Customer360Insights

The Customer360Insights dataset is a synthetic collection meticulously designed to mirror the multifaceted nature of customer interactions within an e-commerce platform. It encompasses a wide array of variables, each serving as a pillar to support various analytical explorations. Here’s a breakdown of the dataset and the potential analyses it enables:

Dataset Description

• Customer Demographics: Includes FullName, Gender, Age, CreditScore, and MonthlyIncome. These variables provide a demographic snapshot of the customer base, allowing for segmentation and targeted marketing analysis.
• Geographical Data: Comprising Country, State, and City, this section facilitates location-based analytics, market penetration studies, and regional sales performance.
• Product Information: Details like Category, Product, Cost, and Price enable product trend analysis, profitability assessment, and inventory optimization.
• Transactional Data: Captures the customer journey through SessionStart, CartAdditionTime, OrderConfirmation, OrderConfirmationTime, PaymentMethod, and SessionEnd. This rich temporal data can be used for funnel analysis, conversion rate optimization, and customer behavior modeling.
• Post-Purchase Details: With OrderReturn and ReturnReason, analysts can delve into return rate calculations, post-purchase satisfaction, and quality control.

Types of Analysis

• Descriptive Analytics: Understand basic metrics like average monthly income, most common product categories, and typical credit scores.
• Predictive Analytics: Use machine learning to predict credit risk or the likelihood of a purchase based on demographics and session activity.
• Customer Segmentation: Group customers by demographics or purchasing behavior to tailor marketing strategies.
• Geospatial Analysis: Examine sales distribution across different regions and optimize logistics. Time Series Analysis: Study the seasonality of purchases and session activities over time.
• Funnel Analysis: Evaluate the customer journey from session start to order confirmation and identify drop-off points.
• Cohort Analysis: Track customer cohorts over time to understand retention and repeat purchase patterns.
• Market Basket Analysis: Discover product affinities and develop cross-selling strategies.

This dataset is a playground for data enthusiasts to practice cleaning, transforming, visualizing, and modeling data. Whether you’re conducting A/B testing for marketing campaigns, forecasting sales, or building customer profiles, Customer360Insights offers a rich, realistic dataset for honing your data science skills.

Curious about how I created the data? Feel free to click here and take a peek! 😉

📊🔍 Good Luck and Happy Analysing 🔍📊

Amazon Products dataset to explore detailed product listings, pricing, reviews, and sales data. Popular use cases include competitive analysis, market trend forecasting, and e-commerce strategy optimization.

Use our Amazon Products dataset to explore detailed information on products across various categories, including pricing, reviews, ratings, and sales data. This dataset is ideal for e-commerce professionals, market analysts, and product managers looking to analyze market trends, optimize product listings, and refine competitive strategies.

Leverage this dataset to track pricing trends, assess customer feedback, and uncover popular product categories. Whether you're conducting competitive analysis, performing market research, or optimizing product strategies, the Amazon Products dataset provides key insights to stay ahead in the e-commerce landscape.

Dataset Features

• Title: The name or title of the product.
• seller_name: The name of the seller offering the product.
• Brand: The brand associated with the product.
• Description: A detailed description of the product, including key features.
• initial_price: The original price of the product before any discounts.
• final_price: The current price of the product after discounts.
• Currency: The currency in which the product is priced (e.g., GBP, USD).
• Availability: The stock status (e.g., in stock, out of stock).
• reviews_count: The total number of customer reviews.
• Categories: The specific category the product belongs to.
• asin: Amazon Standard Identification Number.
• buybox_seller: The seller currently winning the Amazon Buy Box.
• number_of_sellers: The number of sellers offering this product.
• root_bs_rank: The overall ranking of the product in the Amazon best-sellers list.
• answered_questions: The number of questions answered in the product Q&A section.
• domain: The website domain where the product is being sold.
• images_count: The number of images available for the product.
• URL: The link to the product page on Amazon.
• video_count: The number of videos available for the product.
• image_url: The URL of the primary image associated with the product.
• item_weight: The weight of the product.
• Rating: The average rating of the product based on customer reviews.
• product_dimensions: The dimensions of the product (e.g., length, width, height) and weight.
• seller_id: The unique identifier for the seller.
• date_first_available: The date when the product was first made available on Amazon.
• discount: Any discount applied to the product.
• model_number: The model number of the product.
• manufacturer: The company that manufactures the product.
• department: The department under which the product is categorized (e.g., Health & Household).
• plus_content: A flag indicating if the product has Amazon’s “Plus Content” (additional marketing content).
• upc: The Universal Product Code (UPC) associated with the product.
• video: URL(s) of any video content associated with the product.
• top_review: A summary or excerpt from the top customer review.
• variations: Different product variations (e.g., different sizes or flavors).
• delivery: Information on the delivery options (e.g., free delivery or Prime delivery).
• features: Key features or highlights of the product.
• format: The format of the product (e.g., powder, liquid).
• buybox_prices: Pricing details for the product, including the base and tiered prices.
• parent_asin: The ASIN of the parent product (if the product is part of a larger group of similar products).
• input_asin: The ASIN of the product as input for Amazon searches.
• ingredients: List of ingredients in the product (if applicable).
• origin_url: The source URL for product-related information or ingredients.
• bought_past_month: A flag indicating if the product was bought in the past month.
• is_available: Availability status of the product (True/False).
• root_bs_category: The broad product category (e.g., Health & Household).
• bs_category: The specific subcategory the product belongs to.
• bs_rank: The rank of the product in its specific subcategory.
• badge: Any badge or label the product has earned (e.g., Amazon's Choice).
• subcategory_rank: The rank of the product within its subcategory.
• amazon_choice: A flag indicating if the product has been selected as Amazon’s Choice.
• images: A list of URLs for additional product images.
• product_details: Detailed product specifications and features.
• prices_breakdown: A breakdown of the price, including any discounts or promotions.
• country_of_origin: The country where the product is made.
• from_the_brand: Information from the brand or manufact

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!

This dataset was created to simulate a market basket dataset, providing insights into customer purchasing behavior and store operations. The dataset facilitates market basket analysis, customer segmentation, and other retail analytics tasks. Here's more information about the context and inspiration behind this dataset:

Context:

Retail businesses, from supermarkets to convenience stores, are constantly seeking ways to better understand their customers and improve their operations. Market basket analysis, a technique used in retail analytics, explores customer purchase patterns to uncover associations between products, identify trends, and optimize pricing and promotions. Customer segmentation allows businesses to tailor their offerings to specific groups, enhancing the customer experience.

Inspiration:

The inspiration for this dataset comes from the need for accessible and customizable market basket datasets. While real-world retail data is sensitive and often restricted, synthetic datasets offer a safe and versatile alternative. Researchers, data scientists, and analysts can use this dataset to develop and test algorithms, models, and analytical tools.

Dataset Information:

The columns provide information about the transactions, customers, products, and purchasing behavior, making the dataset suitable for various analyses, including market basket analysis and customer segmentation. Here's a brief explanation of each column in the Dataset:

• Transaction_ID: A unique identifier for each transaction, represented as a 10-digit number. This column is used to uniquely identify each purchase.
• Date: The date and time when the transaction occurred. It records the timestamp of each purchase.
• Customer_Name: The name of the customer who made the purchase. It provides information about the customer's identity.
• Product: A list of products purchased in the transaction. It includes the names of the products bought.
• Total_Items: The total number of items purchased in the transaction. It represents the quantity of products bought.
• Total_Cost: The total cost of the purchase, in currency. It represents the financial value of the transaction.
• Payment_Method: The method used for payment in the transaction, such as credit card, debit card, cash, or mobile payment.
• City: The city where the purchase took place. It indicates the location of the transaction.
• Store_Type: The type of store where the purchase was made, such as a supermarket, convenience store, department store, etc.
• Discount_Applied: A binary indicator (True/False) representing whether a discount was applied to the transaction.
• Customer_Category: A category representing the customer's background or age group.
• Season: The season in which the purchase occurred, such as spring, summer, fall, or winter.
• Promotion: The type of promotion applied to the transaction, such as "None," "BOGO (Buy One Get One)," or "Discount on Selected Items."

Use Cases:

• Market Basket Analysis: Discover associations between products and uncover buying patterns.
• Customer Segmentation: Group customers based on purchasing behavior.
• Pricing Optimization: Optimize pricing strategies and identify opportunities for discounts and promotions.
• Retail Analytics: Analyze store performance and customer trends.

Note: This dataset is entirely synthetic and was generated using the Python Faker library, which means it doesn't contain real customer data. It's designed for educational and research purposes.

Tabulating and Visualizing Supermarket Data

In this portfolio, I present an analysis of supermarket data, focusing on total sales, product categories, highest-spending customers, states with the highest and lowest sales, top-selling regions, and the most profitable city. This analysis provides valuable insights into supermarket performance and customer behavior.

Total Sales:

This chart illustrates the total sales over a specific time period. It serves as a key indicator of the supermarket's financial performance, showing revenue trends.

Product Categories:

A pie chart displays the distribution of sales across various product categories. It helps identify which product categories are the most popular and which may require additional marketing efforts.

Highest-Spending Customer:

The bar chart reveals the highest-spending customer, allowing the supermarket to recognize and reward loyal customers, while also gaining insights into their preferences.

States with the Highest Sales:

A map or bar chart showcases the states with the highest sales. This data can inform inventory management and marketing strategies.

Top-Selling Regions:

A bar chart displays the regions that generate the most sales, enabling the supermarket to concentrate resources where they are most effective.

Most Profitable City:

The pie chart reveals the city with the highest sales, providing insights into localized market dynamics.

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Power BI:

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The Local Food Marketing Practices Survey (LFMPS) is a dataset created by the U.S. Department of Agriculture's National Agricultural Statistics Service (NASS) to track marketing practices of farms selling locally or regionally produced agricultural food products. First conducted in 2015 and updated in 2020 as part of the Census of Agriculture, it provides benchmark data on direct-to-consumer and direct-to-intermediate-market sales, including revenue, channels (e.g., farmers' markets, CSAs, on-farm sales), and operational characteristics. Key features include exclusion of farms not engaged in local sales, detailed breakdowns of sales by state (e.g., California accounted for the largest share of direct sales in 2020), and insights into trends like the dominance of direct-to-consumer marketing (77% of operations in 2020). The dataset supports policy development, academic research, and industry analysis by quantifying the economic impact of local food systems. For example, in 2020, 147,307 operations generated $9.0 billion through direct marketing. Unique aspects include its focus on branded regional products and granular data on sales distribution (e.g., direct-to-consumer sales constituted 33% of total direct sales despite being the most common channel). Data is accessible via the NASS Quick Stats database.

Our dataset features comprehensive housing market data, extracted from 250,000 records sourced directly from Redfin USA. Our Crawl Feeds team utilized proprietary in-house tools to meticulously scrape and compile this valuable data.

Key Benefits of Our Housing Market Data:

• In-Depth Market Analysis: Gain insights into the real estate market with up-to-date data on recently sold properties.
  
  
• Price Trend Identification: Track and analyze price trends across different cities.
  
  
• Accurate Price Estimation: Estimate property values based on key factors such as area, number of beds and baths, square footage, and more.
  
  
• Detailed Real Estate Statistics: Access detailed statistics segmented by zip code, area, and state.

Unlock the Power of Redfin Data for Real Estate Professionals

Leveraging our Redfin properties dataset allows real estate professionals to make data-driven decisions. With detailed insights into property listings, sales history, and pricing trends, agents and investors can identify opportunities in the market more effectively. The data is particularly useful for comparing neighborhood trends, understanding market demand, and making informed investment decisions.

Enhance Your Real Estate Research with Custom Filters and Analysis

Our Redfin dataset is not only extensive but also customizable, allowing users to apply filters based on specific criteria such as property type, listing status, and geographic location. This flexibility enables researchers and analysts to drill down into the data, uncovering patterns and insights that can guide strategic planning and market entry decisions. Whether you're tracking the performance of single-family homes or exploring multi-family property trends, this dataset offers the depth and accuracy needed for thorough analysis.

Looking for deeper insights or a custom data pull from Redfin?
Send a request with just one click and explore detailed property listings, price trends, and housing data.
🔗 Request Redfin Real Estate Data

With the phone book era far in the past, database and directory publishers have been forced to transform their business approach, focusing on their digital presence. Despite many publishers rapidly moving away from print services, they are experiencing immovable competition from online search engines and social media platforms within the digital space, negatively affecting revenue growth potential. Industry revenue has been eroding at a CAGR of 4.4% over the past five years and in 2024, a 3.9% drop has led to the industry revenue totaling $4.4 billion. Profit continues to drop in line with revenue, accounting for 4.7% of revenue as publishers invest more in their digital platforms. Interest in printed directories has disappeared as institutional clients and consumers have continued their shift to convenient online resources. Declining demand for print advertising has curbed revenue growth and online revenue has only slightly mitigated this downturn. Though many traditional publishers, such as Yellow Pages, now operate under parent companies with digital resources, directory publishers remain low on the list of options businesses have to choose from in digital advertising. Due to the convenience and connectivity that Facebook and Google services offer, traditional directory publishers have a limited ability to compete. Many providers have rebranded and tailored their services toward client needs, though these efforts have only had a marginal impact on revenue growth. The industry is forecast to decline at an accelerated CAGR of 5.2% over the next five years, reaching an estimated $3.4 billion in 2029, as businesses and consumers continually turn to digital alternatives for information and advertising opportunities. As AI and digital technology innovation expands, social media company products will likely improve at a faster rate than the digital offerings that directory publishers can provide. Though these companies will seek external partnerships to cut costs, they face an uphill battle to boost their visibility and reverse consumer habit trends.

The survey interviewed 254 retailer shops in 10 sub-cities of Addis Ababa. 30 supermarkets, 20 mini-markets, 100 regular shops, 80 dairy shops and 24 open market shops selling dairy products were interviewed. Details of the sampling strategy is found in the attachment. The survey collected information on the characteristics of the shop, details of dairy products sold, prices and quality. Policy makers, research, and other stakeholders can use this data to analyses dairy value chain in Ethiopia and dairy retailing practices in Ethiopia. This data set was collected through research of the project “Improving the evidence and policies for better performing livestock systems in Ethiopia” lead by the International Food Policy Research Institution as part of the Feed the Future Innovation Lab for Livestock Systems.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Cloudflare (NET) Navigates the Web of Growth

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

1) Data Introduction • The Best-Selling Steam Games of All Time Dataset contains 2,380 of the world's best-selling Steam games, collected from the 'Best sellers' page of the Steam official store as of June 1, 2025.

2) Data Utilization (1) Best-Selling Steam Games of All Time Dataset has characteristics that: • This dataset incorporates data collected from three locations: Steam, GameFAQs, and SteamDB, and consists of 15 columns: game name, review rating, release date, developer, genre tag, support operating system, support language, price, play support, age limit, user rating, difficulty, play time, and expected downloads. • Each game's genre and tag has been consistently refined using only 42 standardized representative tags, and all data is completely available with no missing values. (2) Best-Selling Steam Games of All Time Dataset can be used to: • Game Market Analysis: Based on various information such as sales volume, price, genre, user evaluation, support platform, etc., it can be used to analyze trends, popular genres, price policies, and success factors in the Steam game market. • Recommendation system and game development: You can build a personalized game recommendation system using various characteristics such as user rating, tag, difficulty, and play time, or use it as benchmarking data when planning new games.

Massive eBay Marketplace Data Collection for E-commerce Intelligence

Unlock the power of online marketplace analytics with our comprehensive eBay products dataset. This premium collection contains 1.29 million products from eBay's global marketplace, providing extensive insights into one of the world's largest e-commerce platforms. Perfect for competitive analysis, pricing strategies, market research, and machine learning applications in e-commerce.

Dataset Overview

• Total Products: 1,290,000+ marketplace listings
• Source: eBay Global Marketplace
• Format: CSV, ZIP compressed
• File Size: Optimized compressed format
• Coverage: Multi-category product listings across eBay

Complete Data Fields Included

Product Identification

• id: Unique eBay product identifiers
• name: Complete product titles and names
• url: Direct eBay listing page links
• epid: eBay Product ID for catalog matching
• source: Data source identification

Product Details

• raw_product_description: Original unprocessed product descriptions
• product_description: Cleaned and formatted product descriptions
• brand: Brand names and manufacturer information
• mpn: Manufacturer Part Numbers
• gtin13: Global Trade Item Numbers (barcodes)

Pricing and Availability

• price: Current listing prices
• currency: Currency information for international listings
• in_stock: Stock availability status
• breadcrumbs: Category navigation paths

Visual and Technical Data

• images: Product image URLs and references
• crawled_at: Data collection timestamps

Key Use Cases

E-commerce Market Research

• Analyze eBay marketplace trends and patterns
• Study product category performance
• Monitor pricing strategies across sellers
• Identify high-demand product categories

Competitive Intelligence

• Benchmark pricing against eBay marketplace
• Analyze product positioning strategies
• Study seller competition and market share
• Monitor inventory levels and availability

Price Optimization

• Develop dynamic pricing algorithms
• Analyze price elasticity across categories
• Compare marketplace pricing trends
• Optimize listing prices for maximum visibility

Machine Learning Applications

• Train recommendation systems
• Develop price prediction models
• Create product categorization algorithms
• Build inventory forecasting systems

Target Industries

E-commerce Retailers

• Marketplace Strategy: Optimize eBay selling strategies
• Pricing Intelligence: Competitive price monitoring
• Product Research: Identify profitable product opportunities
• Inventory Planning: Demand forecasting and stock optimization

Technology Companies

• AI Training Data: Machine learning model development
• Analytics Platforms: E-commerce intelligence tools
• Price Comparison: Marketplace comparison services
• Search Enhancement: Product discovery optimization

Market Research Firms

• Industry Reports: E-commerce marketplace analysis
• Consumer Behavior: Online shopping pattern studies
• Brand Monitoring: Brand performance tracking
• Trend Analysis: Market trend identification

Academic Research

• E-commerce Studies: Online marketplace research
• Business Intelligence: Retail analytics case studies
• Data Science Projects: Large-scale dataset analysis
• Economic Research: Digital marketplace economics

Data Quality Features

• Comprehensive Coverage: 1.29 million unique products
• Rich Metadata: Complete product information included
• Validated Data: Quality-checked and processed
• Structured Format: Ready-to-use CSV format
• Global Scope: International marketplace coverage

Abstract

Syngenta is committed to increasing crop productivity and to using limited resources such as land, water and inputs more efficiently. Since 2014, Syngenta has been measuring trends in agricultural input efficiency on a global network of real farms. The Good Growth Plan dataset shows aggregated productivity and resource efficiency indicators by harvest year. The data has been collected from more than 4,000 farms and covers more than 20 different crops in 46 countries. The data (except USA data and for Barley in UK, Germany, Poland, Czech Republic, France and Spain) was collected, consolidated and reported by Kynetec (previously Market Probe), an independent market research agency. It can be used as benchmarks for crop yield and input efficiency.

Geographic coverage

National coverage

Analysis unit

Agricultural holdings

Kind of data

Sample survey data [ssd]

Sampling procedure

A. Sample design Farms are grouped in clusters, which represent a crop grown in an area with homogenous agro- ecological conditions and include comparable types of farms. The sample includes reference and benchmark farms. The reference farms were selected by Syngenta and the benchmark farms were randomly selected by Kynetec within the same cluster.

B. Sample size Sample sizes for each cluster are determined with the aim to measure statistically significant increases in crop efficiency over time. This is done by Kynetec based on target productivity increases and assumptions regarding the variability of farm metrics in each cluster. The smaller the expected increase, the larger the sample size needed to measure significant differences over time. Variability within clusters is assumed based on public research and expert opinion. In addition, growers are also grouped in clusters as a means of keeping variances under control, as well as distinguishing between growers in terms of crop size, region and technological level. A minimum sample size of 20 interviews per cluster is needed. The minimum number of reference farms is 5 of 20. The optimal number of reference farms is 10 of 20 (balanced sample).

C. Selection procedure The respondents were picked randomly using a “quota based random sampling” procedure. Growers were first randomly selected and then checked if they complied with the quotas for crops, region, farm size etc. To avoid clustering high number of interviews at one sampling point, interviewers were instructed to do a maximum of 5 interviews in one village.

BF Screened from Indonesia were selected based on the following criterion: (a) Corn growers in East Java - Location: East Java (Kediri and Probolinggo) and Aceh
- Innovative (early adopter); Progressive (keen to learn about agronomy and pests; willing to try new technology); Loyal (loyal to technology that can help them)
- making of technical drain (having irrigation system)
- marketing network for corn: post-harvest access to market (generally they sell 80% of their harvest)
- mid-tier (sub-optimal CP/SE use)
- influenced by fellow farmers and retailers
- may need longer credit

(b) Rice growers in West and East Java - Location: West Java (Tasikmalaya), East Java (Kediri), Central Java (Blora, Cilacap, Kebumen), South Lampung
- The growers are progressive (keen to learn about agronomy and pests; willing to try new technology)
- Accustomed in using farming equipment and pesticide. (keen to learn about agronomy and pests; willing to try new technology) - A long rice cultivating experience in his area (lots of experience in cultivating rice)
- willing to move forward in order to increase his productivity (same as progressive)
- have a soil that broad enough for the upcoming project
- have influence in his group (ability to influence others) - mid-tier (sub-optimal CP/SE use)
- may need longer credit

Mode of data collection

Face-to-face [f2f]

Research instrument

Data collection tool for 2019 covered the following information:

(A) PRE- HARVEST INFORMATION

PART I: Screening PART II: Contact Information PART III: Farm Characteristics a. Biodiversity conservation b. Soil conservation c. Soil erosion d. Description of growing area e. Training on crop cultivation and safety measures PART IV: Farming Practices - Before Harvest a. Planting and fruit development - Field crops b. Planting and fruit development - Tree crops c. Planting and fruit development - Sugarcane d. Planting and fruit development - Cauliflower e. Seed treatment

(B) HARVEST INFORMATION

PART V: Farming Practices - After Harvest a. Fertilizer usage b. Crop protection products c. Harvest timing & quality per crop - Field crops d. Harvest timing & quality per crop - Tree crops e. Harvest timing & quality per crop - Sugarcane f. Harvest timing & quality per crop - Banana g. After harvest PART VI - Other inputs - After Harvest a. Input costs b. Abiotic stress c. Irrigation

See all questionnaires in external materials tab

Cleaning operations

Data processing:

Kynetec uses SPSS (Statistical Package for the Social Sciences) for data entry, cleaning, analysis, and reporting. After collection, the farm data is entered into a local database, reviewed, and quality-checked by the local Kynetec agency. In the case of missing values or inconsistencies, farmers are re-contacted. In some cases, grower data is verified with local experts (e.g. retailers) to ensure data accuracy and validity. After country-level cleaning, the farm-level data is submitted to the global Kynetec headquarters for processing. In the case of missing values or inconsistences, the local Kynetec office was re-contacted to clarify and solve issues.

Quality assurance Various consistency checks and internal controls are implemented throughout the entire data collection and reporting process in order to ensure unbiased, high quality data.

• Screening: Each grower is screened and selected by Kynetec based on cluster-specific criteria to ensure a comparable group of growers within each cluster. This helps keeping variability low.

• Evaluation of the questionnaire: The questionnaire aligns with the global objective of the project and is adapted to the local context (e.g. interviewers and growers should understand what is asked). Each year the questionnaire is evaluated based on several criteria, and updated where needed.

• Briefing of interviewers: Each year, local interviewers - familiar with the local context of farming -are thoroughly briefed to fully comprehend the questionnaire to obtain unbiased, accurate answers from respondents.

• Cross-validation of the answers: o Kynetec captures all growers' responses through a digital data-entry tool. Various logical and consistency checks are automated in this tool (e.g. total crop size in hectares cannot be larger than farm size) o Kynetec cross validates the answers of the growers in three different ways: 1. Within the grower (check if growers respond consistently during the interview) 2. Across years (check if growers respond consistently throughout the years) 3. Within cluster (compare a grower's responses with those of others in the group) o All the above mentioned inconsistencies are followed up by contacting the growers and asking them to verify their answers. The data is updated after verification. All updates are tracked.

• Check and discuss evolutions and patterns: Global evolutions are calculated, discussed and reviewed on a monthly basis jointly by Kynetec and Syngenta.

• Sensitivity analysis: sensitivity analysis is conducted to evaluate the global results in terms of outliers, retention rates and overall statistical robustness. The results of the sensitivity analysis are discussed jointly by Kynetec and Syngenta.

• It is recommended that users interested in using the administrative level 1 variable in the location dataset use this variable with care and crosscheck it with the postal code variable.

Data appraisal

Due to the above mentioned checks, irregularities in fertilizer usage data were discovered which had to be corrected:

For data collection wave 2014, respondents were asked to give a total estimate of the fertilizer NPK-rates that were applied in the fields. From 2015 onwards, the questionnaire was redesigned to be more precise and obtain data by individual fertilizer products. The new method of measuring fertilizer inputs leads to more accurate results, but also makes a year-on-year comparison difficult. After evaluating several solutions to this problems, 2014 fertilizer usage (NPK input) was re-estimated by calculating a weighted average of fertilizer usage in the following years.

The transition from printed databases and directories to online formats has left the Canadian Database and Directory Publishing industry reeling, with revenue decreasing over the five years to 2023 because of this transition and COVID-19. From the advertisers' perspective, marketing costs are allocated to the media channels that most accurately reflect consumer behaviour. As more consumers shift to digital directory and database substitutes, demand for print advertisements, previously the industry's largest revenue source and profit indicator, has declined. Over the five years to 2023, the number of consumers with smartphones, which come with online directory capabilities via their ability to connect to the internet, has risen alongside internet connectivity. This has coincided with declining demand for industry products. Consequently, industry revenue has been declining an annualized 10.2% over the past five years, and is expected to reach $1.4 billion in 2023. This includes a decrease of 3.6% in 2023 alone.The industry has historically been dominated by several players, mainly telephone companies with access to consumer and business contact information. As the industry has contracted, companies have spun off their directory divisions. This was exemplified by the industry-defining event of Bell Canada handing off what would become Yellow Media Limited to KKR & Co. Inc. and the Ontario Teachers' Pension Plan Board. Over the past five years, this trend has continued, with companies selling off their failing segments to larger companies. The purchasing companies have used merger and acquisition activities to diversify their service and product offerings, entering various third-party fields, including market research, data processing and analytics, and database management.Over the five years to 2028, the industry will likely continue its downward spiral. During this period, total advertising expenditure is expected to rise. However, total print advertisement expenditure will likely decline as a share of total spending. The use of print advertisements will likely continue to become obsolete over the next five years. The most significant contributing factor to this decline is expected to be the growing use of digital advertisements. Consequently, IBISWorld forecasts industry revenue will decrease an annualized 3.4% to $1.2 billion over the five years to 2028.

This synthetic yet realistic dataset offers insights into smartphone features, customer reviews, and sales data. It includes over 90 customer reviews for six popular smartphone models from leading brands such as Apple, Samsung, and Google. The dataset is designed to help understand how various product specifications influence purchasing decisions and overall customer satisfaction. It combines detailed product specifications, customer star ratings, review texts, and verified purchase status with estimated sales figures per model.

Columns

• model_id (Integer): A unique identifier for each distinct phone model.
• brand (String): The manufacturer of the phone (e.g., "Apple", "Samsung", "Google").
• model_name (String): The specific name of the phone model (e.g., "iPhone 15").
• price (Integer): The retail price of the phone in USD.
• screen_size (Float): The diagonal screen size of the phone in inches.
• battery (Integer): The battery capacity of the phone in mAh.
• camera_main (String): The resolution of the phone's main camera (e.g., "48MP").
• ram (Integer): The amount of RAM (Random Access Memory) in GB.
• storage (Integer): The internal storage capacity in GB.
• has_5g (Boolean): Indicates whether the phone model supports 5G connectivity (TRUE/FALSE).
• water_resistant (String): The water resistance rating, if any (e.g., "IP68" or "None").
• units_sold (Integer): An estimated number of units sold for market analysis purposes.
• review_id (Integer): A unique identifier for each customer review.
• user_name (String): A randomly generated name for the reviewer.
• star_rating (Integer): The customer's rating, ranging from 1 (worst) to 5 (best).
• verified_purchase (Boolean): Indicates whether the reviewer's purchase was verified (TRUE/FALSE).
• review_date (Date): The date when the review was submitted, in YYYY-MM-DD format (e.g., "2023-05-10").
• review_text (String): Simulated text of the customer's review, based on features and rating (e.g., "The 48MP camera is amazing!").

Distribution

The dataset is typically provided in a CSV file format. It comprises over 90 customer review records, along with corresponding smartphone product specifications and sales data for 6 distinct phone models. The exact total number of rows or the specific file size in MB/GB is not specified.

Usage

This dataset is ideal for various analytical applications, including: * Feature importance analysis: Determining which smartphone specifications (e.g., battery life, camera quality) most significantly influence customer ratings and purchasing decisions. * Sentiment analysis: Applying Natural Language Processing (NLP) techniques to extract insights and sentiment from customer review texts. * Pricing strategy optimisation: Analysing the correlation between price and customer satisfaction or sales volume. * Market research: Comparing performance and customer perception across different brands (e.g., Apple vs. Samsung vs. Google) and models. * Sales vs. features correlation: Investigating how product features and pricing impact estimated units sold.

Coverage

This dataset has a Global region coverage. It includes data pertaining to six smartphone models from three major brands: Apple (iPhone 14, iPhone 15), Samsung (Galaxy S22, Galaxy S23), and Google (Pixel 7, Pixel 8). The review dates are indicative of data from around 2023. While it includes customer reviews, specific demographic details of the reviewers are not available beyond randomly generated usernames. As a synthetic dataset, it is designed to be realistic for general market analysis.

License

CC0

Who Can Use It

This dataset is suitable for: * Data Analysts and Scientists: For performing regression analysis, sentiment analysis, and predictive modelling. * Marketing Professionals: To understand consumer preferences, optimise product features, and refine marketing strategies. * Product Managers: To inform product development, feature prioritisation, and competitive analysis. * Market Researchers: To study market trends, brand comparisons, and consumer behaviour in the smartphone industry. * Academics and Students: For educational purposes and research projects related to consumer electronics, e-commerce, and data analysis.

Dataset Name Suggestions

• Smartphone Customer Satisfaction Data
• Mobile Phone Market & Reviews Dataset
• Consumer Electronics Feature Analysis
• Smartphone Product Performance
• Mobile Device Sales and Reviews

Attributes

Original Data Source: Smartphone Feature Optimization (Marketing Mix)

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**# 📌 Dataset Description for Kaggle: Amazon Best Sellers Data

**📖 Overview

This dataset contains real-time Amazon Best Sellers data across multiple countries and categories, specifically focusing on Software products. The data is collected via an API and includes details such as product titles, prices, star ratings, number of reviews, and rank changes.

With this dataset, you can analyze trending products, pricing strategies, and customer preferences across different regions. It is useful for market analysis, competitor research, and e-commerce insights.

📊 Dataset Contents

Each row in this dataset represents a top-selling software product on Amazon for a specific country. The dataset includes the following columns:

product_title 🏷️ – Name of the product product_price 💰 – Price of the product in the respective country’s currency product_star_rating ⭐ – Average star rating of the product product_num_ratings 📝 – Total number of customer reviews rank 🔢 – Current ranking of the product in the Best Sellers list country 🌍 – The country where the ranking is recorded

🎯 Potential Use Cases

✅ E-commerce Market Analysis – Identify top-selling software products in different regions. ✅ Pricing Strategy Optimization – Compare prices across markets and track fluctuations. ✅ Customer Sentiment Analysis – Analyze customer ratings and review trends. ✅ Competitor Research – Understand how products rank in different countries. ✅ Trend Forecasting – Observe rank changes and predict upcoming best-sellers.

Use our Best Buy products to collect ratings, prices, and descriptions about products from an e-commerce online web. You can purchase either the entire dataset or a customized subset, depending on your requirements. The Best Buy Products Dataset stands as a comprehensive resource for businesses, researchers, and analysts aiming to navigate the vast array of products offered by Best Buy, a leading retailer in consumer electronics and technology. Tailored to provide a deep understanding of Best Buy's e-commerce ecosystem, this dataset facilitates market analysis, pricing optimization, customer behavior comprehension, and competitor assessment. At its core, the dataset encompasses essential attributes such as product ID, title, descriptions, ratings, reviews, pricing details, and seller information. These fundamental data elements empower users to glean insights into product performance, customer sentiment, and seller credibility, thereby facilitating informed decision-making processes. Whether you're a retailer looking to enhance your product portfolio, a researcher investigating trends in consumer electronics, or an analyst seeking to refine e-commerce strategies, the Best Buy Products Dataset offers a valuable resource for uncovering opportunities and driving success in the ever-evolving landscape of retail.

This dataset contains data on all Real Property parcels that have sold since 2013 in Allegheny County, PA.

Before doing any market analysis on property sales, check the sales validation codes. Many property "sales" are not considered a valid representation of the true market value of the property. For example, when multiple lots are together on one deed with one price they are generally coded as invalid ("H") because the sale price for each parcel ID number indicates the total price paid for a group of parcels, not just for one parcel. See the Sales Validation Codes Dictionary for a complete explanation of valid and invalid sale codes.

Sales Transactions Disclaimer: Sales information is provided from the Allegheny County Department of Administrative Services, Real Estate Division. Content and validation codes are subject to change. Please review the Data Dictionary for details on included fields before each use. Property owners are not required by law to record a deed at the time of sale. Consequently the assessment system may not contain a complete sales history for every property and every sale. You may do a deed search at http://www.alleghenycounty.us/re/index.aspx directly for the most updated information. Note: Ordinance 3478-07 prohibits public access to search assessment records by owner name. It was signed by the Chief Executive in 2007.

Honda Used Car Selling Prices dataset is now fully cleaned to be used for Exploratory Data Analysis. The analysis can be performed in order to check the market trends. This dataset is small but it contains valuable insights in order to understand the car prices and models. Different Machine Learning algorithms can be applied to predict the car prices, like Linear Regression