The Web Analytics Market in Retail and CPG is experiencing robust growth, projected to reach $1.22 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 18.19% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing need for data-driven decision-making within retail and CPG companies is paramount. Businesses are leveraging web analytics to gain deeper insights into customer behavior, optimize marketing campaigns, and personalize the shopping experience. The rise of e-commerce and omnichannel strategies further intensifies the demand for sophisticated web analytics solutions. Specifically, the ability to track customer journeys across multiple touchpoints, analyze real-time data, and measure the effectiveness of online marketing initiatives are crucial factors driving market growth. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are enabling more predictive analytics, empowering businesses to anticipate customer needs and proactively address potential challenges. Competitive pressures are also pushing companies to adopt advanced web analytics technologies to gain a competitive edge and improve operational efficiency. Segmentation reveals a strong demand across both SMEs and large enterprises, with significant application in search engine optimization (SEO), online marketing automation, customer profiling, application performance management, and social media management. Major players like Google, IBM, Meta, and Salesforce are strategically positioned to capitalize on this expanding market. The market's growth trajectory is expected to be consistent throughout the forecast period, driven by continued digital transformation within the retail and CPG sectors. While challenges such as data privacy concerns and the complexity of integrating diverse data sources exist, the overall market outlook remains positive. The North American market is anticipated to hold a significant share, given the region's advanced digital infrastructure and high adoption of web analytics technologies. However, other regions, particularly Asia Pacific, are expected to show significant growth due to the rapid expansion of e-commerce and increasing internet penetration. The market's future success hinges on the continued development of innovative analytics solutions that address the specific needs of retail and CPG companies, providing actionable insights that drive revenue growth, customer loyalty, and operational efficiency. Recent developments include: April 2024 - IBM Consulting and Microsoft have unveiled the opening of the IBM-Microsoft Experience Zone in Bangalore, India. The Experience Zone is designed as an exclusive venue where clients can delve into the potential of generative AI, hybrid cloud solutions, and other advanced Microsoft offerings. The goal is to expedite their business transformations and secure a competitive edge., January 2024 - Microsoft Corp. announced a suite of generative AI and data solutions tailored for retailers. These solutions cover every touchpoint of the retail shopper journey, from crafting personalized shopping experiences and empowering store associates to harness and consolidating retail data, ultimately aiding brands in better connecting with their target audiences. Microsoft's initiatives include introducing copilot templates on Azure OpenAI Service, enhancing retailers' ability to craft personalized shopping experiences, and streamlining store operations. Microsoft Fabric hosts advanced retail data solutions, while Microsoft Dynamics 365 Customer Insights boasts new copilot features. Microsoft also rolled out the Retail Media Creative Studio within the Microsoft Retail Media Platform. These advancements collectively bolster Microsoft Cloud for Retail, providing retailers with diverse tools to integrate copilot experiences across the entire shopper journey seamlessly.. Key drivers for this market are: Growing Demand for Online Shopping Trends, Rising Adoption of Analytics Tools to Understand Customer Preferences; Increasing Customer Centric Approach and Use of Recommendation Engines. Potential restraints include: Growing Demand for Online Shopping Trends, Rising Adoption of Analytics Tools to Understand Customer Preferences; Increasing Customer Centric Approach and Use of Recommendation Engines. Notable trends are: Search Engine Optimization and Ranking Sector Significantly Driving the Market Growth.

This dataset is a continuous parameter grid (CPG) of mean depth to water table in the Pacific Northwest. Source data come from the Digital General Soil Map of the United States, produced by the Natural Resources Conservation Service, United States Department of Agriculture.

This dataset is a continuous parameter grid (CPG) of upstream contributing area data (flow accumulation) in the Pacific Northwest. Source data come from the U.S. Geological Survey National Elevation Dataset and NHDPlus Version 2.

These datasets are continuous parameter grids (CPG) of hydrologic soil group percentages in the Pacific Northwest. Source data come from the Digital General Soil Map of the United States, produced by the Natural Resources Conservation Service, United States Department of Agriculture.

This dataset is a continuous parameter grid (CPG) of mean basin elevation data (30 meter pixels) in the Pacific Northwest. Source data come from the U.S. Geological Survey National Elevation Dataset, via the National Hydrography Dataset Plus V2.

This dataset is a continuous parameter grid (CPG) of baseflow index values (percent of discharge as baseflow) estimated at U.S. Geological Survey (USGS) streamgages in the Pacific Northwest. Source data was produced by David Wolock of USGS.

The Trade Promotion Management (TPM) and Optimization market within the consumer goods sector is experiencing robust growth, projected to reach $618.4 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 7.9% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing complexity of modern retail channels, encompassing both brick-and-mortar and e-commerce, necessitates sophisticated TPM solutions to optimize promotional campaigns and maximize ROI. Data-driven decision-making is becoming paramount, with companies increasingly leveraging advanced analytics and data harmonization capabilities to understand consumer behavior and refine their promotional strategies. Furthermore, the rise of omnichannel strategies demands integrated TPM systems capable of managing promotions across all touchpoints, ensuring consistent messaging and maximizing customer engagement. The Food and Beverage sector, particularly within retail and e-commerce, is a significant driver of market growth, reflecting the intense competition and need for effective promotional tools in these saturated markets. Companies are actively seeking TPM solutions to improve forecast accuracy, streamline operations, and ultimately enhance profitability. The market segmentation highlights the diverse applications of TPM solutions. Data harmonization is crucial for integrating data from disparate sources, providing a holistic view of promotional performance. Order management functionalities streamline the process of handling promotional orders and optimizing inventory levels. Head office planning tools enable centralized control and strategic decision-making regarding promotional activities. While the Food and Beverage sector dominates, other sectors are also showing increasing adoption, reflecting the broad applicability of effective promotion management techniques. The competitive landscape is dynamic, with established players like SAP and Oracle competing alongside specialized providers like Blueshift and IRI Worldwide, indicating a healthy ecosystem of innovation and competition. Geographic expansion is also driving growth, with North America and Europe currently holding significant market share, while Asia-Pacific presents a high-growth potential market due to increasing retail modernization and consumer spending.

Historical ownership data of CPG by FRANKLIN RESOURCES INC

This dataset is a continuous parameter grid (CPG) of topographic wetness index in the Pacific Northwest. Source data come from the U.S. Geological Survey National Elevation Dataset and NHDPlus Version 2.

The Location Analytics market is experiencing robust growth, projected to reach a significant size by 2033. A Compound Annual Growth Rate (CAGR) of 19.70% from 2019 to 2024 suggests a substantial increase in market value. This expansion is driven by several key factors. The increasing availability and affordability of location data from various sources, including GPS, mobile devices, and IoT sensors, fuels the market's growth. Businesses across diverse sectors are leveraging location analytics to gain actionable insights for improved operational efficiency, targeted marketing campaigns, and better customer experiences. Furthermore, advancements in data analytics technologies, such as AI and machine learning, are enabling more sophisticated location-based analysis, further driving market adoption. The emergence of cloud-based location analytics platforms enhances accessibility and scalability for businesses of all sizes. However, the market also faces certain challenges. Data privacy concerns and regulations, such as GDPR, pose significant restraints. The complexity of integrating various data sources and the need for skilled professionals to interpret and utilize the insights can also hinder wider market penetration. Despite these challenges, the long-term outlook for the location analytics market remains optimistic. The continued proliferation of connected devices and the rising demand for data-driven decision-making across industries will propel further growth. Market segmentation reveals strong performance across various sectors including retail, transportation and logistics, and government, with North America and Europe currently holding the largest market shares. The increasing adoption of location analytics in emerging economies like those within the Asia-Pacific region promises substantial future growth potential. The competitive landscape is characterized by a mix of established players, including Microsoft, Salesforce (through Tableau), and SAS, and emerging pure-play vendors like Google and TomTom, indicating a dynamic and innovative market. Recent developments include: November 2021 - Noogata announced the launch of its location analytics library. Building on the success of its existing e-commerce library, the location analytics library applies the power of Noogata's no code AI data analytics platform to physical locations for consumer packaged goods (CPG) brands., December 2020 - Microsoft unveiled a 'unified data governance service' as part of its Azure cloud platform, Azure Purview, that can discover and catalog all of an organization's data across all environments and locations.. Key drivers for this market are: Increasing Use of Spatial Data and Analytics in Various Industries, Growing Propensity of Consumers Toward Applications that Use Location Data. Potential restraints include: Increasing Use of Spatial Data and Analytics in Various Industries, Growing Propensity of Consumers Toward Applications that Use Location Data. Notable trends are: FMCG and E-Commerce Sector is Expected to Hold Significant Market Share.

Training dataset consists of DNA methylation data for breast cancer prediction, specifically:

  Raw Data Sources:

Main data file: data/processed dataset.csv - Contains DNA methylation data (CpG sites) Mapping file: data/geo_sample_code_condition_mapping_complete (BLOOD).xlsx - Contains sample labels

  Data Structure:

Features: CpG sites (columns starting with 'cg') Target: Binary classification (Cancer vs Non-Cancer) The data is initially transposed to have: Rows:… See the full description on the dataset page: https://huggingface.co/datasets/ahmad0yamany/bcp_TD.

These datasets are continuous parameter grids (CPG) of irrigated agriculture data (percent of basin classified as irrigated) for the years 2002, 2007, and 2012 in the Pacific Northwest. Source data was the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset for the United States (MIrAD-US), produced by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center.

Data source 1Analysis of correlation between the CpG methylation (450K arrays) and RNA transcripts (RNAseq) levels within the same gene in tumour samples.Data source 2The multi-omics driver scores for all ERGs

North America Source-to-Pay (S2P) Market size was valued at USD 437.06 Million in 2023 and is projected to reach USD 1195.08 Million by 2031, growing at a CAGR of 14.78% from 2024 to 2031.

North America Source-to-Pay (S2P) Market Dynamics

The key market dynamics that are shaping the North America Source-to-Pay (S2P) Market include:

Key Market Drivers

Digital Transformation: As North American businesses embrace digital transformation, there is an increased demand for S2P solutions to automate and expedite procurement processes. This move not only improves efficiency but also increases spending visibility and control, allowing for better decision-making.

Cost Reduction Pressures: In an economic environment when cost control is critical, North American enterprises use S2P systems to find cost-cutting possibilities and optimize spending. These systems give extensive spend analytics, allowing businesses to make informed decisions to minimize costs and enhance their bottom line.

Supply Chain Optimization: The demand for effective supply chain management is driving the use of S2P solutions in North America. These solutions provide complete capabilities for managing supplier relationships and performance, ensuring that supply chains are both efficient and resilient to disturbances.

Key Challenges:

Integration Complexity: Integrating S2P solutions with existing enterprise resource planning (ERP) systems is complicated and expensive. Aligning historical systems with new S2P solutions presents problems for organizations, impedes seamless data flow and process efficiency, and has an impact on market growth.

Change Management: Implementing S2P solutions necessitates considerable shifts in company processes and culture. Convincing stakeholders to embrace new systems and adapt established workflows is tough, resulting in opposition and delays in implementation.

Supplier Adoption: Getting all vendors on board with adopting an S2P platform is difficult. Smaller suppliers lack the resources and technical expertise to properly connect with these digital platforms, resulting in inefficiencies and fragmented procurement procedures.

Key Trends:

Artificial Intelligence and Machine Learning: AI and machine learning are increasingly being used in S2P applications to improve predictive analytics, automate repetitive operations, and make better decisions. These technologies aid in estimating demand, optimizing inventory, and finding possible cost savings, resulting in more effective procurement methods.

Sustainability Focus: There is an increasing trend for sustainable procurement methods within S2P operations. Companies prioritize suppliers who demonstrate environmental responsibility and sustainable operations by incorporating sustainability criteria into their sourcing and procurement choices.

Cloud-Based Solutions: The adoption of cloud-based S2P systems is growing due to their scalability, flexibility, and lower IT overhead. These platforms provide remote cooperation, real-time data access, and continual system updates, so making procurement procedures more agile.

Enhanced Supplier Collaboration: Modern S2P solutions are increasingly focused on improving supplier collaboration. Platforms now include more tools for managing supplier relationships, such as real-time communication, performance tracking, and collaborative problem-solving, which improves overall supply chain resilience and efficiency.

These datasets are continuous parameter grids (CPG) of permeability (and impermeability) of surface geology in the Pacific Northwest. Source data come from work by Chris Konrad, U.S. Geological Survey (USGS), and geologic map databases produced by USGS scientists.

These datasets are continuous parameter grids (CPG) of land cover types (percent of basin classified by cover type) for the years 2001, 2006, and 2011 in the Pacific Northwest. Cover types include forest, agriculture, developed, wetland, and open water. Source data was the National Land Cover Dataset, produced by the Multi-Resolution Land Characteristics (MRLC) Consortium.

This dataset should be used for the practical of the "Advanced Forensic Biology" lesson. It has been slightly modified from the initial source (see below). It consists of two parts:

1) CpG methylation beta values: tabulated separated methylation beta values in a file called cpg_methylation_beta_values.tsv.

2) Samples to chronological age : the relationship between sample identifiers and their chronological age in a tabulated separated file called cpg_methylation_sample_age.tsv.

3) CpG identifier to gene symbol and annotation: a tabulated separated file containing the relationship between CpG island identifier, the corresponding gene and its functional annotation. cpg_methylation_cpg_to_annotation.tsv

Dataset provenance:

• Bocklandt S, Lin W, Sehl ME, Sánchez FJ, Sinsheimer JS, Horvath S, Vilain E. Epigenetic predictor of age. PLoS One. 2011;6(6):e14821. doi: 10.1371/journal.pone.0014821. Epub 2011 Jun 22. PMID: 21731603; PMCID: PMC3120753.
• GEO link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE28746

The Source-to-Pay (S2P) outsourcing market is experiencing robust growth, driven by the increasing need for enhanced efficiency, cost optimization, and improved supply chain visibility among businesses globally. The market is projected to reach a substantial size, with a Compound Annual Growth Rate (CAGR) fueling this expansion. While precise figures for market size and CAGR are not provided, based on industry trends and comparable market reports, a reasonable estimation would place the 2025 market size at approximately $15 billion, growing at a CAGR of 12% through 2033. This growth is largely fueled by the rising adoption of advanced technologies like AI and machine learning within S2P solutions, enabling better automation, data analytics, and risk management. The increasing complexity of global supply chains and the need for real-time data visibility are also key drivers. Furthermore, the shift towards digital transformation initiatives across diverse industries is pushing organizations to adopt S2P outsourcing to streamline their procurement processes and gain a competitive edge. The market is segmented by type (Source-to-Contact and Procure-to-Pay) and application (Manufacturing, Consumer Packaged Goods (CPG), Software and IT, Energy and Chemicals, and Others). The Procure-to-Pay segment currently holds a significant market share, owing to its extensive functionalities encompassing the entire procurement cycle, from purchase order generation to payment processing. Similarly, Manufacturing and CPG industries are major adopters of S2P solutions due to their complex supply chains and high procurement volumes. Geographic segmentation reveals North America and Europe as leading markets, with significant growth potential in Asia Pacific regions due to increasing technological adoption and industrial expansion. However, factors such as data security concerns and the integration complexities with existing enterprise resource planning (ERP) systems pose challenges to market expansion.

Lifesight specializes in alternative consumer data collected from mobile audiences to provide intelligence covering consumer identity, behavior and interests. The company’s massive consumer datasets of over 2.3 billion profiles across the globe, in over 36 markets, and are designed to help users leverage alternative data for various use cases in industries such as CPG, retail, financial services and travel.

Lifesight collects high quality persistent human mobility data from location aware mobile apps using an embedded SDK. Lifesight complies with global privacy regulations including CCPA and GDPR, and adheres to strict data collection (opt-in) and opt-out processes.

This dataset provides aggregated data from all sources and it is delivered at quadkey grid level 17 (circa 300x300m cells)