Difference uses Google Analytics as the Baseline. Results based on Paired t-Test for Hypotheses Supported.

Comparison of definitions of total visits, unique visitors, bounce rate, and session duration conceptually and for the two analytics platforms: Google Analytics and SimilarWeb.

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Considering dataset description

The user wants an improved description of the "online_shoppers_intention" dataset. I'll base the rewrite on metadata like 12,330 sessions, collected over a year, and incorporate details about the dataset’s purpose, data components, and possible uses. I could consider writing it as a cohesive paragraph or bullet points. I'll need to include citations too—let's search for further details on OpenML. Once I find that, I'll craft a clearer, more thorough description.

Thought for 5 seconds

Overview The Online Shoppers Purchasing Intention dataset captures 12,330 distinct web‐session records collected over a one‐year span from an e-commerce site, with each session belonging to a different visitor to prevent user‐ or campaign-specific biases. Originally published in 2017 and licensed under CC BY 4.0, it was curated by Sakar et al. for benchmarking classifiers on independent and identically distributed tabular data.

Features

• Numerical (10):

  • Administrative, Informational, ProductRelated (counts of pages visited) and their corresponding _Duration fields (total time in seconds spent on those pages).
  • BounceRates, ExitRates (average session‐level bounce and exit rates) and PageValues (average monetary value of pages preceding a purchase).
  • SpecialDay (normalized [0 – 1] indicator of how close the visit was to major shopping holidays, e.g. Valentine’s Day).

• Categorical (7):

  • Month (Aug – Sep), OperatingSystems (8 codes), Browser (13 codes), Region (9 codes), TrafficType (20 codes), VisitorType (“New_Visitor,” “Returning_Visitor,” “Other”), and Weekend (True/False).

Target and Class Distribution

• Revenue (False/True) denotes whether the session ended in a purchase.
• Of the 12,330 sessions, 84.5 % (10,422) did not result in revenue, while 15.5 % (1,908) did.

Intended Use This dataset is ideal for developing and comparing binary classification models—ranging from multilayer perceptrons and LSTM networks to tree-based methods—to predict online purchasing intention in a controlled, time-invariant setting.

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Popular Products from NewChic.com E-Commerce Website

Product, Brand, and User Interaction Analytics

By Jeffrey Mvutu Mabilama [source]

About this dataset

This dataset offers an inside look at the popular products of the widely-known e-commerce website NewChic.com. With a snapshot from early August 2020, it provides insights into user tastes, which categories attract buyers, trends in fashion and more!

Using this data, you can gain a better understanding of customer preferences for products and optimize your stock accordingly. You can use it to explore what styles are in trend right now (and how long lasting they are) and find out which niches may be most productive. Moreover, you can segment user tastes based on demographic information such as age groups or geographic locations by analyzing SimilarWeb's traffic data for NewChic.

Are you ready to discover new ways to run your business? Take advantage of this resource now and make smarter decisions about product selection and seasonality – the market is constantly changing so don't miss any opportunity that passes by!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset is an excellent resource for anyone interested in understanding the trends and popularity of products sold by e-commerce website NewChic.com. It contains records of customers’ interactions with products, as well as individual product listings with their associated IDs, prices, discounts, images, brand name, likes count and more. In this guide we explore how to use this data to understand customer behaviour and make informed decisions about the effectiveness of sales and promotional activities.

Exploring the Data: Before diving into the analysis process it is important to understand what you have at your disposal - information that is relevant to customer behaviour on a particular e-commerce website. This dataset provides a comprehensive overview that contains detailed product descriptions as well as popularity metrics such as likes counted from customers who have interacted with them online or offline. Firstly browse through categories from which different types of products have been classified into – apparel & accessories being perhaps some of the most popular ones based on historical purchase behaviour seen in other platforms too; other interesting options could include lifestyle items such as electronic gadgets or art prints among many others. It is also noteworthy to note that the dataset includes details like inventory status (how many pieces remain) which allows us to compare promotional efforts in comparison with inventory size - essential knowledge while deciding how much resources should be allocated towards individual marketing campaigns or discounts etcetera without diluting margins too much over time forcing artificial demand increases simply because stock needs clearing out fast but doesn’t necessarily indicate genuine interest from customers which would leadto sustainable long term engagement opportunities

Processing & Analysing Data: Now begins the real work! Using different methods including descriptive statistics/visualizations & predictive modelling techniques benchmark performance/stock levels against user ratings collected by customers allows insights into customer approval ratings giving quick actionable ideas on where adjustments may need attention so changes can be necessary quickly while avoiding any further stock damage due massive overhauls like introducing completely new styles/products leading greater expenses then required initially so instead focus energies toward aligning existing repertoire while focusing positive public opinion providing value at same time reducing overall costs saving vital resources better engage elsewhere later Additionally analyse past purchase histories similar items helps develop effective plans tweak look structure features down level send more appropriate recommendations instead just trying guess previously failed giving real world tangible results making improvements bottom line profit margin every step way monitoring adjustments dynamically drive fresh continual improvement ways Thereafter when try determine whether current instructions still driving shift user engagement could dampen beginning success measure returns alignment adjust accordingly ensure always stay relevant continuously

Research Ideas

• Use this dataset to compare the popularity of different products across countries, and discover which countries have an affinity to particular styles.
• Identify popular brands in different product categories, then use this insigh...

🛒 E-Commerce Customer Behavior and Sales Dataset 📊 Dataset Overview This comprehensive dataset contains 5,000 e-commerce transactions from a Turkish online retail platform, spanning from January 2023 to March 2024. The dataset provides detailed insights into customer demographics, purchasing behavior, product preferences, and engagement metrics.

🎯 Use Cases This dataset is perfect for:

Customer Segmentation Analysis: Identify distinct customer groups based on behavior Sales Forecasting: Predict future sales trends and patterns Recommendation Systems: Build product recommendation engines Customer Lifetime Value (CLV) Prediction: Estimate customer value Churn Analysis: Identify customers at risk of leaving Marketing Campaign Optimization: Target customers effectively Price Optimization: Analyze price sensitivity across categories Delivery Performance Analysis: Optimize logistics and shipping 📁 Dataset Structure The dataset contains 18 columns with the following features:

Order Information Order_ID: Unique identifier for each order (ORD_XXXXXX format) Date: Transaction date (2023-01-01 to 2024-03-26) Customer Demographics Customer_ID: Unique customer identifier (CUST_XXXXX format) Age: Customer age (18-75 years) Gender: Customer gender (Male, Female, Other) City: Customer city (10 major Turkish cities) Product Information Product_Category: 8 categories (Electronics, Fashion, Home & Garden, Sports, Books, Beauty, Toys, Food) Unit_Price: Price per unit (in TRY/Turkish Lira) Quantity: Number of units purchased (1-5) Transaction Details Discount_Amount: Discount applied (if any) Total_Amount: Final transaction amount after discount Payment_Method: Payment method used (5 types) Customer Behavior Metrics Device_Type: Device used for purchase (Mobile, Desktop, Tablet) Session_Duration_Minutes: Time spent on website (1-120 minutes) Pages_Viewed: Number of pages viewed during session (1-50) Is_Returning_Customer: Whether customer has purchased before (True/False) Post-Purchase Metrics Delivery_Time_Days: Delivery duration (1-30 days) Customer_Rating: Customer satisfaction rating (1-5 stars) 📈 Key Statistics Total Records: 5,000 transactions Date Range: January 2023 - March 2024 (15 months) Average Transaction Value: ~450 TRY Customer Satisfaction: 3.9/5.0 average rating Returning Customer Rate: 60% Mobile Usage: 55% of transactions 🔍 Data Quality ✅ No missing values ✅ Consistent formatting across all fields ✅ Realistic data distributions ✅ Proper data types for all columns ✅ Logical relationships between features 💡 Sample Analysis Ideas Customer Segmentation with K-Means Clustering

Segment customers based on spending, frequency, and recency Sales Trend Analysis

Identify seasonal patterns and peak shopping periods Product Category Performance

Compare revenue, ratings, and return rates across categories Device-Based Behavior Analysis

Understand how device choice affects purchasing patterns Predictive Modeling

Build models to predict customer ratings or purchase amounts City-Level Market Analysis

Compare market performance across different cities 🛠️ Technical Details File Format: CSV (Comma-Separated Values) Encoding: UTF-8 File Size: ~500 KB Delimiter: Comma (,) 📚 Column Descriptions Column Name Data Type Description Example Order_ID String Unique order identifier ORD_001337 Customer_ID String Unique customer identifier CUST_01337 Date DateTime Transaction date 2023-06-15 Age Integer Customer age 35 Gender String Customer gender Female City String Customer city Istanbul Product_Category String Product category Electronics Unit_Price Float Price per unit 1299.99 Quantity Integer Units purchased 2 Discount_Amount Float Discount applied 129.99 Total_Amount Float Final amount paid 2469.99 Payment_Method String Payment method Credit Card Device_Type String Device used Mobile Session_Duration_Minutes Integer Session time 15 Pages_Viewed Integer Pages viewed 8 Is_Returning_Customer Boolean Returning customer True Delivery_Time_Days Integer Delivery duration 3 Customer_Rating Integer Satisfaction rating 5 🎓 Learning Outcomes By working with this dataset, you can learn:

Data cleaning and preprocessing techniques Exploratory Data Analysis (EDA) with Python/R Statistical analysis and hypothesis testing Machine learning model development Data visualization best practices Business intelligence and reporting 📝 Citation If you use this dataset in your research or project, please cite:

E-Commerce Customer Behavior and Sales Dataset (2024) Turkish Online Retail Platform Data (2023-2024) Available on Kaggle ⚖️ License This dataset is released under the CC0: Public Domain license. You are free to use it for any purpose.

🤝 Contribution Found any issues or have suggestions? Feel free to provide feedback!

📞 Contact For questions or collaborations, please reach out through Kaggle.

Happy Analyzing! 🚀

Keywords: e-c...

Source:

Mariusz Šapczyński, Cracow University of Economics, Poland, lapczynm '@' uek.krakow.pl Sylwester Białowąs, Poznan University of Economics and Business, Poland, sylwester.bialowas '@' ue.poznan.pl

Data Set Information:

The dataset contains information on clickstream from online store offering clothing for pregnant women. Data are from five months of 2008 and include, among others, product category, location of the photo on the page, country of origin of the IP address and product price in US dollars.

Attribute Information:

The dataset contains 14 variables described in a separate file (See 'Data set description')

Relevant Papers:

N/A

Citation Request:

If you use this dataset, please cite:

Šapczyński M., Białowąs S. (2013) Discovering Patterns of Users' Behaviour in an E-shop - Comparison of Consumer Buying Behaviours in Poland and Other European Countries, “Studia Ekonomiczne†, nr 151, “La société de l'information : perspective européenne et globale : les usages et les risques d'Internet pour les citoyens et les consommateurs†, p. 144-153

Data description ìe-shop clothing 2008î

Variables:

1. YEAR (2008)

========================================================

2. MONTH -> from April (4) to August (8)

========================================================

3. DAY -> day number of the month

========================================================

4. ORDER -> sequence of clicks during one session

========================================================

5. COUNTRY -> variable indicating the country of origin of the IP address with the

following categories:

1-Australia 2-Austria 3-Belgium 4-British Virgin Islands 5-Cayman Islands 6-Christmas Island 7-Croatia 8-Cyprus 9-Czech Republic 10-Denmark 11-Estonia 12-unidentified 13-Faroe Islands 14-Finland 15-France 16-Germany 17-Greece 18-Hungary 19-Iceland 20-India 21-Ireland 22-Italy 23-Latvia 24-Lithuania 25-Luxembourg 26-Mexico 27-Netherlands 28-Norway 29-Poland 30-Portugal 31-Romania 32-Russia 33-San Marino 34-Slovakia 35-Slovenia 36-Spain 37-Sweden 38-Switzerland 39-Ukraine 40-United Arab Emirates 41-United Kingdom 42-USA 43-biz (.biz) 44-com (.com) 45-int (.int) 46-net (.net) 47-org (*.org)

========================================================

6. SESSION ID -> variable indicating session id (short record)

========================================================

7. PAGE 1 (MAIN CATEGORY) -> concerns the main product category:

1-trousers 2-skirts 3-blouses 4-sale

========================================================

8. PAGE 2 (CLOTHING MODEL) -> contains information about the code for each product

(217 products)

========================================================

9. COLOUR -> colour of product

1-beige 2-black 3-blue 4-brown 5-burgundy 6-gray 7-green 8-navy blue 9-of many colors 10-olive 11-pink 12-red 13-violet 14-white

========================================================

10. LOCATION -> photo location on the page, the screen has been divided into six parts:

1-top left 2-top in the middle 3-top right 4-bottom left 5-bottom in the middle 6-bottom right

========================================================

11. MODEL PHOTOGRAPHY -> variable with two categories:

1-en face 2-profile

========================================================

12. PRICE -> price in US dollars

========================================================

13. PRICE 2 -> variable informing whether the price of a particular product is higher than

the average price for the entire product category

1-yes 2-no

========================================================

14. PAGE -> page number within the e-store website (from 1 to 5)

++++++++++++++++++++++++++++++++++++++++++++++++++++++++

The market for free online survey software and tools is experiencing robust growth, driven by the increasing need for efficient and cost-effective data collection across diverse sectors. The accessibility of these tools, coupled with their user-friendly interfaces, has democratized market research, enabling small businesses, academic institutions, and non-profit organizations to conduct surveys with ease. While the exact market size in 2025 is unavailable, a reasonable estimate, considering the market's growth trajectory and the expanding adoption of digital tools, places it around $1.5 billion. This robust growth is fueled by several key drivers: the rising popularity of online research methods, the need for rapid data acquisition and analysis, and the increasing sophistication of free survey software features, which now include advanced analytics and reporting capabilities. Furthermore, the diverse application across market research, academic studies, internal enterprise management and other sectors, further drives growth. Market segmentation by survey type (mobile vs. web) presents opportunities for specialized tool development and market penetration. Although some constraints like limitations in advanced features compared to paid software and data security concerns exist, the ongoing innovation and development of free software tools mitigate these challenges to a large extent. The competitive landscape is vibrant, featuring established players like SurveyMonkey and Qualtrics alongside newer entrants, fostering continuous improvement and competitive pricing. The projected Compound Annual Growth Rate (CAGR) for the market, while not explicitly given, can be estimated conservatively at 12% for the forecast period of 2025-2033. This estimate considers the continued digitalization of market research and the ongoing expansion of the online survey software market. The regional breakdown suggests North America and Europe will remain dominant markets, but the Asia-Pacific region is expected to demonstrate significant growth fueled by increasing internet penetration and a burgeoning middle class. The presence of several Chinese companies in the list of major players further supports this projection. The market will continue to witness innovation in areas such as AI-powered survey design and analysis, and increased integration with other business software platforms, further driving market growth and attracting new users.

About the Dataset

This dataset contains Quality of Life indices for various countries around the globe, extracted from the Numbeo website. The data provides valuable metrics for comparing countries based on several aspects of living standards, which can assist in decisions such as choosing a place to live or analyzing global trends in quality of life.

OBS: The code to generate this dataset is presented on: https://www.kaggle.com/code/marcelobatalhah/web-scrapping-quality-of-life-index

Columns in the Dataset

1. Rank:
   The global rank of the country based on its Quality of Life Index according to Year (1 = highest quality of life).

2. Country:
   The name of the country.

3. Quality of Life Index:
   A composite index that evaluates the overall quality of life in a country by combining other indices, such as Safety, Purchasing Power, and Health Care.

4. Purchasing Power Index:
   Measures the relative purchasing power of the average consumer in a country compared to New York City (baseline = 100).

5. Safety Index:
   Indicates the safety level of a country. A higher score suggests a safer environment.

6. Health Care Index:
   Evaluates the quality and accessibility of healthcare in the country.

7. Cost of Living Index:
   Measures the relative cost of living in a country compared to New York City (baseline = 100).

8. Property Price to Income Ratio:
   Compares the affordability of real estate by dividing the average property price by the average income.

9. Traffic Commute Time Index:
   Reflects the average time spent commuting due to traffic.

10. Pollution Index:
    Rates the level of pollution in the country (air, water, etc.).

11. Climate Index:
    Rates the favorability of the climate in the country (higher = more favorable).

12. Year:
    Year when the metrics were extracted.

Key Insights from the Dataset

• The Quality of Life Index aggregates multiple indicators, making it a useful single metric to compare countries.
• Specific indices such as Safety Index or Health Care Index allow for focused analysis on areas like security or healthcare quality.
• Cost of Living Index and Purchasing Power Index can help determine the affordability of living in each country.

How the Data Was Collected

• The dataset was built using web scraping techniques in Python.
• The data was extracted from the "Quality of Life Rankings by Country" page on Numbeo.
• Libraries used:
  • requests for retrieving webpage content.
  • BeautifulSoup for parsing the HTML and extracting relevant information.
  • pandas for organizing and storing the data in a structured format.

Possible Applications

1. Relocation Decision Making:
   Use the dataset to compare countries and identify destinations with high quality of life, safety, and healthcare.

2. Global Analysis:
   Perform exploratory data analysis (EDA) to identify trends and correlations across quality of life metrics.

3. Visualization:
   Plot global maps, bar charts, or other visualizations to better understand the data.

4. Predictive Modeling:
   Use this dataset as a base for machine learning tasks, like predicting Quality of Life Index based on other metrics.

Contains Gallup data from countries that are home to more than 98% of the world's population through a state-of-the-art Web-based portal. Gallup Analytics puts Gallup's best global intelligence in users' hands to help them better understand the strengths and challenges of the world's countries and regions. Users can access Gallup's U.S. Daily tracking and World Poll data to compare residents' responses region by region and nation by nation to questions on topics such as economic conditions, government and business, health and wellbeing, infrastructure, and education.

The Gallup Analytics Database is accessed through the Cornell University Libraries here. In addition, a CUL subscription also allows access to the Gallup Respondent Level Data. For access please refer to the documentation below and then request the variables you need here.

Before requesting data from the World Poll, please see the Getting Started guide and the Worldwide Research Methodology and Codebook (You will need to request access). The Codebook will give you information about all available variables in the datasets. There are other guides available as well in the google folder. You can also access information about questions asked and variables using the Gallup World Poll Reference Tool. You will need to create your user account to access the tool. This will only give you access to information about the questions asked and variables. It will not give you access to the data.

For further documentation and information see this site from New York University Libraries. The Gallup documentation for the World Poll methodology is also available under the Data and Documentation tab.

In addition to the World Poll and Daily Tracking Poll, also available are the Gallup Covid-19 Survey, Gallup Poll Social Series Surveys, Race Relations Survey, Confidence in Institutions Survey, Honesty and Ethics in Professions Survey, and Religion Battery.

The process for getting access to respondent-level data from the Gallup U.S. Daily Tracking is similar to the World Poll Survey. There is no comparable discovery tool for U.S. Daily Tracking poll questions, however. Users need to consult the codebooks and available variables across years.

The COVID-19 web survey began on March 13, 2020 with daily random samples of U.S. adults, aged 18 and older who are members of the Gallup Panel. Before requesting data, please see the Gallup Panel COVID-19 Survey Methodology and Codebook.

The Gallup Poll Social Series (GPSS) dataset is a set of public opinion surveys designed to monitor U.S. adults’ views on numerous social, economic, and political topics. More information is available on the Gallup website: https://www.gallup.com/175307/gallup-poll-social-series-methodology.aspx As each month has a unique codebook, contact CCSS-ResearchSupport@cornell.edu to discuss your interests and start the data request process.

Starting in 1973, Gallup started measuring the confidence level in several US institutions like Congress, Presidency, Supreme Court, Police, etc. The included dataset includes data beginning in 1973 and data is collected once per year. Users should consult the list of available variables.

The Race Relations Poll includes topics that were previously represented in the GPSS Minority Relations Survey that ran through 2016. The Race Relations Survey was conducted November 2018. Users should consult the codebook for this poll before making their request.

The Honesty and Ethics in Professions Survey – Starting in 1976, Gallup started measuring US perceptions of the honesty and ethics of a list of professions. The included dataset was added to the collection in March 2023 and includes data ranging from 1976-2022. Documentation for this collection is located here and will require you to request access.

Religion Battery: Consolidated list of items focused on religion in the US from 1999-2022. Documentation for this collection is located here and will require you to request access.

Despite substantial policy efforts aimed at developing regional innovation systems (RIS), our understanding of institutional factors that promote synergy and integration at the regional scale is limited. To address this gap, we constructed 2 representations of research university ecosystems in California (CA) and Texas (TX) that identify institutional co-occurrences in research and news media, within and across these regions. The selection of these regions is attributed to the University of California and the University of Texas, two multi-campus university systems (MUS) that feature distinct configurations of institutional specialization. As such, we exploit these differences to analyze four institutional assortativity channels that foster system-level synergies: institutional proximity, prestige, homophily, and specialization. The first representation we constructed is based upon ~3 million publications collected from Clarivate Analytics Web of Science Core Collection (WOS) that are affiliated with at least one of the 28 institutions in our sample, which together represent >5% of publications indexed by WOS over the sample period 1970-2020. The 28 institutions consist of 10 institutions belonging to the University of California (UC) system and 12 institutions belonging to the University of Texas (UT) system; we complement these two public multi-campus university systems (MUS) by including six prominent private universities, which represent a non-MUS comparison group. As universities increasingly compete for visibility to attract student enrollment and build scientific reputation, the management of institution of higher education (IHE) brand has emerged as an important strategic endeavor. Hence, the second representation we constructed is based upon ~2 million digital news media articles published between 2000-2020 that specifically mention at least one of these universities. Similar to the first representation, mapping the rates of digital media co-visibility among IHE facilitates a systems-level understanding of the factors that condition the structure and dynamics of brand stratification within research university ecosystems, and fosters the development of novel measures for two dimensions of brand equity – namely, visibility and association. Methods 1) Research affiliated with a particular institution. We collected 2,965,198 records published between 1970-2020 from the Clarivate Analytics WOS Core Collection using their in-house institutional disambiguation tool to identify publications with at least one author from a particular campus. 2) Digital media affiliated with a particular institution. We assembled a dataset of 1,947,349 unique web-based digital media articles representing news articles, blog posts and other web content specifically mentioning any of the institutions by their official name, e.g. “University of California Los Angeles” or “UCLA”, accounting for the official abbreviations. These media articles were originally produced by 57,947 unique media sources, according to primary source data obtained from the Media Cloud project (MC) database, https://www.mediacloud.org/ . We use both data sources to develop a co-occurrence framework for defining university-university relationships based upon research co-production (via collaboration among scholars affiliated with each university) and media article co-visibility over the period 2000-2020, by applying concepts and methods from network science, machine learning (NLP) and organizational science.

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In‑App Analytics Platform Market Outlook

According to our latest research, the Global In‑App Analytics Platform market size was valued at $4.8 billion in 2024 and is projected to reach $16.2 billion by 2033, expanding at a CAGR of 14.1% during 2024–2033. The accelerating digitization of business processes and the growing reliance on data-driven decision-making are major factors propelling the global adoption of in-app analytics platforms. Organizations across industries are increasingly leveraging these platforms to extract actionable insights from user interactions within their applications, driving enhanced customer experiences, operational efficiency, and competitive advantage. As enterprises strive to personalize offerings and optimize engagement, the demand for robust in-app analytics solutions is witnessing unprecedented growth worldwide.

Regional Outlook

North America currently dominates the in-app analytics platform market, holding the largest share of global revenues. This leadership is attributed to the region’s mature digital infrastructure, widespread adoption of advanced analytics, and a strong presence of leading technology vendors. The United States, in particular, accounts for a significant portion of North America’s market share, driven by early adoption across BFSI, healthcare, and retail sectors. Favorable regulatory frameworks, high investments in R&D, and an established ecosystem for cloud-based solutions further reinforce North America’s position. The region’s market value is projected to surpass $6.3 billion by 2033, with a steady CAGR reflecting the ongoing integration of analytics into enterprise workflows and the rise of AI-powered insights.

Asia Pacific is emerging as the fastest-growing region in the in-app analytics platform market, forecasted to expand at a remarkable CAGR of 18.4% through 2033. This rapid growth is primarily fueled by the digital transformation initiatives across China, India, Japan, and Southeast Asian countries. Increasing smartphone penetration, the proliferation of mobile and web applications, and government-driven digital economy programs are catalyzing demand for analytics solutions. Enterprises in the region are ramping up investments in cloud infrastructure and AI technologies, further accelerating adoption. Additionally, the region’s vibrant startup ecosystem and rising e-commerce activities are contributing to the surge in in-app analytics deployments, positioning Asia Pacific as a key growth engine for the market.

In contrast, emerging economies in Latin America, the Middle East, and Africa present a mixed landscape for the in-app analytics platform market. While these regions are witnessing growing interest in analytics for mobile and web applications, several challenges persist. Limited digital infrastructure, skill gaps, and variable regulatory environments can hinder widespread adoption. However, localized demand in sectors such as retail, telecommunications, and financial services is gradually driving uptake. Tailored solutions that address language, compliance, and integration requirements are gaining traction. As governments and enterprises in these regions prioritize digital transformation, the market is expected to experience incremental growth, albeit at a more measured pace compared to leading regions.

Report Scope

Mobile App Statistics (Apple iOS app store)

The ever-changing mobile landscape is a challenging space to navigate. . The percentage of mobile over desktop is only increasing. Android holds about 53.2% of the smartphone market, while iOS is 43%. To get more people to download your app, you need to make sure they can easily find your app. Mobile app analytics is a great way to understand the existing strategy to drive growth and retention of future user.

With million of apps around nowadays, the following data set has become very key to getting top trending apps in iOS app store. This data set contains more than 7000 Apple iOS mobile application details. The data was extracted from the iTunes Search API at the Apple Inc website. R and linux web scraping tools were used for this study.

Interactive full Shiny app can be seen here( https://multiscal.shinyapps.io/appStore/)

Data collection date (from API); July 2017

Dimension of the data set; 7197 rows and 16 columns

Content:

appleStore.csv

1. "id" : App ID

2. "track_name": App Name

3. "size_bytes": Size (in Bytes)

4. "currency": Currency Type

5. "price": Price amount

6. "rating_count_tot": User Rating counts (for all version)

7. "rating_count_ver": User Rating counts (for current version)

8. "user_rating" : Average User Rating value (for all version)

9. "user_rating_ver": Average User Rating value (for current version)

10. "ver" : Latest version code

11. "cont_rating": Content Rating

12. "prime_genre": Primary Genre

13. "sup_devices.num": Number of supporting devices

14. "ipadSc_urls.num": Number of screenshots showed for display

15. "lang.num": Number of supported languages

16. "vpp_lic": Vpp Device Based Licensing Enabled

appleStore_description.csv

1. id : App ID
2. track_name: Application name
3. size_bytes: Memory size (in Bytes)
4. app_desc: Application description

Acknowledgements

The data was extracted from the iTunes Search API at the Apple Inc website. R and linux web scraping tools were used for this study.

Inspiration

1. How does the App details contribute the user ratings?
2. Try to compare app statistics for different groups?

Reference: R package From github, with devtools::install_github("ramamet/applestoreR")

Licence

Copyright (c) 2018 Ramanathan Perumal

The POI solution is designed specifically to help organizations understand and be more informed about the location that they are interested in.

It's a perfect solution for organizations that typically have many installations such as Service Centers, Retail Outlets, Fuel Stations etc spread across a geography. Alternatively it could be used for single stand alone POIs as well such as a Museum.

Understanding the flow of customers to and from the location, Home location analysis, Occupancy analysis, Customer Profiling (Age, gender, SEC, Nationality, Ethnicity etc) can be effectively done at a frequency of T-1 Day.

The solution typically helps organizations in measuring/comparing performance of different POIs. Our customers use it to decide which locations to keep operational, which locations to close and where to make new outlets

Demo Projects: http://santander-mobility.s3-website.eu-central-1.amazonaws.com/ http://prod-kido-demos-qatar.s3-website.eu-central-1.amazonaws.com/

SaaS-based Business Analytics Market size was valued at USD 12.21 Billion in 2024 and is projected to reach USD 35.51 Billion by 2031, growing at a CAGR of 14.31% from 2024 to 2031.

SaaS-based Business Analytics Market Drivers

Cost Efficiency: SaaS-based solutions typically offer lower upfront costs and reduced total cost of ownership compared to traditional on-premise software. The subscription-based model makes advanced analytics accessible to businesses of all sizes without significant capital investment.

Scalability and Flexibility: SaaS-based business analytics solutions can easily scale with the needs of the business. Organizations can adjust their usage and costs according to their requirements, adding or removing features as needed without the hassle of hardware upgrades.

Ease of Implementation and Use: SaaS solutions are known for their quick deployment and user-friendly interfaces. Businesses can start using these tools with minimal IT intervention, reducing the time and resources needed for implementation and training.

The global Data Analytics Software Tools market is projected to reach a substantial USD 120 billion by 2025, exhibiting a robust Compound Annual Growth Rate (CAGR) of 12% during the forecast period of 2025-2033. This remarkable expansion is fueled by an ever-increasing volume of data generated across industries and the growing imperative for businesses to leverage this data for informed decision-making, operational efficiency, and competitive advantage. The digital transformation initiatives sweeping across sectors, coupled with the proliferation of cloud computing, are significant drivers underpinning this market's growth. Organizations are increasingly investing in advanced analytics capabilities to gain deeper insights into customer behavior, optimize supply chains, enhance product development, and mitigate risks. The market segmentation by application reveals the widespread adoption of data analytics tools. The Government, Retail & eCommerce, Healthcare & Life Sciences, BFSI (Banking, Financial Services, and Insurance), and Manufacturing sectors are leading the charge, each seeking to harness data for critical functions like fraud detection, personalized marketing, predictive diagnostics, and process optimization. The shift towards Cloud-Based solutions is particularly noteworthy, offering scalability, flexibility, and cost-effectiveness compared to traditional On-Premises deployments. Key players such as Teradata Corporation, IBM, Oracle Corporation, Amazon Web Services, SAP, Informatica, and Microsoft Corporation are at the forefront, continuously innovating and offering a comprehensive suite of tools to meet the diverse and evolving needs of the market. Geographically, North America is expected to dominate, followed by Europe and the rapidly growing Asia Pacific region, driven by increasing digital adoption and government support for data-driven initiatives. This comprehensive report delves into the dynamic landscape of Data Analytics Software Tools, providing in-depth insights into market dynamics, product offerings, regional trends, and future outlook. With an estimated global market size exceeding $70,000 million in 2023, this sector is a critical enabler of digital transformation across industries. The report offers granular analysis for key players, including Teradata Corporation, IBM, Oracle Corporation, Amazon Web Services, SAP, Informatica, and Microsoft Corporation, and examines their impact across diverse segments such as Government, Retail & eCommerce, Healthcare & Life Sciences, BFSI, Manufacturing, and Others. We explore both Cloud-Based and On-Premises deployment models, alongside significant industry developments shaping the market's trajectory.

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