The Connected Retail market, valued at approximately $XX million in 2025, is poised for significant growth, exhibiting a Compound Annual Growth Rate (CAGR) of 3.23% from 2025 to 2033. This expansion is driven by the increasing adoption of technologies like IoT (Internet of Things), artificial intelligence (AI), and big data analytics to enhance customer experience, optimize inventory management, and improve operational efficiency. Retailers are leveraging connected devices such as smart shelves, RFID tags, and beacons to gain real-time insights into customer behavior, inventory levels, and supply chain performance. The integration of these technologies allows for personalized shopping experiences, targeted promotions, and improved loss prevention measures, ultimately boosting sales and profitability. Key segments driving growth include hardware (point-of-sale systems, digital signage), software (analytics platforms, customer relationship management (CRM) systems), and services (integration, consulting, and maintenance). The adoption of various communication technologies, including Zigbee, NFC, Bluetooth Low Energy, and Wi-Fi, further fuels market expansion. North America is currently the largest regional market, followed by Europe and Asia-Pacific, with the latter expected to witness significant growth in the coming years due to increasing digitalization and rising e-commerce penetration. However, market growth faces some restraints. High initial investment costs associated with implementing connected retail solutions can be a barrier for smaller retailers. Concerns regarding data security and privacy also pose challenges, necessitating robust security measures and transparent data handling practices. Furthermore, the complexity of integrating various technologies and systems within a retailer's existing infrastructure requires significant expertise and careful planning. Despite these challenges, the long-term benefits of enhanced customer experience, optimized operations, and improved profitability are driving sustained investment and adoption of connected retail technologies across various retail segments, ensuring continued market expansion in the forecast period. Companies like Honeywell, IBM, NXP Semiconductors, and Cisco Systems are playing a significant role in shaping this evolving landscape. This comprehensive report provides an in-depth analysis of the rapidly evolving Connected Retail Market, offering invaluable insights for businesses seeking to capitalize on the transformative potential of digital technologies within the retail landscape. We project the market to reach USD XXX million by 2033, showcasing substantial growth opportunities across various segments. The study period covers 2019-2033, with 2025 serving as the base and estimated year. This report leverages data from the historical period (2019-2024) and forecasts market trends until 2033. Key players analyzed include Honeywell International Inc, IBM Corporation, NXP Semiconductors NV, Softweb Solutions Inc, Cisco Systems Inc, Microsoft Corporation, Zebra Technologies Corp, Verizon Enterprise Solutions, SAP SE, and Intel Corporation (list not exhaustive). Key drivers for this market are: , Increased Adoption of IoT Devices. Potential restraints include: , Data Security and Privacy Concerns. Notable trends are: Emergence of IoT in Retail is Expected to Drive the Market.

The Asia Pacific retail analytics market was valued at USD 1.79 Billion in 2022 and is anticipated to project robust growth at a CAGR of 19.63% during the forecast period.

Federated Analytics Retail Consortium Market Outlook

According to our latest research, the global Federated Analytics Retail Consortium market size reached USD 1.42 billion in 2024, reflecting robust growth driven by the increasing adoption of privacy-preserving analytics and collaborative data intelligence across the retail sector. The market is poised to expand at a CAGR of 23.7% from 2025 to 2033, with the market expected to reach USD 11.98 billion by 2033. This significant expansion is primarily attributed to the escalating demand for advanced analytics solutions that enable retailers to leverage shared insights without compromising sensitive customer data, thereby driving innovation and operational efficiency at scale.

One of the foremost growth factors propelling the Federated Analytics Retail Consortium market is the intensifying need for privacy-centric data analytics frameworks in the retail industry. As data privacy regulations such as GDPR, CCPA, and other regional mandates become more stringent, retailers are increasingly seeking solutions that allow them to collaborate on analytics projects without exposing proprietary or personally identifiable information. Federated analytics enables multiple retailers to jointly analyze data and extract valuable insights while keeping raw data decentralized and secure. This approach not only ensures compliance with evolving data privacy standards but also fosters trust among consortium members, making federated analytics a preferred choice for modern retail organizations aiming to balance innovation with privacy.

Another pivotal driver for market growth is the rising complexity and volume of retail data generated from omnichannel operations, customer touchpoints, and supply chain activities. Retailers are under immense pressure to optimize inventory, personalize marketing, and enhance customer experience—all of which require access to rich, diverse datasets. Federated analytics allows consortium members to pool their analytical capabilities and share derived insights, leading to more accurate demand forecasting, efficient inventory management, and targeted marketing initiatives. By leveraging the collective intelligence of industry peers, retailers can better respond to market trends, mitigate risks such as fraud, and achieve superior business outcomes, thereby fueling the adoption of federated analytics platforms.

Technological advancements and the proliferation of cloud computing are also accelerating the growth of the Federated Analytics Retail Consortium market. The integration of AI, machine learning, and edge computing with federated analytics solutions is enabling real-time data processing and advanced predictive modeling across distributed retail environments. Cloud-based deployment models, in particular, are making it easier for retailers of all sizes to participate in federated analytics initiatives without the need for extensive on-premises infrastructure. This democratization of access is encouraging small and medium enterprises to join retail consortiums, further expanding the addressable market and driving continuous innovation in analytics methodologies and use cases.

From a regional perspective, North America currently leads the Federated Analytics Retail Consortium market, accounting for a significant share owing to its mature retail ecosystem and early adoption of advanced analytics technologies. However, Asia Pacific is emerging as the fastest-growing region, supported by rapid digital transformation, expanding retail networks, and increasing investments in data-driven business models. Europe also maintains a strong position, driven by strict data privacy regulations and a high concentration of multinational retail chains. As global retailers seek to navigate the complexities of cross-border data collaboration and analytics, regional consortiums are expected to play a crucial role in shaping the future landscape of federated analytics in retail.

Component Analysis

The Federated Analytics Retail Consortium market is segmented by component into software, hardware, and services, each playing a vital role in enabling seamless analytics collaboration across retail organizations. The software segment dominates the market, primarily due to the growing demand for advanced federated learning platforms, data orchestration tools, and analytics engines that facilitate secure multi-party computation. These software solutions are designed to handle complex data integra

Vrinda Store: Interactive Ms Excel dashboardVrinda Store: Interactive Ms Excel dashboard Feb 2024 - Mar 2024Feb 2024 - Mar 2024 The owner of Vrinda store wants to create an annual sales report for 2022. So that their employees can understand their customers and grow more sales further. Questions asked by Owner of Vrinda store are as follows:- 1) Compare the sales and orders using single chart. 2) Which month got the highest sales and orders? 3) Who purchased more - women per men in 2022? 4) What are different order status in 2022?

And some other questions related to business. The owner of Vrinda store wanted a visual story of their data. Which can depict all the real time progress and sales insight of the store. This project is a Ms Excel dashboard which presents an interactive visual story to help the Owner and employees in increasing their sales. Task performed : Data cleaning, Data processing, Data analysis, Data visualization, Report. Tool used : Ms Excel The owner of Vrinda store wants to create an annual sales report for 2022. So that their employees can understand their customers and grow more sales further. Questions asked by Owner of Vrinda store are as follows:- 1) Compare the sales and orders using single chart. 2) Which month got the highest sales and orders? 3) Who purchased more - women per men in 2022? 4) What are different order status in 2022? And some other questions related to business. The owner of Vrinda store wanted a visual story of their data. Which can depict all the real time progress and sales insight of the store. This project is a Ms Excel dashboard which presents an interactive visual story to help the Owner and employees in increasing their sales. Task performed : Data cleaning, Data processing, Data analysis, Data visualization, Report. Tool used : Ms Excel Skills: Data Analysis · Data Analytics · ms excel · Pivot Tables

The Europe retail analytics market was valued at USD 1.12 Billion in 2022 and is anticipated to project robust growth at a CAGR of 18.73% during the forecast period.

Context and Methodology

Research Domain:
The dataset is part of a project focused on retail sales forecasting. Specifically, it is designed to predict daily sales for Rossmann, a chain of over 3,000 drug stores operating across seven European countries. The project falls under the broader domain of time series analysis and machine learning applications for business optimization. The goal is to apply machine learning techniques to forecast future sales based on historical data, which includes factors like promotions, competition, holidays, and seasonal trends.

Purpose:
The primary purpose of this dataset is to help Rossmann store managers predict daily sales for up to six weeks in advance. By making accurate sales predictions, Rossmann can improve inventory management, staffing decisions, and promotional strategies. This dataset serves as a training set for machine learning models aimed at reducing forecasting errors and supporting decision-making processes across the company’s large network of stores.

How the Dataset Was Created:
The dataset was compiled from several sources, including historical sales data from Rossmann stores, promotional calendars, holiday schedules, and external factors such as competition. The data is split into multiple features, such as the store's location, promotion details, whether the store was open or closed, and weather information. The dataset is publicly available on platforms like Kaggle and was initially created for the Kaggle Rossmann Store Sales competition. The data is made accessible via an API for further analysis and modeling, and it is structured to help machine learning models predict future sales based on various input variables.

Technical Details

Dataset Structure:

The dataset consists of three main files, each with its specific role:

1. Train:
   This file contains the historical sales data, which is used to train machine learning models. It includes daily sales information for each store, as well as various features that could influence the sales (e.g., promotions, holidays, store type, etc.).

   https://handle.test.datacite.org/10.82556/yb6j-jw41
   PID: b1c59499-9c6e-42c2-af8f-840181e809db

2. Test2:
   The test dataset mirrors the structure of train.csv but does not include the actual sales values (i.e., the target variable). This file is used for making predictions using the trained machine learning models. It is used to evaluate the accuracy of predictions when the true sales data is unknown.

   https://handle.test.datacite.org/10.82556/jerg-4b84
   PID: 7cbb845c-21dd-4b60-b990-afa8754a0dd9

3. Store:
   This file provides metadata about each store, including information such as the store’s location, type, and assortment level. This data is essential for understanding the context in which the sales data is gathered.

   https://handle.test.datacite.org/10.82556/nqeg-gy34
   PID: 9627ec46-4ee6-4969-b14a-bda555fe34db

Data Fields Description:

• Id: A unique identifier for each (Store, Date) combination within the test set.

• Store: A unique identifier for each store.

• Sales: The daily turnover (target variable) for each store on a specific day (this is what you are predicting).

• Customers: The number of customers visiting the store on a given day.

• Open: An indicator of whether the store was open (1 = open, 0 = closed).

• StateHoliday: Indicates if the day is a state holiday, with values like:

  • 'a' = public holiday,

  • 'b' = Easter holiday,

  • 'c' = Christmas,

  • '0' = no holiday.

• SchoolHoliday: Indicates whether the store is affected by school closures (1 = yes, 0 = no).

• StoreType: Differentiates between four types of stores: 'a', 'b', 'c', 'd'.

• Assortment: Describes the level of product assortment in the store:

  • 'a' = basic,

  • 'b' = extra,

  • 'c' = extended.

• CompetitionDistance: Distance (in meters) to the nearest competitor store.

• CompetitionOpenSince[Month/Year]: The month and year when the nearest competitor store opened.

• Promo: Indicates whether the store is running a promotion on a particular day (1 = yes, 0 = no).

• Promo2: Indicates whether the store is participating in Promo2, a continuing promotion for some stores (1 = participating, 0 = not participating).

• Promo2Since[Year/Week]: The year and calendar week when the store started participating in Promo2.

• PromoInterval: Describes the months when Promo2 is active, e.g., "Feb,May,Aug,Nov" means the promotion starts in February, May, August, and November.

Software Requirements

To work with this dataset, you will need to have specific software installed, including:

• DBRepo Authorization: This is required to access the datasets via the DBRepo API. You may need to authenticate with an API key or login credentials to retrieve the datasets.

• Python Libraries: Key libraries for working with the dataset include:

  • pandas for data manipulation,

  • numpy for numerical operations,

  • matplotlib and seaborn for data visualization,

  • scikit-learn for machine learning algorithms.

Additional Resources

Several additional resources are available for working with the dataset:

1. Presentation:
   A presentation summarizing the exploratory data analysis (EDA), feature engineering process, and key insights from the analysis is provided. This presentation also includes visualizations that help in understanding the dataset’s trends and relationships.

2. Jupyter Notebook:
   A Jupyter notebook, titled Retail_Sales_Prediction_Capstone_Project.ipynb, is provided, which details the entire machine learning pipeline, from data loading and cleaning to model training and evaluation.

3. Model Evaluation Results:
   The project includes a detailed evaluation of various machine learning models, including their performance metrics like training and testing scores, Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). This allows for a comparison of model effectiveness in forecasting sales.

4. Trained Models (.pkl files):
   The models trained during the project are saved as .pkl files. These files contain the trained machine learning models (e.g., Random Forest, Linear Regression, etc.) that can be loaded and used to make predictions without retraining the models from scratch.

5. sample_submission.csv:
   This file is a sample submission file that demonstrates the format of predictions expected when using the trained model. The sample_submission.csv contains predictions made on the test dataset using the trained Random Forest model. It provides an example of how the output should be structured for submission.

These resources provide a comprehensive guide to implementing and analyzing the sales forecasting model, helping you understand the data, methods, and results in greater detail.

The Middle East & Africa retail analytics market was valued at USD 0.59 Billion in 2022 and is anticipated to project robust growth at a CAGR of 18.47% during the forecast period.

The global home improvement retail market is expected to reach a value of USD 1,290.8 billion by 2033, exhibiting a CAGR of 6.1% during the forecast period of 2025-2033. The expansion of the market can be attributed to the rising demand for home improvement projects fueled by growing disposable income, increased homeownership rates, and a shift towards do-it-yourself (DIY) projects. Additionally, the increasing popularity of smart home devices and systems is anticipated to drive market growth as consumers seek to enhance the functionality and convenience of their homes. Key trends shaping the home improvement retail market include the emergence of e-commerce, the growing popularity of sustainable and eco-friendly products, and the adoption of innovative technologies such as artificial intelligence (AI) and augmented reality (AR). The shift towards online retail has enabled consumers to easily access a wider selection of products and compare prices, while the adoption of AI and AR is enhancing the customer experience by providing personalized recommendations, virtual tours, and interactive product demos. These trends are expected to continue to drive market growth over the coming years, as consumers increasingly seek convenient, personalized, and sustainable home improvement solutions. The home improvement retail market is a large and growing industry, with a global market size of $663.3 billion in 2019. The market is expected to grow to $940.3 billion by 2027, at a compound annual growth rate (CAGR) of 4.7%. The growth of the home improvement retail market is being driven by a number of factors, including:

The increasing popularity of home improvement projects The rising cost of housing The aging population The growing number of single-family homes

The home improvement retail market is concentrated in a few major players, including The Home Depot, Lowe's, and Menard's. These companies have a strong presence in the United States and are expanding into other markets. The home improvement retail market is characterized by a number of trends, including:

The growing popularity of online shopping The increasing use of mobile devices for home improvement projects The rising demand for sustainable products The growing popularity of smart home products

The home improvement retail market is expected to continue to grow in the coming years. The growth of the market will be driven by the increasing popularity of home improvement projects, the rising cost of housing, the aging population, and the growing number of single-family homes.

The global market for Supply Chain IT Transformation Services in the retail sector is experiencing robust growth, driven by the increasing need for enhanced agility, resilience, and efficiency in the face of evolving consumer demands and global economic uncertainties. The market, currently estimated at $50 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $120 billion by 2033. This expansion is fueled by several key factors: the widespread adoption of cloud-based solutions for improved scalability and cost-efficiency, the integration of advanced analytics and AI for predictive demand planning and optimized inventory management, and the growing focus on omnichannel strategies requiring seamless integration across different sales channels. Large enterprises are currently the dominant segment, however, the increasing digitalization of SMEs is expected to significantly boost their participation in the coming years. End-to-end transformation projects remain the largest segment, reflecting the desire for holistic solutions, while the adoption of managed services is experiencing rapid growth due to its cost-effectiveness and reduced operational burden. Geographic analysis reveals North America and Europe as the leading markets, benefiting from established digital infrastructure and higher adoption rates of advanced technologies. However, Asia-Pacific, particularly India and China, is poised for significant growth due to its large retail market and rapidly expanding digital economy. Competition in this space is intense, with major players like Accenture, TCS, Infosys, and others vying for market share. Success hinges on the ability to deliver comprehensive solutions that address the specific needs of retailers across various segments, leveraging expertise in areas such as cloud migration, data analytics, automation, and cybersecurity. The adoption of advanced technologies, such as blockchain for improved supply chain transparency and IoT for real-time inventory tracking, presents substantial growth opportunities for service providers. However, challenges such as the high initial investment costs associated with transformation projects, the need for skilled professionals, and the integration complexity of legacy systems represent potential restraints to market expansion. Overcoming these obstacles through strategic partnerships, investment in talent development, and the adoption of agile methodologies is critical for sustained growth within this dynamic market.

Market Basket Analysis

Market basket analysis with Apriori algorithm

The retailer wants to target customers with suggestions on itemset that a customer is most likely to purchase .I was given dataset contains data of a retailer; the transaction data provides data around all the transactions that have happened over a period of time. Retailer will use result to grove in his industry and provide for customer suggestions on itemset, we be able increase customer engagement and improve customer experience and identify customer behavior. I will solve this problem with use Association Rules type of unsupervised learning technique that checks for the dependency of one data item on another data item.

Introduction

Association Rule is most used when you are planning to build association in different objects in a set. It works when you are planning to find frequent patterns in a transaction database. It can tell you what items do customers frequently buy together and it allows retailer to identify relationships between the items.

An Example of Association Rules

Assume there are 100 customers, 10 of them bought Computer Mouth, 9 bought Mat for Mouse and 8 bought both of them. - bought Computer Mouth => bought Mat for Mouse - support = P(Mouth & Mat) = 8/100 = 0.08 - confidence = support/P(Mat for Mouse) = 0.08/0.09 = 0.89 - lift = confidence/P(Computer Mouth) = 0.89/0.10 = 8.9 This just simple example. In practice, a rule needs the support of several hundred transactions, before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.

Strategy

• Data Import
• Data Understanding and Exploration
• Transformation of the data – so that is ready to be consumed by the association rules algorithm
• Running association rules
• Exploring the rules generated
• Filtering the generated rules
• Visualization of Rule

Dataset Description

• File name: Assignment-1_Data
• List name: retaildata
• File format: . xlsx
• Number of Row: 522065

• Number of Attributes: 7

  • BillNo: 6-digit number assigned to each transaction. Nominal.
  • Itemname: Product name. Nominal.
  • Quantity: The quantities of each product per transaction. Numeric.
  • Date: The day and time when each transaction was generated. Numeric.
  • Price: Product price. Numeric.
  • CustomerID: 5-digit number assigned to each customer. Nominal.
  • Country: Name of the country where each customer resides. Nominal.

https://user-images.githubusercontent.com/91852182/145270162-fc53e5a3-4ad1-4d06-b0e0-228aabcf6b70.png">

Libraries in R

First, we need to load required libraries. Shortly I describe all libraries.

• arules - Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules).
• arulesViz - Extends package 'arules' with various visualization. techniques for association rules and item-sets. The package also includes several interactive visualizations for rule exploration.
• tidyverse - The tidyverse is an opinionated collection of R packages designed for data science.
• readxl - Read Excel Files in R.
• plyr - Tools for Splitting, Applying and Combining Data.
• ggplot2 - A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
• knitr - Dynamic Report generation in R.
• magrittr- Provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression. There is flexible support for the type of right-hand side expressions.
• dplyr - A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
• tidyverse - This package is designed to make it easy to install and load multiple 'tidyverse' packages in a single step.

https://user-images.githubusercontent.com/91852182/145270210-49c8e1aa-9753-431b-a8d5-99601bc76cb5.png">

Data Pre-processing

Next, we need to upload Assignment-1_Data. xlsx to R to read the dataset.Now we can see our data in R.

https://user-images.githubusercontent.com/91852182/145270229-514f0983-3bbb-4cd3-be64-980e92656a02.png"> https://user-images.githubusercontent.com/91852182/145270251-6f6f6472-8817-435c-a995-9bc4bfef10d1.png">

After we will clear our data frame, will remove missing values.

https://user-images.githubusercontent.com/91852182/145270286-05854e1a-2b6c-490e-ab30-9e99e731eacb.png">

To apply Association Rule mining, we need to convert dataframe into transaction data to make all items that are bought together in one invoice will be in ...

The global market size of South Retail is $XX million in 2018 with XX CAGR from 2014 to 2018, and it is expected to reach $XX million by the end of 2024 with a CAGR of XX% from 2019 to 2024.
Global South Retail Market Report 2019 - Market Size, Share, Price, Trend and Forecast is a professional and in-depth study on the current state of the global South Retail industry. The key insights of the report:
1.The report provides key statistics on the market status of the South Retail manufacturers and is a valuable source of guidance and direction for companies and individuals interested in the industry.
2.The report provides a basic overview of the industry including its definition, applications and manufacturing technology.
3.The report presents the company profile, product specifications, capacity, production value, and 2013-2018 market shares for key vendors.
4.The total market is further divided by company, by country, and by application/type for the competitive landscape analysis.
5.The report estimates 2019-2024 market development trends of South Retail industry.
6.Analysis of upstream raw materials, downstream demand, and current market dynamics is also carried out
7.The report makes some important proposals for a new project of South Retail Industry before evaluating its feasibility.
There are 4 key segments covered in this report: competitor segment, product type segment, end use/application segment and geography segment.
For competitor segment, the report includes global key players of South Retail as well as some small players.
The information for each competitor includes:
* Company Profile
* Main Business Information
* SWOT Analysis
* Sales, Revenue, Price and Gross Margin
* Market Share

For product type segment, this report listed main product type of South Retail market
* Product Type I
* Product Type II
* Product Type III

For end use/application segment, this report focuses on the status and outlook for key applications. End users sre also listed.
* Application I
* Application II
* Application III

For geography segment, regional supply, application-wise and type-wise demand, major players, price is presented from 2013 to 2023. This report covers following regions:
* North America
* South America
* Asia & Pacific
* Europe
* MEA (Middle East and Africa)
The key countries in each region are taken into consideration as well, such as United States, China, Japan, India, Korea, ASEAN, Germany, France, UK, Italy, Spain, CIS, and Brazil etc.

Reasons to Purchase this Report:
* Analyzing the outlook of the market with the recent trends and SWOT analysis
* Market dynamics scenario, along with growth opportunities of the market in the years to come
* Market segmentation analysis including qualitative and quantitative research incorporating the impact of economic and non-economic aspects
* Regional and country level analysis integrating the demand and supply forces that are influencing the growth of the market.
* Market value (USD Million) and volume (Units Million) data for each segment and sub-segment
* Competitive landscape involving the market share of major players, along with the new projects and strategies adopted by players in the past five years
* Comprehensive company profiles covering the product offerings, key financial information, recent developments, SWOT analysis, and strategies employed by the major market players
* 1-year analyst support, along with the data support in excel format.
We also can offer customized report to fulfill special requirements of our clients. Regional and Countries report can be provided as well.

Here are a few use cases for this project:

Use Case 1: Gender-Based Retail Analytics By analyzing customer demographics in retail stores, the "man vrouw dataset 1" can help retailers understand the gender distribution of their shoppers, empowering them to make informed decisions on store layout, marketing strategies, and product placements.

Use Case 2: Crowd Monitoring and Event Management This model can help enhance safety and optimize visitor experience at crowded events, such as concerts or festivals, by identifying the gender distribution of attendees, enabling promoters to customize services, restrooms allocation, and security measures accordingly.

Use Case 3: Digital Advertising and Marketing Using the "man vrouw dataset 1" model, businesses can better target their digital advertisements by understanding the key demographic visiting specific websites or engaging with specific content, allowing for tailored ad campaigns designed to target male or female audiences.

Use Case 4: Smart Surveillance and Security Systems The model can be used in surveillance and security systems to help identify and track people by their HU classes (man or vrouw) in premises like airports or corporate buildings, allowing security teams to analyze patterns and prevent potential threats.

Use Case 5: Social Media Image Analysis The "man vrouw dataset 1" model can be used to analyze the gender composition of social media images, providing insights into trends, preferences, and behaviors of different gender groups on social platforms. This information can then be used for targeted marketing or social research purposes.

The DIY retail market is expanding rapidly, driven by the growing demand for home improvement projects and the increasing popularity of online shopping. The market size reached USD XX million in 2025 and is projected to witness a CAGR of XX% during the forecast period from 2025 to 2033. This growth is attributed to factors such as the rising consumer spending on home improvement projects, the increasing availability of DIY products online, and the growing popularity of home automation. The market is segmented into online sales and offline sales, with the former gaining significant traction due to the convenience and accessibility it offers. In terms of product types, decoration and indoor gardening, painting and wallpaper, tools and hardware, building materials, lighting, and plumbing and equipment are the major categories. Home Depot, Lowe's, Menards, Ace Hardware, Rona, Homebase, Sherwin-Williams, Kingfisher, ADEO, and HORNBACH Group are prominent players in the global DIY retail market. The market is highly fragmented, with regional players holding significant market share in their respective regions. North America and Europe hold the largest market share, while Asia-Pacific is expected to witness the highest growth rate during the forecast period.

Here are a few use cases for this project:

1. Retail Analytics: This model can be utilized to analyze the floor plan of retail stores. By identifying floors and shelves, it could provide data on product placement, customer's walking patterns, or best locations for advertising displays.

2. Robot Navigation: In warehouses or industrial settings, this model could be integrated into autonomous ground vehicles or robots to identify and navigate floor spaces, and avoid shelving units or other obstacles.

3. Layout Optimization: It can be used by architects, interior designers, or construction planners to optimize the use of space within a building. It can help identify whether a change in physical layout could improve functionality and usability.

4. Virtual Reality Space Modeling: Using this model, developers could create more realistic virtual environments for VR or AR applications such as simulations, games, or training programs.

5. Security and Surveillance: Integration into security systems to analyze human traffic, recognize unusual placements, or track an individual's behavior, which could be useful in both retail and security applications.

Here are a few use cases for this project:

1. Retail Analytics: You can use the "MachLearn" model to analyze consumer behavior within various store classes. By identifying and classifying different store classes, a business can gain insights on customer habits, popular areas of the store, time spent per store class, etc., which can help optimize store layout and product placement.

2. Safety and Security: The model can be used to monitor activity in public places like stores to detect any unusual activities (e.g., overcrowded stores, long queues, etc.). This can also support theft detection and prevention initiatives.

3. Urban Planning: City planners and governments can utilize this model for research and planning. By classifying stores, they can get an overview of the local business environment and strategically plan for more balanced economic development, including the allocation of resources and infrastructure.

4. Real-Time Store Classification: App developers can use this model for real-time location-based services. Users could get information on what kind of stores are around them just by pointing their camera around—useful for tourists, newcomers, or even local residents.

5. Commercial Real Estate: Real estate agencies and investors can utilize this model to identify and classify various store types in different neighborhoods or areas. This can help in determining commercial property values, predicting future trends, and making investment decisions.

The global home improvement retail market is poised for significant growth, with a projected CAGR of XX% during the forecast period 2025-2033. The market size is estimated at XXX million in 2025 and is expected to reach XXX million by 2033, driven by rising disposable income, increasing awareness of home improvement projects, and urbanization. The private home segment holds the largest market share due to the increasing preference for homeowners to enhance the quality of their homes and create a comfortable living space. Key industry players include The Home Depot, Inc., Lowe's Companies, Inc., and Groupe Adeo SA, among others. Factors propelling market growth include the surge in DIY (Do-it-yourself) home improvement projects, government incentives for home renovations, and the growing popularity of online home improvement platforms. However, economic downturns, the volatility of raw material prices, and supply chain disruptions pose challenges to market expansion. Regional variations in market dynamics are also evident, with North America and Europe dominating the market, while Asia-Pacific is expected to witness substantial growth in the coming years. The home improvement retail market encompasses a diverse range of businesses that cater to homeowners and construction professionals by providing building materials, home appliances, tools, and furnishings. Key market players include The Home Depot, Lowe's Companies, Inc., Groupe Adeo SA, Kingfisher plc, and others.

Video-based People Counting System Market Outlook

The global video-based people counting system market size was valued at $1.8 billion in 2023 and is expected to reach $4.2 billion by 2032, growing at a CAGR of 9.8% during the forecast period from 2024-2032. This significant growth can be attributed to the increasing adoption of advanced video analytics technologies across various sectors such as retail, transportation, and hospitality, as well as the growing need for efficient crowd management solutions in large public venues.

The video-based people counting system market is experiencing rapid growth due to the rising demand for accurate footfall measurement and analytics in retail environments. Retailers are increasingly adopting these systems to gain deeper insights into customer behavior, optimize store layouts, and enhance the overall shopping experience. Furthermore, advancements in artificial intelligence and machine learning are making these systems more precise and capable of providing real-time data, which is crucial for making informed business decisions. Additionally, the integration of these systems with other business intelligence tools is driving their widespread adoption.

Another major driver for the growth of the video-based people counting system market is the increasing emphasis on security and safety in transportation hubs such as airports, train stations, and bus terminals. These systems offer real-time monitoring and crowd control, which are essential for preventing overcrowding, ensuring smooth operations, and maintaining a high level of security. The use of thermal imaging technology in these systems is particularly gaining traction as it enables accurate counting in varying light conditions, thereby enhancing their reliability and effectiveness.

The hospitality and entertainment sectors are also significant contributors to the market growth. Hotels, resorts, and event organizers are leveraging video-based people counting systems to manage guest flow, optimize staff allocation, and improve service delivery. In sports and entertainment venues, these systems help manage large crowds during events, ensuring safety and enhancing the overall visitor experience. The ability to analyze visitor data and patterns helps these industries improve their operational efficiency and customer satisfaction.

The integration of a Video Analytics System within the video-based people counting market is revolutionizing how data is processed and utilized. These systems leverage advanced algorithms to not only count individuals but also analyze behavioral patterns and predict trends. By employing sophisticated video analytics, businesses can gain deeper insights into customer interactions and preferences, leading to more informed decision-making. This technology is particularly beneficial in sectors like retail and transportation, where understanding crowd dynamics can significantly enhance operational efficiency and customer satisfaction. As the demand for real-time analytics grows, the role of video analytics systems becomes increasingly pivotal in shaping the future of people counting solutions.

Regionally, North America holds a significant share of the video-based people counting system market due to the high adoption rate of advanced technologies and the presence of major market players. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by rapid urbanization, increasing investments in smart city projects, and the rising importance of retail analytics in emerging economies such as China and India. Europe also represents a substantial market share, with growing applications in retail and transportation sectors.

Component Analysis

The component segment of the video-based people counting system market is categorized into hardware, software, and services. The hardware segment includes cameras, sensors, and other physical devices used to capture video footage and detect people. This segment is expected to hold a significant share of the market, driven by the increasing deployment of advanced cameras with higher resolution and enhanced features such as thermal imaging and 3D depth sensing. The demand for robust and reliable hardware components is crucial for the accurate functioning of these systems.

The software segment is anticipated to witness substantial growth during the forecast period. This segment encompasses the various software soluti

This is a realistic and structured pizza sales dataset covering the time span from **2024 to 2025. ** Whether you're a beginner in data science, a student working on a machine learning project, or an experienced analyst looking to test out time series forecasting and dashboard building, this dataset is for you.

📁 What’s Inside? The dataset contains rich details from a pizza business including:

✅ Order Dates & Times ✅ Pizza Names & Categories (Veg, Non-Veg, Classic, Gourmet, etc.) ✅ Sizes (Small, Medium, Large, XL) ✅ Prices ✅ Order Quantities ✅ Customer Preferences & Trends

It is neatly organized in Excel format and easy to use with tools like Python (Pandas), Power BI, Excel, or Tableau.

💡** Why Use This Dataset?** This dataset is ideal for:

📈 Sales Analysis & Reporting 🧠 Machine Learning Models (demand forecasting, recommendations) 📅 Time Series Forecasting 📊 Data Visualization Projects 🍽️ Customer Behavior Analysis 🛒 Market Basket Analysis 📦 Inventory Management Simulations

🧠 Perfect For: Data Science Beginners & Learners BI Developers & Dashboard Designers MBA Students (Marketing, Retail, Operations) Hackathons & Case Study Competitions

pizza, sales data, excel dataset, retail analysis, data visualization, business intelligence, forecasting, time series, customer insights, machine learning, pandas, beginner friendly

The global retail accounting software market is projected to reach a value of 1022.8 million USD by 2033, exhibiting a CAGR of 3.6% during the forecast period (2025-2033). Key drivers of this growth include the increasing adoption of cloud-based solutions, the need for improved financial visibility, and the growing complexity of retail operations. Additionally, the rising adoption of mobile devices and the integration of artificial intelligence (AI) and machine learning (ML) into accounting software are also driving market expansion. The market is segmented into two primary types: cloud-based and on-premises. The cloud-based segment is expected to dominate the market throughout the forecast period due to its scalability, cost-effectiveness, and ease of deployment. In terms of application, the market is divided into large enterprises and SMEs. Large enterprises are expected to hold a larger share of the market due to their greater need for advanced accounting capabilities and data analytics. The market also includes a diverse range of solution providers, including Square, Lightspeed, Oracle, Fishbowl, and QuickBooks, among others. This report provides an in-depth analysis of the global retail accounting software market, offering valuable insights into market dynamics, key trends, and competitive landscape. The report covers the period from 2023 to 2030 and projects future market developments based on extensive research and expert analysis.

Snapshot img

Geographic Information System Analytics Market Size 2024-2028

The geographic information system analytics market size is forecast to increase by USD 12 billion at a CAGR of 12.41% between 2023 and 2028.

The GIS Analytics Market analysis is experiencing significant growth, driven by the increasing need for efficient land management and emerging methods in data collection and generation. The defense industry's reliance on geospatial technology for situational awareness and real-time location monitoring is a major factor fueling market expansion. Additionally, the oil and gas industry's adoption of GIS for resource exploration and management is a key trend. Building Information Modeling (BIM) and smart city initiatives are also contributing to market growth, as they require multiple layered maps for effective planning and implementation. The Internet of Things (IoT) and Software as a Service (SaaS) are transforming GIS analytics by enabling real-time data processing and analysis.
Augmented reality is another emerging trend, as it enhances the user experience and provides valuable insights through visual overlays. Overall, heavy investments are required for setting up GIS stations and accessing data sources, making this a promising market for technology innovators and investors alike.

What will be the Size of the GIS Analytics Market during the forecast period?

Request Free Sample

The geographic information system analytics market encompasses various industries, including government sectors, agriculture, and infrastructure development. Smart city projects, building information modeling, and infrastructure development are key areas driving market growth. Spatial data plays a crucial role in sectors such as transportation, mining, and oil and gas. Cloud technology is transforming GIS analytics by enabling real-time data access and analysis. Startups are disrupting traditional GIS markets with innovative location-based services and smart city planning solutions. Infrastructure development in sectors like construction and green buildings relies on modern GIS solutions for efficient planning and management. Smart utilities and telematics navigation are also leveraging GIS analytics for improved operational efficiency.
GIS technology is essential for zoning and land use management, enabling data-driven decision-making. Smart public works and urban planning projects utilize mapping and geospatial technology for effective implementation. Surveying is another sector that benefits from advanced GIS solutions. Overall, the GIS analytics market is evolving, with a focus on providing actionable insights to businesses and organizations.

How is this Geographic Information System Analytics Industry segmented?

The geographic information system analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Retail and Real Estate
  Government
  Utilities
  Telecom
  Manufacturing and Automotive
  Agriculture
  Construction
  Mining
  Transportation
  Healthcare
  Defense and Intelligence
  Energy
  Education and Research
  BFSI


Components

  Software
  Services


Deployment Modes

  On-Premises
  Cloud-Based


Applications

  Urban and Regional Planning
  Disaster Management
  Environmental Monitoring Asset Management
  Surveying and Mapping
  Location-Based Services
  Geospatial Business Intelligence
  Natural Resource Management


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    South Korea


  Middle East and Africa

    UAE


  South America

    Brazil


  Rest of World

By End-user Insights

The retail and real estate segment is estimated to witness significant growth during the forecast period.

The GIS analytics market analysis is witnessing significant growth due to the increasing demand for advanced technologies in various industries. In the retail sector, for instance, retailers are utilizing GIS analytics to gain a competitive edge by analyzing customer demographics and buying patterns through real-time location monitoring and multiple layered maps. The retail industry's success relies heavily on these insights for effective marketing strategies. Moreover, the defense industries are integrating GIS analytics into their operations for infrastructure development, permitting, and public safety. Building Information Modeling (BIM) and 4D GIS software are increasingly being adopted for construction project workflows, while urban planning and designing require geospatial data for smart city planning and site selection.

The oil and gas industry is leveraging satellite imaging and IoT devices for land acquisition and mining operations. In the public sector,