatically classify different kinds of consumer goods based on the SKU number. It can identify different variations of a product easily, reducing manual labor associated with such tasks.

2. Automated Checkout Systems: The model can be integrated into self-checkout stations in supermarkets or convenience stores. When customers scan their items, the system can identify the product class, ensuring a faster checkout process.

3. Smart Vending Machines: Implementing this model in vending machines would allow the machines to identify and keep track of the stocked items. Knowing what is inside each time can help maintain inventory levels automatically.

4. Recycling Sorting Systems: The model can also be used in waste sorting machines to classify different types of plastic waste based on the SKU product classes, assisting in efficient recycling processes.

5. E-Commerce Image Tagging: Online retailers could use this model for automatic image tagging. When a new product image is uploaded, it could identify the product class and generate appropriate metadata, improving search accuracy.

Measuring a brand’s success in entering China: Previously when a brand entered China, it required significant investment in distribution and marketing. These days, a brand can just launch their products on Taobao to test the market. Tracking the impact of special events: If China consumers turn against a company, the Taobao dataset will allow you to see the impact this has on consumer demand. The recent COVID-19 crisis is a good example. The Taobao dataset can allow you to track in near real-time the extent to which consumer demand has fallen and when consumers start buying goods again. Competitive analysis: You can use it to compare Nike vs Adidas vs Under Armour by category. Tracking general consumer preferences e.g., Blackmore and A2 used to only sell in Australia. Once they entered China, sales soared, their share prices went up by 300-400%