The awareness among worldwide consumers about companies selling their personal data to third parties has grown in recent years. As of July 2022, three in four consumers in selected countries worldwide said they knew that companies sell personal information. In comparison, in 2020, this share was a little over 60 percent.

Although a majority of internet users aged between 18 and 75 years in the United Kingdom (UK) are still skeptical when it comes to personal data being collected by companies, a small share (** percent) would be willing to share this data in return for financial compensation. These types of data mainly included purchase history and location data, while a slightly smaller percentage stated they were willing to sell their browsing history and online media consumption to companies.

According to data collected in April 2022 in the United States, Telegram was the alternative social media platform that claimed to provide necessary privacy settings and a conscious approach to handling user data. Rumble appeared to have none of the mentioned online privacy control options among all the platforms. Gab and Parler were relatively neutral, claiming they wouldn't sell user data or have targeted third-party ads at the time of the research.

Goals :

1. Sales Analysis:

Sales data forms the backbone of this dataset, and it allows users to delve into various aspects of sales performance.

2. Product Analysis:

Each product in this dataset comes with its unique identifier (StockCode) and its name (Description).

3. Customer Segmentation:

If you associated specific business logic onto the transactions (such as calculating total amounts), then you could use standard machine learning methods or even RFM (Recency, Frequency, Monetary) segmentation techniques combining it with 'CustomerID' for your customer base to understand customer behavior better.

4. Geographical Analysis:

The Country column enables analysts to study purchase patterns across different geographical locations.

5. Sales Performance Dashboard:

To track the sales performance of the online retail company, a sales performance dashboard can be created. This dashboard can include key metrics such as total sales, sales by product category, sales by customer segment, and sales by geographical location. By visualizing the sales data in an interactive dashboard, it becomes easier to identify trends, patterns, and areas for improvement.

Research Ideas ****:

1. Inventory Management: By analyzing the quantity and frequency of product sales, retailers can effectively manage their stock and predict future demand. This would help ensure that popular items are always available while less popular items aren't overstocked.
2. Customer Segmentation: Data from different countries can be used to understand buying habits across different geographical locations. This will allow the retail company to tailor its marketing strategy for each specific region or country, leading to more effective advertising campaigns.
3. Sales Trend Analysis: With data spanning almost a year, temporal patterns in purchasing behavior can be identified, including seasonality and other trends (like an increase in sales during holidays). Techniques like time-series analysis could provide insights into peak shopping times or days of the week when sales are typically high.
4. Predictive Analysis for Cross-Selling & Upselling: Based on a customer's previous purchase history, predictive algorithms can be utilized to suggest related products that might interest the customer, enhancing upsell and cross-sell opportunities.
5. Detecting Fraud: Analysing sale returns (marked with 'c' in InvoiceNo) across customers or regions could help pinpoint fraudulent activities or operational issues leading to those returns
6. RFM Analysis: By using the RFM (Recency, Frequency, Monetary) segmentation technique, the online retail company can gain insights into customer behavior and tailor their marketing strategies accordingly.

**************Steps :**************

1. Data manipulation and cleaning from raw data using SQL language Google Big Query
2. Data filtering, grouping, and slicing
3. Data Visualization using Tableau
4. Data visualization analysis and result

In order to enable members of a socio-technical evolutionary-teal organization to design their technical component, we conducted a workshop that structures the collaboration between technical trained participants and non-trained participants. The workshop aims to transform "vague needs" into technical descriptions in the form of user stories.

The workshop is the second part of series of workshops all limited to two hours. It uses the methods of Design Thinking and Participatory Design.

The workshop has been recorded in video and this data set contains a textual German transcript, transcripts of the moderation cards that have been created during the workshop, invitations of the participants, additional material needed to conduct the workshop (timetable, moderation concept, etc.), and material used during the workshop by the participants (leaflets, slides, etc.).

We hope that the material can be used to (a) comprehend the interpretation used in our qualitative research, (b) to adapt the workshop model by other volunteers of our case study Viva con Agua de St. Pauli e.V. (https://www.vivaconagua.org/), and (c) investigate other interesting research questions.

This child item describes R code used to determine whether public-supply water systems buy water, sell water, both buy and sell water, or are neutral (meaning the system has only local water supplies) using water source information from a proprietary dataset from the U.S. Environmental Protection Agency. This information was needed to better understand public-supply water use and where water buying and selling were likely to occur. Buying or selling of water may result in per capita rates that are not representative of the population within the water service area. This dataset is part of a larger data release using machine learning to predict public supply water use for 12-digit hydrologic units from 2000-2020. Output from this code was used as an input feature variable in the public supply water use machine learning model. This page includes the following files: ID_WSA_04062022_Buyers_Sellers_DR.R - an R script used to determine whether a public-supply water service area buys water, sells water, or is neutral BuySell_readme.txt - a README text file describing the script

A 2021 poll conducted among privacy experts worldwide showed that ** percent of companies collected personal data of subjects living in the EU, while ** percent of firms did the same for individuals living in Canada. A further ** percent of survey respondents stated that their companies collected personal data from identifiable subjects in the United Kingdom.

According to Cognitive Market Research, the global Data Exchange Platform Services Market size was USD XX million in 2024 and will expand at a compound annual growth rate (CAGR) of XX% from 2024 to 2033.

North America held largest share of XX% in the year 2024 
Europe held share of XX% in the year 2024 
Asia-Pacific held significant share of XX% in the year 2024 
South America held significant share of XX% in the year 2024
Middle East and Africa held significant share of XX% in the year 2024 

Market Dynamics of the Data Exchange Platform Service Market:

Key Drivers for the Data Exchange Platform Service Market

Businesses Are Increasingly Requiring Third-Party Data to Analyse Consumer Purchase Behavior and the Market which las led to the growth of the market 

The market is experiencing an increase in demand for third-party data, which is being met by data exchange platform services. This data ranges from traffic and financial data to climatic, geographic, and streaming sensor data. In order to enhance their statistical and machine learning models, data scientists and researchers are always searching for new sources of data. Third-party data, including as demographic, psychographic, and social media information, is needed by market researchers in a variety of domains to enhance analysis, predictions, and plans and to build 360-degree perspectives of their clientele. Furthermore, big companies are already requesting clickstream data in order to, among other things, personalize user experiences and develop engaging suggestion engines. For instance, in January 2020, IBM Corporation and Yara International worked together to create an open data sharing platform that can help with field and farm data collaboration, allowing more food to be produced globally while leaving a reduced environmental impact. It is anticipated that demand for data exchange platform services will continue to grow during the forecast period due to intensifying competition and platform service providers' rush to create premium features. In order to enable data consumers to quickly survey, purchase, upload, and query such data sets, businesses are increasingly working to simplify the process for data providers to package, distribute, sell, protect, and manage data assets. Unquestionably, an uncontested data exchange platform fosters development for all parties involved—data operators, suppliers, and customers—and is easier to market and use. Throughout the forecast period, all of these factors will be propelling the worldwide data exchange platform services market.

Restraints for the Data Exchange Platform Service Market

High initial costs for Data Exchange Platform Services may hamper the growth of the market 

Initial installation costs for demand planning solution programs might be high. They also incur additional expenditures associated with upkeep. Furthermore, organizations may be compelled to boost their expenditures for staff training on how to use the systems, in addition to spending on information technology (IT) infrastructure within the company. These challenges may impede Data Exchange Platform Services market growth throughout the projection period, particularly for small and medium-sized businesses. Without internal knowledge or technical resources, the costs for gear purchases, implementation fees, and software licensing can be prohibitive. Furthermore, continuing maintenance, such as repairs, training expenses, and IT assistance, may put further strain on already limited funds Market Overview of the Data Exchange Platform Services Market

Data Exchange Platform Services are often valuable for marketers, developers, website owners, and UI/UX professionals. It collects mouse motions such as scrolling, highlighting, typing, keypresses, heatmaps, and funnels, which assist to improve the efficiency of an application or website and obtain greater conversion rates. A replay solution delivers intangible facts for users who encounter difficult challenges when visiting a website. It helps to identify issues, eradicate them, and provide a smoother online experience. Furthermore, it aids in inspecting possible consumer behavior, better investigating customer wants, and adjusting web design layouts. A session replay tool lets the customer support staff fix difficulties in real-time using heatmap analysis, which reveals...

Data Monetization Market size was valued at USD 3.5 Billion in 2023 and is projected to reach USD 8.5 Billion by 2030, growing at a CAGR of 20.3% during the forecast period 2024-2030.Global Data Monetization Market DriversThe growth and development of the Data Monetization Market are attributed to certain main market drivers. These factors have a big impact on how integrated gas systems are demanded and adopted in different sectors. Several of the major market forces are as follows:Increasing Data Volume: As digital technologies have spread widely, the amount of data produced by organizations, people, and networked devices has increased exponentially. Organizations have the opportunity to monetize their data assets due to the volume of data.Advanced Analytics and Data Technologies: Organisations may now extract meaningful insights from their data thanks to developments in analytics techniques like machine learning and artificial intelligence. These insights can be made profitable in a number of ways, such by providing data-driven goods and services or specialized advertising.A Greater Attention to Data Monetization Strategies: Companies are aggressively looking for ways to monetize their data assets as they become more and more aware of their worth. This entails creating plans for how to market, package, and sell data to third parties or how to create value by streamlining decision-making procedures.Regulatory Environment: Organisations are being prompted to investigate compliant methods of monetizing their data assets by regulatory frameworks like the CCPA and GDPR, which have raised awareness regarding data protection and security. Businesses who are involved in data monetization operations must take compliance with these requirements into account.Data marketplaces are becoming more and more popular, offering venues for the purchase, sale, and exchange of data assets. By facilitating trades between users and data producers, these markets increase accessibility and liquidity within the ecosystem of data monetization.Industry Convergence and Partnerships: In order to take advantage of one another's data assets for mutual gain, industries are working together more and more and establishing partnerships. Collaborations across industries help businesses generate new revenue streams and develop creative data-driven solutions.Demand for Personalised Experiences: Customers are coming to expect more and more from companies in a variety of industries when it comes to personalized experiences. Through data monetization, businesses can use consumer information to create customized goods, services, and advertising campaigns that increase client happiness and loyalty.

eBay is an e-commerce platform, at first only providing customer-to-customer auction services, expanding into business-to-consumer shortly afterwards. In the 2000’s, eBay went on a spending...

Note:- Only publicly available data can be worked upon

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Data collected about Trade Credit Policy in Brazil (considering just SMEs). Our hypothesis are basically the following:

Solving uncertainties for the buyer: H1: Companies that sell high quality, technology-based products give longer credit periods to allow the quality of the products to be checked before any actual payment is made. H2: Selling companies with less reputation give longer credit periods, when reputation is measured by way of metrics involving customer size and concentration. H3: Selling companies that have a high proportion of their external sales on credit give longer credit periods. H4: Selling companies that operate in highly seasonal markets give longer credit periods.

Solving uncertainties for the seller: H5: Using cash-on-delivery (CoD) or cash-before-delivery (CbD) payment conditions is more common when the seller: (a) is smaller; (b) sells mainly to end users; and (c) has a larger proportion of foreign sales on credit. H6: The use of two instalment terms is associated with: (a) fewer days delay; and (b) selling mainly to smaller customers.

Regarding price discrimination and trade credit policy, therefore, the following hypotheses are tested: H7a: The actual rate of interest on the immediate payment discount is positively associated with: i) the size of the selling company; ii) being one of the main players in the market; iii) adopting sales maximization (instead of risk reduction) as the main objective of credit; iv) customer concentration;
v) negotiations with large customers; vi) negotiations mainly with wholesale buyers. H7b: The actual interest rate on immediate payment discount is negatively associated with: i) negotiations, mainly with the end user; ii) the proportion of foreign sales on credit.

By using this dataset you acknowledge the following:Kansas Open Records Act StatementThe Kansas Open Records Act provides in K.S.A. 45-230 that "no person shall knowingly sell, give or receive, for the purpose of selling or offering for sale, any property or service to persons listed therein, any list of names and addresses contained in, or derived from public records..." Violation of this law may subject the violator to a civil penalty of $500.00 for each violation. Violators will be reported for prosecution.By accessing this site, the user makes the following certification pursuant to K.S.A. 45-220(c)(2): "The requester does not intend to, and will not: (A) Use any list of names or addresses contained in or derived from the records or information for the purpose of selling or offering for sale any property or service to any person listed or to any person who resides at any address listed; or (B) sell, give or otherwise make available to any person any list of names or addresses contained in or derived from the records or information for the purpose of allowing that person to sell or offer for sale any property or service to any person listed or to any person who resides at any address listed."

The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim is to build a predictive model and find out the sales of each product at a particular store.

We can separate this process into four levels: Product level, Store level, Customer level, and Macro level.

Store Level Hypotheses:

1. City type: Stores located in urban or Tier 1 cities should have higher sales because of the higher income levels of people there.
2. Population Density: Stores located in densely populated areas should have higher sales because of more demand.
3. Store Capacity: Stores which are very big in size should have higher sales as they act like one-stop-shops and people would prefer getting everything from one place
4. Competitors: Stores having similar establishments nearby should have less sales because of more competition.
5. Marketing: Stores which have a good marketing division should have higher sales as it will be able to attract customers through the right offers and advertising.
6. Location: Stores located within popular marketplaces should have higher sales because of better access to customers.
7. Customer Behavior: Stores keeping the right set of products to meet the local needs of customers will have higher sales.
8. Ambiance: Stores which are well-maintained and managed by polite and humble people are expected to have higher footfall and thus higher sales.

Product Level Hypotheses:

1. Brand: Branded products should have higher sales because of higher trust in the customer.
2. Packaging: Products with good packaging can attract customers and sell more.
3. Utility: Daily use products should have a higher tendency to sell as compared to the specific use products.
4. Display Area: Products which are given bigger shelves in the store are likely to catch attention first and sell more.
5. Visibility in Store: The location of product in a store will impact sales. Ones which are right at entrance will catch the eye of customer first rather than the ones in back.
6. Advertising: Better advertising of products in the store will should higher sales in most cases.
7. Promotional Offers: Products accompanied with attractive offers and discounts will sell more.

Develop Louisville Focuses on the full range of land development activities, including planning and design, vacant property initiatives, advanced planning, housing & community development programs, permits and licensing, land acquisition, public art and clean and green sustainable development partnerships.Data Dictionary:“LBA” is the abbreviation for the Louisville and Jefferson County LBA Authority, Inc."Parcel ID" is an identification code assigned to a piece of real estate by the Jefferson County Property Valuation Administration. The Parcel ID is used for record keeping and tax purposes.“IMPROV” stands for whether or not the real estate parcel had an “improvement” (i.e., a structure) situated on it at the time it was sold. “1” indicates that a structure existed when the parcel was sold and “0” indicates that the parcel was an empty, piece of land.“APPLICANT” is the individual(s) or active business entity that submitted an Application to Purchase the real estate parcel and whose application was presented to and approved by the LBA’s Board of Directors. The Board of Directors must approve each application before a transfer deed is officially recorded with the Office of the County Clerk of Jefferson County, Kentucky.“SALE DATE” is the date that the Applicant signed the transfer deed for the respective real estate parcel.“SALE AMOUNT” is the amount that the Applicant paid to purchase the respective real estate parcel.“SALE PROGRAM” is the LBA’s disposition program that the Applicant participated in to acquire the real estate parcel.The Office of Community Development defines each “Sale Program” as follows:Budget Rate (“Budget Rate Policy for New Construction Projects”) – Applicant submitted a proposed construction project for the empty, piece of land.Cut It Keep It - Applicant requested to maintain the empty piece of land situated on the same block as a real estate parcel owned by the Applicant. Applicant must retain ownership of the lot for three (3) years before the Applicant can sell it.Demo for Deed (“Last Look – Demo for Deed”) – Applicant requested to demolish the structure situated on the real estate parcel and retain the land for a future use.Flex Rate (“Flex Rate Policy for New Construction Projects”) – Applicant submitted a proposed construction project for the empty, piece of land but did not have proof of funding or a timeline as to when the project would be completed.Metro Redevelopment – The real estate parcel was part of a redevelopment project being considered by Metro Government.Minimum Pricing Policy – The pricing policy that was approved by the LBA’s Board of Directors and in effect as of the real estate parcel’s sale date.RFP (“Request for Proposals”) - Applicant requested to rehabilitate the structure in order to place it back into productive use within the neighborhood.Save the Structure (“Last Look – Save the Structure”) - Applicant requested to rehabilitate the structure in order to place it back into productive use within the neighborhood.Side Yard – The Applicant requested to acquire the LBA’s adjoining piece of land to make the Applicant’s occupied, real estate parcel larger and more valuable.SOI (“Solicitation of Interest”) – The LBA assembled two (2) or more real estate parcels and the Applicant submitted a redevelopment project for the subject parcels.For more information about each of the current disposition programs that the LBA offers, please refer to the following website pages:https://louisvilleky.gov/government/community-development/vacant-lot-sales-programshttps://louisvilleky.gov/government/community-development/vacant-structures-saleContact:Connie Suttonconnie.sutton@louisvilleky.gov

Snapshot img

Direct Selling Market Size 2025-2029

The direct selling market size is forecast to increase by USD 73.2 million at a CAGR of 5.3% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the increasing use of social media as a sales channel. The social media platforms have become essential tools for direct selling companies to reach and engage with customers, leading to increased sales and market expansion. Another key trend in the market is the rising demand for personalized customer experiences. Direct selling companies are responding to this trend by leveraging technology to offer customized product recommendations and tailored customer interactions, enhancing the overall shopping and social commerce experience. However, the market also faces challenges that require careful navigation.
Regulatory scrutiny and compliance are becoming increasingly important issues for direct selling companies. The governments around the world are increasing their focus on regulating the direct selling industry, with stricter rules regarding product safety, labeling, and marketing practices. Companies must invest in compliance efforts to avoid potential legal issues and maintain their reputation. These challenges, while significant, also present opportunities for companies that can effectively navigate the regulatory landscape and provide high-quality, safe products and services to customers.

What will be the Size of the Direct Selling Market during the forecast period?

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The market continues to evolve, driven by shifting consumer preferences and advances in technology. Independent consultants leverage customer service and residual income to build thriving businesses in various sectors, including personal care, home products, health and wellness, and financial services. Sales promotion and lead generation are key strategies, with trade shows and social media marketing essential for expanding customer bases. Team building and party plans facilitate growth through a multi-level marketing structure, offering flexible schedules and professional development opportunities. Customer retention remains a priority, with consumer loyalty fostered through exceptional customer relationship management and product quality.
Regulatory frameworks ensure business ethics and legal compliance. Data analytics and digital marketing tools, including mobile apps, provide valuable insights and competitive advantages. Brands continue to launch innovative products, from essential oils to weight management solutions, meeting diverse consumer needs and enhancing brand awareness. The industry association supports members with training and development, product launches, and industry news. Overall, the market's continuous dynamism offers opportunities for growth and innovation across numerous sectors.

How is this Direct Selling Industry segmented?

The direct selling 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.

Type

  Single-level marketing
  Multi-level marketing


Product

  Health and wellness
  Cosmetics and personal care
  Household goods and durables
  Others


Sales Channel

  Person-to-Person
  Online Sales
  Party Plan


End-User

  Individual Consumers
  Businesses


Geography

  North America

    US


  Europe

    France
    Germany
    UK


  APAC

    Australia
    China
    Indonesia
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The single-level marketing segment is estimated to witness significant growth during the forecast period.

Direct selling is a dynamic and evolving market where independent consultants directly connect with customers to sell a range of products and services. This model, which includes personal care, home products, cosmetics , health and wellness, financial services, and more, prioritizes customer service and relationship management. Flexible schedules enable consultants to balance their work and personal lives, making it an attractive option for many. Direct sales events such as trade shows and parties provide opportunities for lead generation and brand awareness. Business ethics are crucial in this industry, with a focus on transparency and legal compliance. Team building and training and development are essential for consultant success, fostering a collaborative and supportive environment.

Compensation plans offer residual income, ensuring consultants earn commissions on their sales volume. Sales promotions and digital marketing, including social media and mobile apps, help boost sales and customer retention. Data analytics plays a significant role in understanding consumer preferences and optimizing marketing strategi

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Philippines License to Sell Issued: Unit: Total data was reported at 511,921.000 Unit in 2017. This records an increase from the previous number of 473,387.000 Unit for 2016. Philippines License to Sell Issued: Unit: Total data is updated yearly, averaging 300,829.000 Unit from Dec 1981 (Median) to 2017, with 37 observations. The data reached an all-time high of 511,921.000 Unit in 2017 and a record low of 8,839.000 Unit in 1981. Philippines License to Sell Issued: Unit: Total data remains active status in CEIC and is reported by Housing and Land Use Regulatory Board. The data is categorized under Global Database’s Philippines – Table PH.EB003: License to Sell Issued: Annual.

Will you take vehicle insurance?

An insurance policy is an arrangement by which a company undertakes to provide a guarantee of compensation for specified loss, damage, illness, or death in return for the payment of a specified premium. A premium is a sum of money that the customer needs to pay regularly to an insurance company for this guarantee.

For example, you may pay a premium of Rs. 5000 each year for a health insurance cover of Rs. 200,000/- so that if, God forbid, you fall ill and need to be hospitalised in that year, the insurance provider company will bear the cost of hospitalisation etc. for upto Rs. 200,000. Now if you are wondering how can company bear such high hospitalisation cost when it charges a premium of only Rs. 5000/-, that is where the concept of probabilities comes in picture. For example, like you, there may be 100 customers who would be paying a premium of Rs. 5000 every year, but only a few of them (say 2-3) would get hospitalised that year and not everyone. This way everyone shares the risk of everyone else.

Just like medical insurance, there is vehicle insurance where every year customer needs to pay a premium of a certain amount to its insurance provider company so that in case of an unfortunate accident by the vehicle, the insurance provider company will provide compensation (called ‘sum assured’) to the customer.

Building a model to predict whether a customer would be interested in Vehicle Insurance is extremely helpful for the company because it can then accordingly plan its communication strategy to reach out to those customers and optimise its business model and revenue.

Now, in order to predict, whether the customer would be interested in Vehicle insurance, you have information about demographics (gender, age, region code type), Vehicles (Vehicle Age, Damage), Policy (Premium, sourcing channel) etc.

Content

id: Unique ID for the customer Gender: Gender of the customer Age :: Age of the customer driving license: 0 :: Customer does not have DL, 1 : Customer already has DL RegionCode: Unique code for the region of the customer PreviouslyInsured 1 : Customer already has Vehicle Insurance, 0 : Customer doesn't have Vehicle Insurance VehicleAge: Age of the Vehicle VehicleDamage: 1 : Customer got his/her vehicle damaged in the past, 0 : Customer: Customer didn't get his/her vehicle damaged in the past. AnnualPremium: The amount customer needs to pay as premium in the year PolicySalesChannel: Anonymised Code for the channel of outreaching to the customer ie. Different Agents, Over Mail, Over Phone, In Person, etc. Vintage: Number of Days, Customer has been associated with the company Response: 1: Customer is interested, 0 : Customer is not interested

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