The global Marketing Analytics Services market, currently valued at approximately $10.32 billion (2025), is poised for significant growth. While the precise CAGR is unavailable, considering the rapid adoption of data-driven marketing strategies and the increasing complexity of digital marketing landscapes, a conservative estimate would place the annual growth rate between 10-15% over the forecast period (2025-2033). This growth is fueled by several key drivers: the rising need for precise customer segmentation and targeting, the proliferation of marketing automation tools generating vast datasets, and a growing demand for measurable ROI on marketing investments. Businesses of all sizes, from large enterprises to SMEs, are increasingly relying on marketing analytics to optimize campaigns, personalize customer experiences, and improve overall marketing effectiveness. The market is segmented into online and offline services, catering to the diverse needs of businesses. Online services, leveraging sophisticated data analytics platforms and AI-powered insights, are experiencing faster growth compared to offline services. The dominance of North America and Europe is expected to continue, with the Asia-Pacific region witnessing strong growth potential due to increased digital adoption and a burgeoning middle class. However, challenges such as data privacy concerns, the need for skilled analytics professionals, and the high cost of implementation could potentially restrain market expansion. The competitive landscape is characterized by a mix of large consulting firms (Deloitte, Nielsen), specialized marketing analytics providers (Dun & Bradstreet, ClearPivot), and smaller niche players focusing on specific sectors or technologies. The market is witnessing a trend towards integrated solutions that combine marketing analytics with other marketing technologies, such as CRM and marketing automation platforms. Furthermore, the increasing availability of open-source tools and the emergence of cloud-based analytics solutions are democratizing access to marketing analytics, further fueling market growth. Over the next decade, the focus will likely shift toward predictive analytics, AI-driven insights, and the use of advanced analytics techniques to enhance customer lifetime value and improve business outcomes. This will lead to increased demand for skilled professionals specializing in data science and marketing analytics, driving further market expansion and shaping the competitive landscape.

This repository contains raw and processed data used and described in:

R. Sanchez-Lopez, S.G. Nielsen, M. El-Haj-Ali, F. Bianchi, M, Fereckzowski, O. Cañete, M. Wu, T. Neher, T. Dau and S. Santurette (under review). ``Auditory tests for characterizing hearing deficits in listeners with various hearing abilities: The BEAR test battery,''. submitted to Frontiers in Neuroscience

[Preprint available in medRxiv: https://doi.org/10.1101/2020.02.17.20021949]

One aim of the Better hEAring Rehabilitation (BEAR) project is to define a new clinical profiling tool, a test-battery, for individualized hearing loss characterization. Whereas the loss of sensitivity can be efficiently assessed by pure-tone audiometry, it still remains a challenge to address supra-threshold hearing deficits using appropriate clinical diagnostic tools. In contrast to the classical attenuation-distortion model (Plomp, 1986), the proposed BEAR approach is based on the hypothesis that any listener’s hearing can be characterized along two dimensions reflecting largely independent types of perceptual distortions. Recently, a data-driven approach (Sanchez-Lopez et al., 2018) provided evidence consistent with the existence of two independent sources of distortion, and thus different auditory profiles. Eleven tests were selected for the clinical test battery, based on their feasibility, time efficiency and related evidence from the literature. The proposed tests were divided into five categories: audibility, speech perception, binaural-processing abilities, loudness perception, and spectro-temporal resolution. Seventy-five listeners with symmetric, mild-to-severe sensorineural hearing loss were selected from a clinical population of hearing-aid users. The participants completed all tests in a clinical environment and did not receive systematic training for any of the tasks. The analysis of the results focused on the ability of each test to pinpoint individual differences among the participants, relationships among the different tests, and determining their potential use in clinical settings. The results might be valuable for hearing-aid fitting and clinical auditory profiling.

Please cite this article when using the data

The Dataset BEAR3 has also been used in:

Sanchez-Lopez R, Fereczkowski M, Neher T, Santurette S, Dau T. Robust Data-Driven Auditory Profiling Towards Precision Audiology. Trends in Hearing. January 2020. doi:10.1177/2331216520973539

Sanchez-Lopez, R., Fereczkowski, M., Neher, T., Santurette, S., & Dau, T. (2020). Robust auditory profiling: Improved data-driven method and profile definitions for better hearing rehabilitation. Proceedings of the International Symposium on Auditory and Audiological Research, 7, 281-288. Retrieved from https://proceedings.isaar.eu/index.php/isaarproc/article/view/2019-32

and

Sanchez Lopez, R., Nielsen, S. G., Cañete, O., Fereczkowski, M., Wu, M., Neher, T., Dau, T., & Santurette, S. (2019). A clinical test battery for Better hEAring Rehabilitation (BEAR): Towards the prediction of individual auditory deficits and hearing-aid benefit. In Proceedings of the 23rd International Congress on Acoustics (pp. 3841-3848). Deutsche Gesellschaft für Akustik e.V.. https://doi.org/10.18154/RWTH-CONV-239177

Description of the files:

• BEAR2.xlsx: Anonymized raw data obtained using the BEAR test battery.
• BEAR2_YNH.xlsx: Additional anonymized raw data obtained using the BEAR test battery with young normal-hearing listeners.
• BEAR3.xlsx: Anonymized processed data for statistical data analysis.
• BEAR3_Results_AProfiling.xlsx: BEAR3 dataset including the profiles, probabilities to belong to each of the four profiles and estimated degree of Distortion type-I and Distortion type-II.
• BEAR_Reliability.xlsx: Anonymized raw data similar to BEAR2 for the reliability study.
• DataParticipants.xlsx: Anonymized basic data associated with the participants: Gender, Age, PTA, etc.
• TestBatteryMethods_v1.1.pdf: Documentation of the test methods. Protocol included and corrections.
• Reliability_v1.0.pdf: Detailed explanation about the test-retest reliability study carried out with a subset of the participants.

* The participant IDs in each of the files has been assigned randomly to ensure the anonymization of the data. The pseudo-anonymized data might be shared under request by direct correspondence with the authors.

This dataset was compiled as part of the TIME4CS project, WP4, and lists identified citizen science training resources, as of July 2022.

The EU-citizen.science platform provided the basis for mapping CS training in Europe, as the team behind the platform has put considerable effort into compiling, and encouraging the CS community to contribute, CS training resources. Additionally, training courses were identified based on the case studies in WP1, as most universities do not list their courses on the EU-citizen.science platform.