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Sales Intelligence Market Size 2025-2029

The sales intelligence market size is forecast to increase by USD 4.86 billion at a CAGR of 17.6% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the increasing demand for custom-made solutions that cater to the unique needs of businesses. This trend is fueled by the rapid advancements in cloud technology, enabling real-time access to comprehensive and accurate sales data from anywhere. However, the high initial cost of implementing sales intelligence solutions can act as a barrier to entry for smaller organizations. Furthermore, regulatory hurdles impact adoption in certain industries, requiring strict compliance with data privacy regulations. With the advent of cloud computing and SaaS customer relationship management (CRM) systems, businesses are able to store and access customer information more efficiently. Moreover, the exponential growth of marketing intelligence, driven by big data and natural language processing (NLP) technologies, enables organizations to gain valuable insights from customer interactions.
Despite these challenges, the market's potential is vast, with opportunities for growth in sectors such as healthcare, finance, and retail. Companies seeking to capitalize on these opportunities must navigate these challenges effectively, investing in cost-effective solutions and ensuring regulatory compliance. By doing so, they can gain a competitive edge through improved lead generation, enhanced customer insights, and streamlined sales processes.

What will be the Size of the Sales Intelligence Market during the forecast period?

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In today's business landscape, sales intelligence has become a critical driver of revenue growth. The go-to-market strategy of companies relies heavily on predictive lead scoring and sales pipeline analysis to prioritize opportunities and optimize resource allocation. Sales operations teams leverage revenue intelligence to gain insights into sales performance and identify trends. Data quality is paramount in sales analytics dashboards, ensuring accurate sales negotiation and closing. Sales teams collaborate using sales enablement platforms, which integrate CRM systems and provide sales performance reporting. Sales process mapping and sales engagement tools enable effective communication and productivity. Conversational AI and sales automation software streamline sales outreach and prospecting efforts. Messaging and alerting features help sales teams engage with potential customers effectively, while chatbots facilitate efficient communication.
Sales forecasting models and intent data inform sales management decisions, while salesforce automation and data governance ensure data security and compliance. Sales effectiveness is enhanced through sales negotiation training and sales enablement training. The sales market is dynamic, with trends shifting towards advanced analytics and AI-driven solutions. Companies must adapt to stay competitive, focusing on data-driven strategies and continuous improvement.

How is this Sales Intelligence Industry segmented?

The sales intelligence 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.

Deployment

  Cloud-based
  On-premises


Component

  Software
  Services


Application

  Data management
  Lead management


End-user

  IT and Telecom
  Healthcare and life sciences
  BFSI
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Deployment Insights

The cloud-based segment is estimated to witness significant growth during the forecast period. In today's business landscape, sales intelligence platforms have become indispensable tools for organizations seeking to optimize their sales processes and gain a competitive edge. These solutions offer various features, including deal tracking, win-loss analysis, data mining, sales efficiency, customer journey mapping, sales process optimization, pipeline management, sales cycle analysis, revenue optimization, market research, data integration, customer segmentation, sales engagement, sales coaching, sales playbook, sales process automation, business intelligence (BI), predictive analytics, target account identification, lead generation, account-based marketing (ABM), sales strategy, sales velocity, real-time data, artificial intelligence (AI), sales insights, sales enablement content, sales enablement, sales funnel optimization, sales performance metrics, competitive intelligence, sales methodology, customer churn, and machine learning (ML) for sales forecasting and buyer person

Global Predictive Analytics Market size worth at USD 16.19 Billion in 2023 and projected to USD 113.8 Billion by 2032, with a CAGR of around 24.19% between 2024-2032.

In the fiscal year 2022, Nittetsu Mining Co., Ltd. had net sales worth approximately *** billion Japanese yen. The Tokyo-based mining company was founded in 1939 and focuses on the processing, distribution, and trade of copper and other mineral products, coal and petroleum products, as well as machinery equipment.

According to Cognitive Market Research, the global Data Mining Software market size will be USD XX million in 2025. It will expand at a compound annual growth rate (CAGR) of XX% from 2025 to 2031.

North America held the major market share for more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Europe accounted for a market share of over XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Asia Pacific held a market share of around XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Latin America had a market share of more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Middle East and Africa had a market share of around XX% of the global revenue and was estimated at a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. KEY DRIVERS

Increasing Focus on Customer Satisfaction to Drive Data Mining Software Market Growth

In today’s hyper-competitive and digitally connected marketplace, customer satisfaction has emerged as a critical factor for business sustainability and growth. The growing focus on enhancing customer satisfaction is proving to be a significant driver in the expansion of the data mining software market. Organizations are increasingly leveraging data mining tools to sift through vast volumes of customer data—ranging from transactional records and website activity to social media engagement and call center logs—to uncover insights that directly influence customer experience strategies. Data mining software empowers companies to analyze customer behavior patterns, identify dissatisfaction triggers, and predict future preferences. Through techniques such as classification, clustering, and association rule mining, businesses can break down large datasets to understand what customers want, what they are likely to purchase next, and how they feel about the brand. These insights not only help in refining customer service but also in shaping product development, pricing strategies, and promotional campaigns. For instance, Netflix uses data mining to recommend personalized content by analyzing a user's viewing history, ratings, and preferences. This has led to increased user engagement and retention, highlighting how a deep understanding of customer preferences—made possible through data mining—can translate into competitive advantage. Moreover, companies are increasingly using these tools to create highly targeted and customer-specific marketing campaigns. By mining data from e-commerce transactions, browsing behavior, and demographic profiles, brands can tailor their offerings and communications to suit individual customer segments. For Instance Amazon continuously mines customer purchasing and browsing data to deliver personalized product recommendations, tailored promotions, and timely follow-ups. This not only enhances customer satisfaction but also significantly boosts conversion rates and average order value. According to a report by McKinsey, personalization can deliver five to eight times the ROI on marketing spend and lift sales by 10% or more—a powerful incentive for companies to adopt data mining software as part of their customer experience toolkit. (Source: https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/personalizing-at-scale#/) The utility of data mining tools extends beyond e-commerce and streaming platforms. In the banking and financial services industry, for example, institutions use data mining to analyze customer feedback, call center transcripts, and usage data to detect pain points and improve service delivery. Bank of America, for instance, utilizes data mining and predictive analytics to monitor customer interactions and provide proactive service suggestions or fraud alerts, significantly improving user satisfaction and trust. (Source: https://futuredigitalfinance.wbresearch.com/blog/bank-of-americas-erica-client-interactions-future-ai-in-banking) Similarly, telecom companies like Vodafone use data mining to understand customer churn behavior and implement retention strategies based on insights drawn from service usage patterns and complaint histories. In addition to p...

China Other Mining: Cost of Sales data was reported at 2,670.000 RMB mn in 2018. This records a decrease from the previous number of 3,120.000 RMB mn for 2017. China Other Mining: Cost of Sales data is updated yearly, averaging 1,058.327 RMB mn from Dec 1998 (Median) to 2018, with 21 observations. The data reached an all-time high of 3,120.000 RMB mn in 2017 and a record low of 124.228 RMB mn in 2002. China Other Mining: Cost of Sales data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BGF: Other Mining.

China Mining Auxiliary Activity: Sales Revenue data was reported at 174,190.000 RMB mn in 2018. This records an increase from the previous number of 156,671.000 RMB mn for 2017. China Mining Auxiliary Activity: Sales Revenue data is updated yearly, averaging 174,190.000 RMB mn from Dec 2012 (Median) to 2018, with 7 observations. The data reached an all-time high of 209,953.000 RMB mn in 2014 and a record low of 155,268.000 RMB mn in 2016. China Mining Auxiliary Activity: Sales Revenue data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BGG: Mining Professional and Auxiliary Activity.

The Clinical Data Analytics market is projected to be valued at $5 billion in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of 12%, reaching approximately $15 billion by 2034.

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.

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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.

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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.

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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 ...

Mining Servers Sales Market Outlook

The global mining servers sales market size is projected to grow from USD 3.5 billion in 2023 to USD 7.8 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 9.2% during the forecast period. This substantial growth is driven by the increasing adoption of cryptocurrencies, advancements in mining technologies, and the rising demand for data processing power.

One of the primary growth factors of the mining servers sales market is the escalating popularity and adoption of cryptocurrencies like Bitcoin, Ethereum, and others. As these digital assets gain wider acceptance as both investment vehicles and payment methods, the need for efficient and high-performance mining equipment has surged. This growing demand for cryptocurrency mining is contributing significantly to the expansion of the mining servers market. Furthermore, continuous advancements in mining hardware technology, including the development of more efficient and powerful ASICs, GPUs, CPUs, and FPGA miners, are propelling market growth by enhancing the profitability and feasibility of mining operations.

Another significant driver of market growth is the expansion of cloud mining services. Cloud mining allows users to participate in the mining process without owning and maintaining physical hardware. This service is gaining traction among individual miners and enterprises due to its convenience and lower upfront investment costs. The growing inclination towards cloud-based solutions in various industries is also reflected in the mining sector, where users prefer the flexibility and scalability of cloud mining services. Consequently, the rising popularity of cloud mining is expected to drive the demand for mining servers, contributing to market growth.

The role of Cryptocurrency Mining Hardware is pivotal in the mining servers sales market. These specialized devices are designed to solve complex mathematical problems required to validate and confirm transactions on the blockchain. As the demand for cryptocurrencies continues to rise, so does the need for more efficient and powerful mining hardware. This hardware not only enhances the speed and efficiency of mining operations but also reduces energy consumption, making mining more profitable. The continuous innovation in mining hardware technology is crucial for miners to stay competitive and maximize their returns. As a result, the market for cryptocurrency mining hardware is expected to grow significantly, driven by advancements in technology and the increasing value of digital currencies.

Moreover, the increasing application of data mining across various sectors is boosting the demand for mining servers. Data mining involves extracting valuable information from large datasets to support decision-making processes in industries such as finance, healthcare, retail, and marketing. The growing importance of big data analytics and the need for powerful computational resources to process and analyze vast amounts of data are driving the demand for high-performance mining servers. As organizations continue to leverage data mining for gaining competitive advantages, the market for mining servers is anticipated to witness substantial growth.

On a regional level, North America is expected to dominate the mining servers sales market due to the early adoption of advanced technologies, presence of major mining companies, and strong infrastructure. The Asia Pacific region, particularly China, is also a significant market player due to the concentration of cryptocurrency mining activities and manufacturing of mining hardware. Europe is anticipated to witness steady growth, driven by increasing investments in data centers and technological advancements. The Latin America and Middle East & Africa regions, although smaller in market share, are expected to exhibit gradual growth due to rising awareness and adoption of cryptocurrency mining and data processing technologies.

At the heart of efficient mining operations is the Mining Machine Chip, a critical component that determines the performance and energy efficiency of mining hardware. These chips are specifically designed to handle the intense computational demands of cryptocurrency mining, providing the necessary processing power to solve complex algorithms. The development of more advanced and efficient mining chips is a key focus for manufacturers, as it directly impac

The global market size of Mining Laboratory Automation Solutions 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 Mining Laboratory Automation Solutions Market Report 2019 - Market Size, Share, Price, Trend and Forecast is a professional and in-depth study on the current state of the global Mining Laboratory Automation Solutions industry. The key insights of the report:
1.The report provides key statistics on the market status of the Mining Laboratory Automation Solutions 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 Mining Laboratory Automation Solutions 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 Mining Laboratory Automation Solutions 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 Mining Laboratory Automation Solutions as well as some small players. At least 12 companies are included:
* FLSmidth
* Bruker
* ROCKLABS
* Thermo Fisher Scientific
* GE Energy
* Datech Scientific Limited
For complete companies list, please ask for sample pages.
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 Mining Laboratory Automation Solutions market
* Automated Analyzers and Sample Preparation Equipment
* Container Laboratory
* Laboratory Information Management Systems (LIMS)
* Robotics
For end use/application segment, this report focuses on the status and outlook for key applications. End users sre also listed.
* Mining Companies
* Laboratories

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.

SSR Mining reported $436.7M in Sales Revenues for its fiscal quarter ending in March of 2025. Data for SSR Mining | SSRM - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Indonesia Mining Production: Usage: Domestic Sales: Volume: Copper data was reported at 591,038.000 Ton in 2015. This records a decrease from the previous number of 594,136.000 Ton for 2014. Indonesia Mining Production: Usage: Domestic Sales: Volume: Copper data is updated yearly, averaging 592,587.000 Ton from Dec 2001 (Median) to 2015, with 6 observations. The data reached an all-time high of 883,687.000 Ton in 2012 and a record low of 8,295.000 Ton in 2001. Indonesia Mining Production: Usage: Domestic Sales: Volume: Copper data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Mining and Manufacturing Sector – Table ID.BAE004: Mining Production: Usage.

Endeavour Mining reported $1.04B in Sales Revenues for its fiscal quarter ending in March of 2025. Data for Endeavour Mining | EDV - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last July in 2025.

The U.S. Cheminformatics market is projected to be valued at $1.5 billion in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of 12%, reaching approximately $4.8 billion by 2034.

Graph and download economic data for Quarterly Financial Report: U.S. Corporations: All Mining: Net Sales, Receipts, and Operating Revenues (QFR101MINUSNO) from Q4 2000 to Q1 2025 about operating, receipts, revenue, finance, mining, corporate, Net, sales, industry, and USA.

The Global Mining Explosive Sales Market is expected to witness a CAGR of 5.5% during the forecast period 2021-2028. The growth in the market is mainly attributed to the increasing demand for explosives from the metal mining sector across the globe.

Mining Explosives are high-strength chemical compounds, which are used for blasting operations in mines. These explosives are capable of creating powerful explosions, which help in breaking hard rocks and minerals. Mining explosives are classified into two types, namely, primary mining explosives and secondary mining explosives. Primary mining explosives are used for blasting operations in underground mines, while secondary mining explosives are used for surface mines.

On the basis of Type, Global Mining Explosive Sales Market is segmented into Ammonium Nitrate Explosives (Powder), ANFO, Emulsion Explosive.

Ammonium Nitrate Explosives (Powder):

Ammonium nitrate explosives are a type of chemical explosive that is made from a mixture of ammonium nitrate and fuel oil. These explosives are used in mining, quarrying, and construction applications. Ammonium nitrate explosives are more stable than other types of explosives, making them safer to use.

ANFO:

ANFO is an abbreviation for ammonium nitrate-fuel oil mixture. It is a widely used blasting agent in mining, quarrying, and construction applications. ANFO is made by mixing ammonium nitrate with fuel oil. The mixture is then placed into bags or cartridges and detonated by a blasting cap.

Emulsion explosive:

Emulsion explosive is a type of water-based explosive that contains two immiscible liquids, such as water and fuel oil. Emulsion explosives are more stable than other types of explosives, making them safer to use.

On the basis of Application, Global Mining Explosive Sales Market is segmented into Coal Mining, Quarrying and Nonmetal Mining, Metal Mining.

Coal Mining:

Coal mining is the process of extracting coal from the ground. Coal is a fossil fuel that is used to generate electricity and heat. The coal mining industry uses explosives to break up the coal seam and then remove the coal from the mine.

Quarrying and Nonmetal Mining:

Quarrying is the process of extracting stone, sand, and gravel from quarries. Quarries are usually located in rural areas. The quarrying industry uses explosives to break up the rock and then remove it from the quarry.

Metal Mining:

Metal mining is the process of extracting metals, such as copper, zinc, iron, and gold, from mines. Metal mines are usually located in rural areas. The metal mining industry uses explosives to break up the rock and then remove the metals from the mine.

On the basis of Region, Global Mining Explosive Sales Market is segmented into North America, Latin America, Europe, Asia Pacific, and the Middle East & Africa.

North America: The North American mining explosives market is expected to witness the highest growth during the forecast period. The growth in the market is mainly attributed to the increasing demand for explosives from the coal mining sector in the region.

Latin America: The Latin American mining explosives market is expected to witness moderate growth during the forecast period. The growth in the market is mainly attributed to the increasing demand for explosives from the quarrying and nonmetal mining sectors in the region.

Europe: The European mining explosives market is expected to witness moderate growth during the forecast period. The growth in the market is mainly attributed to the increasing demand for explosives from the coal mining sector in the region.

Asia Pacific: The Asia Pacific mining explosives market is expected to witness significant growth during the forecast period. The growth in the market is mainly attributed to the increasing demand for explosives from the coal mining, quarrying, and nonmetal mining sectors in the region.

Middle East & Africa: The Middle East & Africa mining explosives market is expected to witness moderate growth during the forecast period. The growth in the market is mainly attributed to the increasing demand for explosives from the quarrying and nonmetal mining sectors in the region.

The growth factor of the Global Mining Explosive Sales Market:

The market is expected to witness significant growth due to the increasing demand for explosives from the coal mining, quarrying, and nonmetal mining sectors. T

Metro Mining reported AUD40.69M in Sales Revenues for its fiscal semester ending in June of 2024. Data for Metro Mining | MMI - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last July in 2025.

A fashion distributor sells articles of particular sizes and colors to its customers. In some cases items are returned to the distributor for various reasons. The order data and the related return data were recorded over a two-year period. The aim is to use this data and machine learning to build a model which enables a good prediction of return rates.

The Data

For this task real anonymized shop data are provided in the form of structured text files consisting of individual data sets. Below are some points to note about the files:

1. Each data set is on a single line ending with "CR" ("carriage return", 0xD), "LF" ("carriage return" and "line feed", 0xD and 0xA).
2. The first line has the same structure as the data sets, but contains the names of the respective columns (data fields).
3. The header line and each data set contain multiple fields separated from each other by a semi-colon (;).
4. There is no escape character, quotes are not used.
5. ASCII is the character set used.
6. Missing values may occur. These are coded using the character string NA.

Actually only the field names from the included document features.pdf can appear as column headings in the order used in that document. The associated value ranges are also listed.

The orders_train.txt contains all the data fields from the document whereas the associated test file orders_class.txt does not contain the target variable ``*returnQuantity*''.

The Task

The task is to use known historical data from January 2014 to September 2015 (approx. 2.33 million order positions) to build a model that makes predictions about return rates for order positions. The attribute returnQuantity in the given data indicates the number of articles for each order position (the value 0 means that the article will be kept while a value larger than 0 means that the article will be returned). For sales in the period from October 2015 to December 2015 (approx. 340,000 order positions) the model should then provide predictions for the number of articles which will be returned per order position. The prediction has to be a value of the set of natural numbers including 0. The difference between the prediction and the actual rate for an order position (i..e. error rate) must be as low as possible.

Acknowledgement

This dataset is publicly available in the data-mining-cup-website.

Newmont Mining reported $5.01B in Sales Revenues for its fiscal quarter ending in March of 2025. Data for Newmont Mining | NEM - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last July in 2025.

CoEUR Mining reported $305.44M in Sales Revenues for its fiscal quarter ending in December of 2024. Data for CoEUR Mining | CDE - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last July in 2025.