Analysis of ‘CPM13 - Consumer Price Index’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/2ef22196-72e6-4969-a85c-d06e3d94eb3a on 19 January 2022.

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

Consumer Price Index

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

Analysis of ‘CPM17 - Consumer Price Index Goods and Services’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/6f0e0a2d-892e-4cfc-ad08-d9f71ed64a53 on 13 January 2022.

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

Consumer Price Index Goods and Services

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

Analysis of ‘CPM11 - Contributions to changes in the Consumer Price Index’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/aa3fa562-62ed-4299-9925-5d2b26ee4eda on 15 January 2022.

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

Contributions to changes in the Consumer Price Index

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

Analysis of ‘CPM03 - Consumer Price Index’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/a2410783-a463-448d-b0ef-e0816a9aa955 on 15 January 2022.

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

Consumer Price Index

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

Snapshot img

Retail Analytics Market Size 2025-2029

The retail analytics market size is forecast to increase by USD 28.47 billion, at a CAGR of 29.5% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing volume and complexity of data generated by retail businesses. This data deluge offers valuable insights for retailers, enabling them to optimize operations, enhance customer experience, and make data-driven decisions. However, this trend also presents challenges. One of the most pressing issues is the increasing adoption of Artificial Intelligence (AI) in the retail sector. While AI brings numerous benefits, such as personalized marketing and improved supply chain management, it also raises privacy and security concerns among customers.
Retailers must address these concerns through transparent data handling practices and robust security measures to maintain customer trust and loyalty. Navigating these challenges requires a strategic approach, with a focus on data security, customer privacy, and effective implementation of AI technologies. Companies that successfully harness the power of retail analytics while addressing these challenges will gain a competitive edge in the market.

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

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, driven by the constant need for businesses to gain insights from their data and adapt to shifting consumer behaviors. Entities such as text analytics, data quality, price optimization, customer journey mapping, mobile analytics, time series analysis, regression analysis, social media analytics, data mining, historical data analysis, and data cleansing are integral components of this dynamic landscape. Text analytics uncovers hidden patterns and trends in unstructured data, while data quality ensures the accuracy and consistency of information. Price optimization leverages historical data to determine optimal pricing strategies, and customer journey mapping provides insights into the customer experience.

Mobile analytics caters to the growing number of mobile shoppers, and time series analysis identifies trends and patterns over time. Regression analysis uncovers relationships between variables, social media analytics monitors brand sentiment, and data mining uncovers hidden patterns and correlations. Historical data analysis informs strategic decision-making, and data cleansing prepares data for analysis. Customer feedback analysis provides valuable insights into customer satisfaction, and association rule mining uncovers relationships between customer behaviors and purchases. Predictive analytics anticipates future trends, real-time analytics delivers insights in real-time, and market basket analysis uncovers relationships between products. Data security safeguards sensitive information, machine learning (ML) and artificial intelligence (AI) enhance data analysis capabilities, and cloud-based analytics offers flexibility and scalability.

Business intelligence (BI) and open-source analytics provide comprehensive data analysis solutions, while inventory management and supply chain optimization streamline operations. Data governance ensures data is used ethically and effectively, and loyalty programs and A/B testing optimize customer engagement and retention. Seasonality analysis accounts for seasonal trends, and trend analysis identifies emerging trends. Data integration connects disparate data sources, and clickstream analysis tracks user behavior on websites. In the ever-changing retail landscape, these entities are seamlessly integrated into retail analytics solutions, enabling businesses to stay competitive and adapt to evolving market dynamics.

How is this Retail Analytics Industry segmented?

The retail analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  In-store operation
  Customer management
  Supply chain management
  Marketing and merchandizing
  Others


Component

  Software
  Services


Deployment

  Cloud-based
  On-premises


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The in-store operation segment is estimated to witness significant growth during the forecast period. In the realm of retail, the in-store operation segment of the market plays a pivotal role in optimizing brick-and-mortar retail operations. This segment encompasses various data analytics applications with

Analysis of ‘Consumer Price Index — Mainland ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/5ae9a5cac8d8c915d5faa629 on 18 January 2022.

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

Consumer Price Index. Base 100=2008. MAINLAND. Note: All figures are rounded to three decimal places for publication. (1) Classification of Individual Consumption by Objective.

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

Analysis of ‘Comment on Rulemaking: Title V Consumer Price Index 2016’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/b0137e3e-c5a7-4176-83cb-ca05b3f5faaa on 26 January 2022.

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

DEQ proposes rules to increase Title V operating permit fees by the change in the consumer price index as authorized by federal and state law. The proposed fee increases are necessary for DEQ to provide essential services associated with Oregon’s Title V permitting program.

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

Analysis of ‘Groceries dataset for Market Basket Analysis(MBA)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/rashikrahmanpritom/groceries-dataset-for-market-basket-analysismba on 13 November 2021.

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

The initial dataset was collected from Groceries dataset. Then data was modified and fragmented into 2 datasets for ease of MBA implementation. Here the "groceries data.csv" contains groceries transaction data from which you can do EDA and pre-process the data to feed it in the apriori algorithm. But I have also added pre-processed data as "basket.csv" from which you'll just need to replace nan and encode it using TransactionEncoder after that you can feed the encoded data into the apriori algorithm.

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

Logistics Basket Market Outlook

The global logistics basket market size is anticipated to grow significantly from USD 1.2 trillion in 2023 to approximately USD 2.5 trillion by 2032, registering a CAGR of 8.1% during the forecast period. The key growth factors driving this expansion include the rapid globalization of trade, advancements in logistics technology, and increasing e-commerce activities worldwide.

One of the primary growth drivers of the logistics basket market is the increasing globalization of trade. As companies expand their operations across borders, the demand for efficient and reliable logistics services has surged. This trend is further fueled by the reduction in trade barriers and the creation of free trade agreements, which simplify the process of cross-border trade. Additionally, the growth in international trade agreements has led to an increase in the movement of goods, thereby boosting the demand for comprehensive logistics solutions.

Another significant factor contributing to the market growth is the advancement in logistics technology. The integration of cutting-edge technologies such as Artificial Intelligence (AI), Internet of Things (IoT), blockchain, and automation in the logistics sector has revolutionized the industry. These technologies enhance operational efficiency, reduce costs, and improve supply chain visibility and tracking. Companies are increasingly adopting these innovations to streamline their logistics processes, which in turn drives the growth of the logistics basket market.

The burgeoning e-commerce sector is also a major catalyst for the growth of the logistics basket market. The rise of online shopping platforms has led to an exponential increase in the volume of goods being transported. E-commerce companies require efficient logistics services to ensure the timely delivery of products to customers, driving the demand for transportation, warehousing, and distribution services. The need for last-mile delivery solutions has also grown, further propelling the market expansion.

Regionally, the Asia Pacific region is expected to dominate the logistics basket market during the forecast period, followed by North America and Europe. The rapid industrialization and urbanization in countries like China and India, coupled with the rising disposable income, have led to an increased demand for goods and, consequently, logistics services. Additionally, the presence of major e-commerce companies in the region, such as Alibaba and Flipkart, further fuels the market growth. North America and Europe are also witnessing significant growth due to the high adoption of advanced logistics technologies and a well-established infrastructure.

Service Type Analysis

In the logistics basket market, service types can be broadly categorized into transportation, warehousing, distribution, inventory management, and others. Transportation services form a substantial portion of the market, primarily driven by the increasing need to move goods efficiently from one location to another. Within transportation, the demand for different modes such as roadways, railways, airways, and waterways varies based on factors like cost, distance, and the nature of goods. As logistics providers strive to offer more cost-effective and timely solutions, transportation services continue to evolve with the integration of technologies like GPS tracking and route optimization algorithms.

Warehousing services are another critical component of the logistics basket market. The rise in global trade and e-commerce has led to a significant increase in the demand for warehousing solutions. Companies require strategically located warehouses to store goods before they are distributed to the final destination. Advanced warehousing solutions, including automated storage and retrieval systems, have significantly improved the efficiency of operations. Additionally, the trend of on-demand warehousing is gaining traction, allowing companies to scale their storage needs according to demand fluctuations, hence optimizing costs.

Distribution services, a crucial link in the supply chain, ensure that products reach their intended destinations efficiently. The growth in e-commerce has notably increased the volume of goods requiring distribution. Companies are investing in sophisticated distribution networks that can handle bulk shipments and manage last-mile deliveries efficiently. With the advent of technology, distribution services now offer better tracking capabilities, providing real-time updates and ensuring transparency throughout the delivery proc

The global smart medicine basket dispensing machine market is experiencing robust growth, driven by the increasing demand for efficient medication management in healthcare settings. The market's expansion is fueled by several key factors, including the rising prevalence of chronic diseases requiring complex medication regimens, the increasing focus on reducing medication errors, and the growing adoption of automated systems in hospitals, pharmacies, and nursing facilities. Fully automatic systems are gaining significant traction due to their enhanced accuracy, speed, and efficiency compared to semi-automatic counterparts. The market is segmented geographically, with North America and Europe currently holding significant market share due to advanced healthcare infrastructure and higher adoption rates of advanced technologies. However, rapidly developing economies in Asia-Pacific, particularly China and India, are projected to witness substantial growth in the coming years, driven by increasing healthcare investments and rising disposable incomes. This growth is further facilitated by technological advancements leading to more compact, user-friendly, and cost-effective dispensing machines. The market faces some restraints, primarily related to the high initial investment cost associated with implementing these systems and the need for specialized training for personnel. However, the long-term benefits in terms of improved patient safety and reduced operational costs are outweighing these initial barriers. Looking forward, the market is poised for continued expansion. The integration of smart technologies, such as AI-powered inventory management and remote monitoring capabilities, is expected to further enhance the functionality and appeal of these machines. This will attract further investment and innovation in the sector. The increasing demand for personalized medicine and the growing adoption of telehealth will also contribute significantly to the market's growth trajectory. Competitive players are focused on expanding their product portfolios, forging strategic partnerships, and entering new geographical markets to capitalize on this burgeoning opportunity. Companies like Intergy, Shenzhen Ruichi, and Swisslog Healthcare are leading the charge, constantly innovating and improving their offerings to remain competitive. The forecast period of 2025-2033 is anticipated to witness substantial growth, driven by the aforementioned factors.

Analysis of ‘CPM02 - Consumer Price Index’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/eab35d8b-9add-4cd2-930d-04bf4427322d on 13 January 2022.

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

Consumer Price Index

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

Analysis of ‘🚊 Consumer Price Index’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/consumer-price-indexe on 13 February 2022.

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

About this dataset

9The Consumer Price Index for All Urban Consumers: All Items (CPIAUCSL) is a measure of the average monthly change in the price for goods and services paid by urban consumers between any two time periods.(1) It can also represent the buying habits of urban consumers. This particular index includes roughly 88 percent of the total population, accounting for wage earners, clerical workers, technical workers, self-employed, short-term workers, unemployed, retirees, and those not in the labor force.(1)

The CPIs are based on prices for food, clothing, shelter, and fuels; transportation fares; service fees (e.g., water and sewer service); and sales taxes. Prices are collected monthly from about 4,000 housing units and approximately 26,000 retail establishments across 87 urban areas.(1) To calculate the index, price changes are averaged with weights representing their importance in the spending of the particular group. The index measures price changes (as a percent change) from a predetermined reference date.(1) In addition to the original unadjusted index distributed, the Bureau of Labor Statistics also releases a seasonally adjusted index. The unadjusted series reflects all factors that may influence a change in prices. However, it can be very useful to look at the seasonally adjusted CPI, which removes the effects of seasonal changes, such as weather, school year, production cycles, and holidays.(1)

The CPI can be used to recognize periods of inflation and deflation. Significant increases in the CPI within a short time frame might indicate a period of inflation, and significant decreases in CPI within a short time frame might indicate a period of deflation. However, because the CPI includes volatile food and oil prices, it might not be a reliable measure of inflationary and deflationary periods. For a more accurate detection, the core CPI (Consumer Price Index for All Urban Consumers: All Items Less Food & Energy [CPILFESL]) is often used. When using the CPI, please note that it is not applicable to all consumers and should not be used to determine relative living costs.(1) Additionally, the CPI is a statistical measure vulnerable to sampling error since it is based on a sample of prices and not the complete average.(1)

Attribution: US. Bureau of Labor Statistics from The Federal Reserve Bank of St. Louis

For more information on the consumer price indexes, see:

• (1) Bureau of Economic Analysis. “CPI Detailed Report.” 2013
• (2) Handbook of Methods
• (3) Understanding the CPI: Frequently Asked Questions

This dataset was created by Finance and contains around 900 samples along with Consumer Price Index For All Urban Consumers: All Items, Title:, technical information and other features such as: - Consumer Price Index For All Urban Consumers: All Items - Title: - and more.

How to use this dataset

• Analyze Consumer Price Index For All Urban Consumers: All Items in relation to Title:
• Study the influence of Consumer Price Index For All Urban Consumers: All Items on Title:
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Finance

Start A New Notebook!

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

Analysis of ‘Market Basket Analysis Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ahmtcnbs/datasets-for-appiori on 28 January 2022.

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

Market Basket Analysis Apriori algorithm is given by R. Agrawal and R. Srikant in 1994 for finding frequent itemsets in a dataset for boolean association rule. Name of the algorithm is Apriori because it uses prior knowledge of frequent itemset properties. We apply an iterative approach or level-wise search where k-frequent itemsets are used to find k+1 itemsets.

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

Analysis of ‘Consumer Price Index: Evolution of the CPI in Navarre (General and by groups)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/https-datosabiertos-navarra-es-dataset-indice-de-precios-de-consumo-evoluci-n-del-ipc-en-navarra-general-y-por-grupos- on 18 January 2022.

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

consumer Price Index: Evolution of the CPI in Navarre (General and by groups)

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

Analysis of ‘Consumer Price Index Schleswig-Holstein December 2021’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/stanord_cms-63583 on 16 January 2022.

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

Consumer Price Index Schleswig-Holstein December 2021

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

Analysis of ‘Groceries Market Basket Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/irfanasrullah/groceries on 28 January 2022.

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

Context The Groceries Market Basket Dataset, which can be found here. The dataset contains 9835 transactions by customers shopping for groceries. The data contains 169 unique items.
The data is suitable to do data mining for market basket analysis which has multiple variables.

Acknowledgement Thanks to https://github.com/shubhamjha97/association-rule-mining-apriori
The data is under course Association rules mining using Apriori algorithm. Course Assignment for CS F415- Data Mining @ BITS Pilani, Hyderabad Campus. Done under the guidance of Dr. Aruna Malapati, Assistant Professor, BITS Pilani, Hyderabad Campus.

Pre-processing

The csv file was read transaction by transaction and each transaction was saved as a list. A mapping was created from the unique items in the dataset to integers so that each item corresponded to a unique integer. The entire data was mapped to integers to reduce the storage and computational requirement. A reverse mapping was created from the integers to the item, so that the item names could be written in the final output file.

Don't forget to upvote before you download :)

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

Analysis of ‘E-Commerce Sales Dataset 🛒’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/berkayalan/ecommerce-sales-dataset on 28 January 2022.

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

Context

This dataset contains sales details of an E-Commerce platform. It covers 20.000 unique customers and 150.000 basket transactions.

Datasets

Customer Details

• customer_id: Unique ID given to the Customer.
• sex: Sex of customer.
• customer_age: Age of customer.
• tenure: tenure of customer as month

Basket Details

• customer_id: Unique ID given to the Customer.
• product_id: Unique ID of the product which has been added to the basket by the customer.
• basket_date: Date of transaction.
• basket_count: How many times this event occured during this date?

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

Analysis of ‘Grocery Store Prices, Mongolia’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/robertritz/ub-market-prices on 28 January 2022.

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

Context

The National Statistics Office of Mongolia goes to each major market to record food prices each week in Ulaanbaatar, the capital city of Mongolia. The main purpose for this is to monitor a common basket of goods for use in consumer price index (CPI) calculations.

Content

The data is in a long-form, with date, market, product, and price recorded. All prices are in Mongolian Tugriks. As of 2021 the USD to MNT is about 2850 MNT = 1 USD.

Acknowledgements

This dataset is possible thanks to the hard work of the people of the National Statistics Office of Mongolia.

Inspiration

Often people choose supermarkets over the open markets (called a "zakh"). Mostly this is for convenience, but it is notable how much money people could save by choosing a different market!

This would be a great dataset for EDA or looking at how prices change over time.

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

Analysis of ‘Bakery Sales Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/akashdeepkuila/bakery on 28 January 2022.

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

Context

We live in the era of e-commerce and digital marketing. We have even small scale businesses going online as the opportunities are endless. Since a huge chunk of the people who have access to internet is switching to online shopping, large retailers are actively searching for ways to increase their profit. Market Basket analysis is one such key techniques used by large retailers to to increase sales by understanding the customers' purchasing behavior & patterns. Market basket analysis examines collections of items to find relationships between items that go together within the business context.

Content

The dataset belongs to "The Bread Basket" a bakery located in Edinburgh. The dataset provide the transaction details of customers who ordered different items from this bakery online during the time period from 26-01-11 to 27-12-03. The dataset has 20507 entries, over 9000 transactions, and 4 columns.

Variables

• TransactionNo : unique identifier for every single transaction
• Items : items purchased
• DateTime : date and time stamp of the transactions
• Daypart : part of the day when a transaction is made (morning, afternoon, evening, night)
• DayType : classifies whether a transaction has been made in weekend or weekdays

Inspiration

The dataset is ideal for anyone looking to practice association rule mining and understand the business context of data mining for better understanding of the buying pattern of customers.

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