In 2021, the market for artificial intelligence (AI) in marketing was estimated at ***** billion U.S. dollars. The source projected that the value would increase to more than ***** billion by 2028. What is AI and who uses it? Artificial intelligence (AI) has become one of the most impactful digital innovations of the past few decades. The term refers to the ability of a computer or machine to mimic the competencies of the human mind, with the current ecosystem consisting of machine learning, robotics, artificial neural networks, and natural language processing. All of these features and algorithms are highly versatile and adaptable to the specific requirements of the user, explaining why they have become embedded into many different industries, ranging from telecommunications and financial services to healthcare and pharma. Overall, the global artificial intelligence market was valued at around *** billion U.S. dollars in 2021. AI at the marketing wheel AI is deeply embedded into the digital marketing landscape, and based on the latest reports, more than ** percent of industry experts integrate some form of AI technology into their online marketing activities. This vast adaptation of artificial intelligence for marketing purposes is no surprise considering that its benefits include task automation, campaign personalization, and data analysis, to name but a few. When asked about marketers' main application areas of AI in a recent survey, roughly ** percent of respondents from the U.S., Canada, the UK, and India mentioned ad targeting. Other popular activities they trusted AI with included personalizing content, optimizing e-mail send times, and calculating conversion probability.

According to Cognitive Market Research, the global Artificial Intelligence in Retail market size is USD 4951.2 million in 2023and will expand at a compound annual growth rate (CAGR) of 39.50% from 2023 to 2030.

Enhanced customer personalization to provide viable market output
Demand for online remains higher in Artificial Intelligence in the Retail market.
The machine learning and deep learning category held the highest Artificial Intelligence in Retail market revenue share in 2023.
North American Artificial Intelligence In Retail will continue to lead, whereas the Asia-Pacific Artificial Intelligence In Retail market will experience the most substantial growth until 2030.

Market Dynamics of the Artificial Intelligence in the Retail Market

Key Drivers for Artificial Intelligence in Retail Market

Enhanced Customer Personalization to Provide Viable Market Output

A primary driver of Artificial Intelligence in the Retail market is the pursuit of enhanced customer personalization. A.I. algorithms analyze vast datasets of customer behaviors, preferences, and purchase history to deliver highly personalized shopping experiences. Retailers leverage this insight to offer tailored product recommendations, targeted marketing campaigns, and personalized promotions. The drive for superior customer personalization not only enhances customer satisfaction but also increases engagement and boosts sales. This focus on individualized interactions through A.I. applications is a key driver shaping the dynamic landscape of A.I. in the retail market.

January 2023 - Microsoft and digital start-up AiFi worked together to offer Smart Store Analytics. It is a cloud-based tracking solution that helps merchants with operational and shopper insights for intelligent, cashierless stores.

Source-techcrunch.com/2023/01/10/aifi-microsoft-smart-store-analytics/

Improved Operational Efficiency to Propel Market Growth

Another pivotal driver is the quest for improved operational efficiency within the retail sector. A.I. technologies streamline various aspects of retail operations, from inventory management and demand forecasting to supply chain optimization and cashier-less checkout systems. By automating routine tasks and leveraging predictive analytics, retailers can enhance efficiency, reduce costs, and minimize errors. The pursuit of improved operational efficiency is a key motivator for retailers to invest in AI solutions, enabling them to stay competitive, adapt to dynamic market conditions, and meet the evolving demands of modern consumers in the highly competitive artificial intelligence (AI) retail market.

January 2023 - The EY Retail Intelligence solution, which is based on Microsoft Cloud, was introduced by the Fintech business EY to give customers a safe and efficient shopping experience. In order to deliver insightful information, this solution makes use of Microsoft Cloud for Retail and its technologies, which include image recognition, analytics, and artificial intelligence (A.I.).

Source-www.ey.com/en_gl/news/2023/01/ey-announces-launch-of-retail-solution-that-builds-on-the-microsoft-cloud-to-help-achieve-seamless-consumer-shopping-experiences

Key Restraints for Artificial Intelligence in Retail Market

Data Security Concerns to Restrict Market Growth

A prominent restraint in Artificial Intelligence in the Retail market is the pervasive concern over data security. As retailers increasingly rely on A.I. to process vast amounts of customer data for personalized experiences, there is a growing apprehension regarding the protection of sensitive information. The potential for data breaches and cyberattacks poses a significant challenge, as retailers must navigate the delicate balance between utilizing customer data for AI-driven initiatives and safeguarding it against potential security threats. Addressing these concerns is crucial to building and maintaining consumer trust in A.I. applications within the retail sector.

Key Trends for Artificial Intelligence in Retail Market

Surge in Voice-Enabled Shopping Interfaces Reshaping Retail Experiences

Voice-enabled A.I. assistants such as Amazon Alexa and Google Assistant are revolutionizing the way consumers engage with retail platforms. Shoppers can now utilize voice commands to search, compare, and purchase products, thereby streamlining and accelerating the buying process. Retailers...

Success.ai’s Company Data Solutions provide businesses with powerful, enterprise-ready B2B company datasets, enabling you to unlock insights on over 28 million verified company profiles. Our solution is ideal for organizations seeking accurate and detailed B2B contact data, whether you’re targeting large enterprises, mid-sized businesses, or small business contact data.

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API Features:

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Why Choose Success.ai?

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Our database spans 195 countries and covers 28 million public and private company profiles, with detailed insights into each company’s structure, size, funding history, and key technologies. We provide B2B company data for businesses of all sizes, from small business contact data to large corporations, with extensive coverage in regions such as North America, Europe, Asia-Pacific, and Latin America.

Comprehensive Data Points: Success.ai delivers in-depth information on each company, with over 15 data points, including:

Company Name: Get the full legal name of the company. LinkedIn URL: Direct link to the company's LinkedIn profile. Company Domain: Website URL for more detailed research. Company Description: Overview of the company’s services and products. Company Location: Geographic location down to the city, state, and country. Company Industry: The sector or industry the company operates in. Employee Count: Number of employees to help identify company size. Technologies Used: Insights into key technologies employed by the company, valuable for tech-based outreach. Funding Information: Track total funding and the most recent funding dates for investment opportunities. Maximize Your Sales Potential: With Success.ai’s B2B contact data and company datasets, sales teams can build tailored lists of target accounts, identify decision-makers, and access real-time company intelligence. Our curated datasets ensure you’re always focused on high-value leads—those who are most likely to convert into clients. Whether you’re conducting account-based marketing (ABM), expanding your sales pipeline, or looking to improve your lead generation strategies, Success.ai offers the resources you need to scale your business efficiently.

Tailored for Your Industry: Success.ai serves multiple industries, including technology, healthcare, finance, manufacturing, and more. Our B2B marketing data solutions are particularly valuable for businesses looking to reach professionals in key sectors. You’ll also have access to small business contact data, perfect for reaching new markets or uncovering high-growth startups.

From UK B2B data to contacts across Europe and Asia, our datasets provide global coverage to expand your business reach and identify new...

This dataset contains respondents' answers regarding A.I. Influencer Characteristics, Impulsive Buying, Perceived Value, and Positive Emotional Appeal.

Success.ai’s LinkedIn Data Solutions offer unparalleled access to a vast dataset of 700 million public LinkedIn profiles and 70 million LinkedIn company records, making it one of the most comprehensive and reliable LinkedIn datasets available on the market today. Our employee data and LinkedIn data are ideal for businesses looking to streamline recruitment efforts, build highly targeted lead lists, or develop personalized B2B marketing campaigns.

Whether you’re looking for recruiting data, conducting investment research, or seeking to enrich your CRM systems with accurate and up-to-date LinkedIn profile data, Success.ai provides everything you need with pinpoint precision. By tapping into LinkedIn company data, you’ll have access to over 40 critical data points per profile, including education, professional history, and skills.

Key Benefits of Success.ai’s LinkedIn Data: Our LinkedIn data solution offers more than just a dataset. With GDPR-compliant data, AI-enhanced accuracy, and a price match guarantee, Success.ai ensures you receive the highest-quality data at the best price in the market. Our datasets are delivered in Parquet format for easy integration into your systems, and with millions of profiles updated daily, you can trust that you’re always working with fresh, relevant data.

Global Reach and Industry Coverage: Our LinkedIn data covers professionals across all industries and sectors, providing you with detailed insights into businesses around the world. Our geographic coverage spans 259M profiles in the United States, 22M in the United Kingdom, 27M in India, and thousands of profiles in regions such as Europe, Latin America, and Asia Pacific. With LinkedIn company data, you can access profiles of top companies from the United States (6M+), United Kingdom (2M+), and beyond, helping you scale your outreach globally.

Why Choose Success.ai’s LinkedIn Data: Success.ai stands out for its tailored approach and white-glove service, making it easy for businesses to receive exactly the data they need without managing complex data platforms. Our dedicated Success Managers will curate and deliver your dataset based on your specific requirements, so you can focus on what matters most—reaching the right audience. Whether you’re sourcing employee data, LinkedIn profile data, or recruiting data, our service ensures a seamless experience with 99% data accuracy.

• Best Price Guarantee: We offer unbeatable pricing on LinkedIn data, and we’ll match any competitor.
• Global Scale: Access 700 million LinkedIn profiles and 70 million company records globally.
• AI-Verified Accuracy: Enjoy 99% data accuracy through our advanced AI and manual validation processes.
• Real-Time Data: Profiles are updated daily, ensuring you always have the most relevant insights.
• Tailored Solutions: Get custom-curated LinkedIn data delivered directly, without managing platforms.
• Ethically Sourced Data: Compliant with global privacy laws, ensuring responsible data usage.
• Comprehensive Profiles: Over 40 data points per profile, including job titles, skills, and company details.
• Wide Industry Coverage: Covering sectors from tech to finance across regions like the US, UK, Europe, and Asia.

Key Use Cases:

• Sales Prospecting and Lead Generation: Build targeted lead lists using LinkedIn company data and professional profiles, helping sales teams engage decision-makers at high-value accounts.
• Recruitment and Talent Sourcing: Use LinkedIn profile data to identify and reach top candidates globally. Our employee data includes work history, skills, and education, providing all the details you need for successful recruitment.
• Account-Based Marketing (ABM): Use our LinkedIn company data to tailor marketing campaigns to key accounts, making your outreach efforts more personalized and effective.
• Investment Research & Due Diligence: Identify companies with strong growth potential using LinkedIn company data. Access key data points such as funding history, employee count, and company trends to fuel investment decisions.
• Competitor Analysis: Stay ahead of your competition by tracking hiring trends, employee movement, and company growth through LinkedIn data. Use these insights to adjust your market strategy and improve your competitive positioning.
• CRM Data Enrichment: Enhance your CRM systems with real-time updates from Success.ai’s LinkedIn data, ensuring that your sales and marketing teams are always working with accurate and up-to-date information.
• Comprehensive Data Points for LinkedIn Profiles: Our LinkedIn profile data includes over 40 key data points for every individual and company, ensuring a complete understanding of each contact:

LinkedIn URL: Access direct links to LinkedIn profiles for immediate insights. Full Name: Verified first and last names. Job Title: Current job titles, and prior experience. Company Information: Company name, LinkedIn URL, domain, and location. Work and Per...

Marketing Campaigns Dataset

This repository contains a dataset specifically designed for generating marketing content. The dataset includes various features that are crucial for crafting effective marketing strategies, such as industry, channel, objective, and more. This dataset is ideal for use in machine learning models, AI-powered marketing tools, and data-driven marketing analyses.

  Dataset Overview

The dataset consists of multiple entries, each representing a specific… See the full description on the dataset page: https://huggingface.co/datasets/RafaM97/marketing_social_media.

At VisitIQ™, we provide a wealth of consumer marketing data to help businesses unlock deeper insights and optimize their B2C strategies. Our extensive and meticulously curated datasets are designed to provide a 360-degree view of your target consumers, combining a wide range of behavioral, demographic, and psychographic data points to deliver actionable insights that drive measurable results.

Our comprehensive consumer marketing database is built to fuel data-driven marketing strategies. With our rich behavioral insights, you can understand not just who your customers are, but also how they interact with your brand, what they are looking for, and what motivates their purchasing decisions. By tracking online and offline behaviors, preferences, purchase history, and engagement patterns, VisitIQ™ enables you to segment your audience more effectively and craft personalized marketing messages that resonate with your ideal customer profiles.

In addition to behavioral insights, our datasets provide detailed demographic information, including age, gender, location, income level, education, and household characteristics. This allows you to pinpoint your marketing efforts with incredible precision, reaching the right audience with the right message at the right time. Our data also includes psychographic attributes, such as lifestyle preferences, interests, and values, providing a deeper understanding of what drives consumer behavior and helping you create more compelling and relevant content.

VisitIQ's™ platform integrates seamlessly with your existing marketing stack, enabling you to utilize our consumer marketing data across multiple channels, from digital and social media to email and direct mail. With our data, you can improve targeting, increase engagement, reduce customer acquisition costs, and ultimately achieve a higher return on your marketing investment.

Whether you’re looking to attract new customers, retain existing ones, or re-engage lapsed consumers, VisitIQ™ provides the data you need to build effective, data-driven B2C marketing strategies. Our comprehensive datasets empower you to make informed decisions, optimize your marketing campaigns in real-time, and drive successful outcomes.

Unlock the full potential of your consumer marketing efforts with VisitIQ™. Transform your approach with powerful insights, sharpen your competitive edge, and achieve unparalleled marketing success.

Generative AI in Digital Marketing Market Outlook

According to our latest research, the global Generative AI in Digital Marketing market size stood at USD 5.42 billion in 2024, reflecting robust adoption across industries worldwide. The market is expected to grow at a remarkable CAGR of 28.6% from 2025 to 2033, reaching a forecasted value of USD 52.23 billion by 2033. This impressive expansion is being driven by the increasing integration of advanced AI-driven tools for content creation, personalized marketing, and customer engagement, as businesses seek to optimize marketing efficiency and ROI in an ever-evolving digital landscape.

One of the primary growth factors fueling the Generative AI in Digital Marketing market is the escalating demand for hyper-personalized customer experiences. Modern consumers expect brands to deliver tailored content and offers based on their unique preferences and behaviors. Generative AI solutions excel in analyzing vast datasets and generating highly relevant marketing assets, enabling brands to engage audiences with unprecedented precision. As digital marketing becomes more data-driven, organizations are leveraging generative AI to automate content creation, optimize campaigns in real-time, and enhance the overall customer journey. This trend is particularly pronounced in sectors such as retail, e-commerce, and BFSI, where personalized engagement translates directly into higher conversion rates and customer loyalty.

Another significant driver is the rapid evolution of generative AI software and platforms, which are becoming increasingly accessible and user-friendly. The proliferation of AI-powered tools for tasks like copywriting, image generation, video production, and social media management has democratized digital marketing, empowering both large enterprises and SMEs to compete on a level playing field. Furthermore, the integration of generative AI with existing marketing automation systems and CRM platforms is streamlining workflows and reducing operational costs. As AI models grow more sophisticated, they are enabling marketers to move beyond basic automation to truly creative and context-aware campaign strategies, further accelerating market adoption.

The growing emphasis on data privacy and regulatory compliance is also shaping the trajectory of the Generative AI in Digital Marketing market. With stricter regulations such as GDPR and CCPA, organizations are seeking AI solutions that not only enhance marketing effectiveness but also ensure ethical data usage and transparency. Generative AI vendors are responding by embedding privacy-by-design principles and robust governance frameworks into their offerings. This focus on responsible AI adoption is fostering trust among end-users and stakeholders, thereby supporting sustained market growth. Additionally, the expanding ecosystem of partnerships between AI technology providers, digital agencies, and industry-specific solution vendors is accelerating innovation and broadening the market’s reach.

Regionally, North America continues to dominate the Generative AI in Digital Marketing market, accounting for the largest share in 2024, driven by high technology adoption rates and a mature digital marketing infrastructure. However, Asia Pacific is emerging as the fastest-growing region, propelled by rapid digitalization, rising internet penetration, and a burgeoning e-commerce sector. Europe is also witnessing substantial growth, supported by strong regulatory frameworks and increasing investments in AI research and development. Latin America and the Middle East & Africa are gradually catching up, as businesses in these regions recognize the value of AI-enhanced marketing strategies to expand their digital footprint and drive business growth.

Component Analysis

The Component segment of the Generative AI in Digital Marketing market is bifurcated into software and services, each playing a critical role in the industry’s expansion. The software segment comprises AI-powered platforms and tools

Marketing Agent Meta and Traffic Dataset in AI Agent Marketplace | AI Agent Directory | AI Agent Index from DeepNLP

This dataset is collected from AI Agent Marketplace Index and Directory at http://www.deepnlp.org, which contains AI Agents's meta information such as agent's name, website, description, as well as the monthly updated Web performance metrics, including Google,Bing average search ranking positions, Github Stars, Arxiv References, etc. The dataset is helpful for AI… See the full description on the dataset page: https://huggingface.co/datasets/DeepNLP/marketing-ai-agent.

This data describes the demographics and variables associated with AI-powered marketing for businesses and organisations. It includes variables such as consumer engagement, conversion rate, marketing approach, company industry, company size, and years of operation. It was used in a study to determine the impact of AI-powered personalised marketing strategies in enhancing consumer engagement and conversion rates for organisations and businesses.

Unlock in-depth beauty industry insights with our Ulta Beauty Dataset, featuring 33,000 records in CSV format. This beauty cosmetics dataset provides detailed product listings, pricing, ratings, reviews, brand information, and availability, making it a powerful resource for businesses, analysts, and AI developers in the cosmetics and skincare industry.

With structured and up-to-date data, brands can analyze consumer trends, competitor pricing, product performance, and customer sentiment to optimize their marketing strategies and product offerings. Ideal for AI/ML model training, recommendation engines, price tracking, and trend forecasting, this dataset helps you stay ahead in the dynamic beauty sector.

📩 Get in touch today to access this exclusive Ulta dataset and elevate your data-driven decision-making!

miike-ai/marketing-agent dataset hosted on Hugging Face and contributed by the HF Datasets community

📊 About the Dataset

This dataset explores the influence of AI-generated content across various industries, including journalism, social media, entertainment, and marketing. It provides insights into public sentiment, engagement trends, economic impact, and regulatory responses over time.

With AI-generated content becoming increasingly prevalent, this dataset serves as a valuable resource for data analysts, business strategists, and machine learning researchers to study trends, detect biases, and predict future AI adoption patterns.

💡 This dataset is perfect for AI adoption analysis, industry forecasting, and ethical AI research!

1) Data Introduction • The Dummy Marketing Data for Classification dataset is a dummy dataset created by individuals for 'Data Science for Business' and 'Data-driven marketing' classes. It contains data on age, expenditure, region, and whether apps are downloaded.

2) Data Utilization (1) Dummy Marketing Data for Classification data has characteristics that: • The dataset includes 2 numerical variables, 2 category variables. (2) Dummy Marketing Data for Classification data can be used to: • Data Science classes: useful for training basic concepts and skills in data science, including data preprocessing, exploratory data analysis (EDA), feature engineering, model learning, and evaluation. • Marketing Analysis: Available as hands-on material in classes that teach marketing strategies and data-driven decision-making.

WiserBrand's Comprehensive Customer Call Transcription Dataset: Tailored Insights

WiserBrand offers a customizable dataset comprising transcribed customer call records, meticulously tailored to your specific requirements. This extensive dataset includes:

User ID and Firm Name: Identify and categorize calls by unique user IDs and company names. Call Duration: Analyze engagement levels through call lengths. Geographical Information: Detailed data on city, state, and country for regional analysis. Call Timing: Track peak interaction times with precise timestamps. Call Reason and Group: Categorised reasons for calls, helping to identify common customer issues. Device and OS Types: Information on the devices and operating systems used for technical support analysis. Transcriptions: Full-text transcriptions of each call, enabling sentiment analysis, keyword extraction, and detailed interaction reviews.

Our dataset is designed for businesses aiming to enhance customer service strategies, develop targeted marketing campaigns, and improve product support systems. Gain actionable insights into customer needs and behavior patterns with this comprehensive collection, particularly useful for Consumer Data, Consumer Behavior Data, Consumer Sentiment Data, Consumer Review Data, AI Training Data, Textual Data, and Transcription Data applications.

WiserBrand's dataset is essential for companies looking to leverage Consumer Data and B2B Marketing Data to drive their strategic initiatives in the English-speaking markets of the USA, UK, and Australia. By accessing this rich dataset, businesses can uncover trends and insights critical for improving customer engagement and satisfaction.

Cases:

1. Training Speech Recognition (Speech-to-Text) and Speech Synthesis (Text-to-Speech) Models WiserBrand's Comprehensive Customer Call Transcription Dataset is an excellent resource for training and improving speech recognition models (Speech-to-Text, STT) and speech synthesis systems (Text-to-Speech, TTS). Here’s how this dataset can contribute to these tasks:

Enriching STT Models: The dataset includes a wide variety of real-world customer service calls with diverse accents, tones, and terminologies. This makes it highly valuable for training speech-to-text models to better recognize different dialects, regional speech patterns, and industry-specific jargon. It could help improve accuracy in transcribing conversations in customer service, sales, or technical support.

Contextualized Speech Recognition: Given the contextual information (e.g., reasons for calls, call categories, etc.), it can help models differentiate between various types of conversations (technical support vs. sales queries), which would improve the model’s ability to transcribe in a more contextually relevant manner.

Improving TTS Systems: The transcriptions, along with their associated metadata (such as call duration, timing, and call reason), can aid in training Text-to-Speech models that mimic natural conversation patterns, including pauses, tone variation, and proper intonation. This is especially beneficial for developing conversational agents that sound more natural and human-like in their responses.

Noise and Speech Quality Handling: Real-world customer service calls often contain background noise, overlapping speech, and interruptions, which are crucial elements for training speech models to handle real-life scenarios more effectively.

1. Training AI Agents for Replacing Customer Service Representatives WiserBrand’s dataset can be incredibly valuable for businesses looking to develop AI-powered customer support agents that can replace or augment human customer service representatives. Here’s how this dataset supports AI agent training:

Customer Interaction Simulation: The transcriptions provide a comprehensive view of real customer interactions, including common queries, complaints, and support requests. By training AI models on this data, businesses can equip their virtual agents with the ability to understand customer concerns, follow up on issues, and provide meaningful solutions, all while mimicking human-like conversational flow.

Sentiment Analysis and Emotional Intelligence: The full-text transcriptions, along with associated call metadata (e.g., reason for the call, call duration, and geographical data), allow for sentiment analysis, enabling AI agents to gauge the emotional tone of customers. This helps the agents respond appropriately, whether it’s providing reassurance during frustrating technical issues or offering solutions in a polite, empathetic manner. Such capabilities are essential for improving customer satisfaction in automated systems.

Customizable Dialogue Systems: The dataset allows for categorizing and identifying recurring call patterns and issues. This means AI agents can be trained to recognize the types of queries that come up frequently, allowing them to automate routine tasks such as ...

More and more companies use artificial intelligence (AI). Research aimed to understand acceptance from the perspective of AI users or people affected by AI decisions. However, the perspective of decision-makers in companies (i.e., managers) has not been considered. To address this gap, we investigate managers’ acceptance of AI usage in companies, focusing on two potential determinants. Across four experimental studies (Ntotal = 2025), we tested whether the business area (i.e., human resources vs. finances/ marketing) and AI functionality affect managers’ acceptance of AI (i.e., perceived risk of negative consequences, willingness to invest). Findings indicate that managers (a) perceive more risk of and (b) are less willing to invest in AI usage in human resources than in finances and marketing. Besides, the results suggest that acceptance declines if functionality crosses a critical boundary and AI autonomously implements decisions without prior human control. Accordingly, the current research sheds light on the AI acceptance of managers and gives insights into the role of the business area and AI functionality.

1.Introduction

Sales data collection is a crucial aspect of any manufacturing industry as it provides valuable insights about the performance of products, customer behaviour, and market trends. By gathering and analysing this data, manufacturers can make informed decisions about product development, pricing, and marketing strategies in Internet of Things (IoT) business environments like the dairy supply chain.

One of the most important benefits of the sales data collection process is that it allows manufacturers to identify their most successful products and target their efforts towards those areas. For example, if a manufacturer could notice that a particular product is selling well in a certain region, this information could be utilised to develop new products, optimise the supply chain or improve existing ones to meet the changing needs of customers.

This dataset includes information about 7 of MEVGAL’s products [1]. According to the above information the data published will help researchers to understand the dynamics of the dairy market and its consumption patterns, which is creating the fertile ground for synergies between academia and industry and eventually help the industry in making informed decisions regarding product development, pricing and market strategies in the IoT playground. The use of this dataset could also aim to understand the impact of various external factors on the dairy market such as the economic, environmental, and technological factors. It could help in understanding the current state of the dairy industry and identifying potential opportunities for growth and development.

2. Citation

Please cite the following papers when using this dataset:

1. I. Siniosoglou, K. Xouveroudis, V. Argyriou, T. Lagkas, S. K. Goudos, K. E. Psannis and P. Sarigiannidis, "Evaluating the Effect of Volatile Federated Timeseries on Modern DNNs: Attention over Long/Short Memory," in the 12th International Conference on Circuits and Systems Technologies (MOCAST 2023), April 2023, Accepted

3. Dataset Modalities

The dataset includes data regarding the daily sales of a series of dairy product codes offered by MEVGAL. In particular, the dataset includes information gathered by the logistics division and agencies within the industrial infrastructures overseeing the production of each product code. The products included in this dataset represent the daily sales and logistics of a variety of yogurt-based stock. Each of the different files include the logistics for that product on a daily basis for three years, from 2020 to 2022.

3.1 Data Collection

The process of building this dataset involves several steps to ensure that the data is accurate, comprehensive and relevant.

The first step is to determine the specific data that is needed to support the business objectives of the industry, i.e., in this publication’s case the daily sales data.

Once the data requirements have been identified, the next step is to implement an effective sales data collection method. In MEVGAL’s case this is conducted through direct communication and reports generated each day by representatives & selling points.

It is also important for MEVGAL to ensure that the data collection process conducted is in an ethical and compliant manner, adhering to data privacy laws and regulation. The industry also has a data management plan in place to ensure that the data is securely stored and protected from unauthorised access.

The published dataset is consisted of 13 features providing information about the date and the number of products that have been sold. Finally, the dataset was anonymised in consideration to the privacy requirement of the data owner (MEVGAL).

File	Period	Number of Samples (days)
product 1 2020.xlsx	01/01/2020–31/12/2020	363
product 1 2021.xlsx	01/01/2021–31/12/2021	364
product 1 2022.xlsx	01/01/2022–31/12/2022	365
product 2 2020.xlsx	01/01/2020–31/12/2020	363
product 2 2021.xlsx	01/01/2021–31/12/2021	364
product 2 2022.xlsx	01/01/2022–31/12/2022	365
product 3 2020.xlsx	01/01/2020–31/12/2020	363
product 3 2021.xlsx	01/01/2021–31/12/2021	364
product 3 2022.xlsx	01/01/2022–31/12/2022	365
product 4 2020.xlsx	01/01/2020–31/12/2020	363
product 4 2021.xlsx	01/01/2021–31/12/2021	364
product 4 2022.xlsx	01/01/2022–31/12/2022	364
product 5 2020.xlsx	01/01/2020–31/12/2020	363
product 5 2021.xlsx	01/01/2021–31/12/2021	364
product 5 2022.xlsx	01/01/2022–31/12/2022	365
product 6 2020.xlsx	01/01/2020–31/12/2020	362
product 6 2021.xlsx	01/01/2021–31/12/2021	364
product 6 2022.xlsx	01/01/2022–31/12/2022	365
product 7 2020.xlsx	01/01/2020–31/12/2020	362
product 7 2021.xlsx	01/01/2021–31/12/2021	364
product 7 2022.xlsx	01/01/2022–31/12/2022	365

3.2 Dataset Overview

The following table enumerates and explains the features included across all of the included files.

Feature	Description	Unit
Day	day of the month	-
Month	Month	-
Year	Year	-
daily_unit_sales	Daily sales - the amount of products, measured in units, that during that specific day were sold	units
previous_year_daily_unit_sales	Previous Year’s sales - the amount of products, measured in units, that during that specific day were sold the previous year	units
percentage_difference_daily_unit_sales	The percentage difference between the two above values	%
daily_unit_sales_kg	The amount of products, measured in kilograms, that during that specific day were sold	kg
previous_year_daily_unit_sales_kg	Previous Year’s sales - the amount of products, measured in kilograms, that during that specific day were sold, the previous year	kg
percentage_difference_daily_unit_sales_kg	The percentage difference between the two above values	kg
daily_unit_returns_kg	The percentage of the products that were shipped to selling points and were returned	%
previous_year_daily_unit_returns_kg	The percentage of the products that were shipped to

This dataset contains responses from a survey conducted for a master's thesis at Erasmus University Rotterdam. The survey investigated how consumer perceptions of privacy and trust in interactions with centralized versus decentralized AI-powered chatbots influence customer satisfaction. The survey included a predetermined simulated conversation with an AI-powered chatbot.Purpose of the Study:The main research question addressed in this study is: "How do consumer perceptions of privacy and trust in interactions with centralized versus decentralized AI-powered chatbots influence customer satisfaction?" The study aims to compare the differences in customer satisfaction, privacy concerns, and trust between centralized and decentralized AI-powered chatbots.Data Description:This dataset includes responses from 175 participants after data cleaning and removal of incomplete and biased responses. Participants were randomly assigned to one of three groups:Unaware of the chatbot typeInformed they would interact with a centralized chatbotInformed they would interact with a decentralized chatbotVariables:Customer Satisfaction: Measured with Likert scale questions on a 5-point scale from Strongly disagree to Strongly agree.Consumer Privacy Concerns: Measured with Likert scale questions on a 5-point scale from Strongly disagree to Strongly agree.Consumer Trust in AI-Powered Chatbots: Measured with Likert scale questions on a 5-point scale from Strongly disagree to Strongly agree.Consumer AI Familiarity: Measured with Likert scale questions regarding prior usage and understanding of AI technology on a 5-point scale from Strongly disagree to Strongly agree.Demographic Information: Age group, gender, highest education finished, nationality, and occupation.Chatbot Type: Categorical variable with values: 0 for not aware, 1 for aware of interacting with a centralized chatbot, and 2 for aware of interacting with a decentralized chatbot.Usage Notes:The dataset is provided in a XLSX file format and includes all necessary variables for analysis. The dataset can be used to conduct various statistical analyses, including descriptive statistics, hypothesis testing, and regression analysis.

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Artificial Intelligence Text Generator Market Size 2024-2028

The artificial intelligence (AI) text generator market size is forecast to increase by USD 908.2 million at a CAGR of 21.22% between 2023 and 2028.

The market is experiencing significant growth due to several key trends. One of these trends is the increasing popularity of AI generators in various sectors, including education for e-learning applications. Another trend is the growing importance of speech-to-text technology, which is becoming increasingly essential for improving productivity and accessibility. However, data privacy and security concerns remain a challenge for the market, as generators process and store vast amounts of sensitive information. It is crucial for market participants to address these concerns through strong data security measures and transparent data handling practices to ensure customer trust and compliance with regulations. Overall, the AI generator market is poised for continued growth as it offers significant benefits in terms of efficiency, accuracy, and accessibility.

What will be the Size of the Artificial Intelligence (AI) Text Generator Market During the Forecast Period?

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The market is experiencing significant growth as businesses and organizations seek to automate content creation across various industries. Driven by technological advancements in machine learning (ML) and natural language processing, AI generators are increasingly being adopted for downstream applications in sectors such as education, manufacturing, and e-commerce. 
Moreover, these systems enable the creation of personalized content for global audiences in multiple languages, providing a competitive edge for businesses in an interconnected Internet economy. However, responsible AI practices are crucial to mitigate risks associated with biased content, misinformation, misuse, and potential misrepresentation.

How is this Artificial Intelligence (AI) Text Generator Industry segmented and which is the largest segment?

The artificial intelligence (AI) text generator industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Component

  Solution
  Service


Application

  Text to text
  Speech to text
  Image/video to text


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    India


  South America



  Middle East and Africa

By Component Insights

The solution segment is estimated to witness significant growth during the forecast period.

Artificial Intelligence (AI) text generators have gained significant traction in various industries due to their efficiency and cost-effectiveness in content creation. These solutions utilize machine learning algorithms, such as Deep Neural Networks, to analyze and learn from vast datasets of human-written text. By predicting the most probable word or sequence of words based on patterns and relationships identified In the training data, AIgenerators produce personalized content for multiple languages and global audiences. The application spans across industries, including education, manufacturing, e-commerce, and entertainment & media. In the education industry, AI generators assist in creating personalized learning materials.

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The solution segment was valued at USD 184.50 million in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 33% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The North American market holds the largest share in the market, driven by the region's technological advancements and increasing adoption of AI in various industries. AI text generators are increasingly utilized for content creation, customer service, virtual assistants, and chatbots, catering to the growing demand for high-quality, personalized content in sectors such as e-commerce and digital marketing. Moreover, the presence of tech giants like Google, Microsoft, and Amazon in North America, who are investing significantly in AI and machine learning, further fuels market growth. AI generators employ Machine Learning algorithms, Deep Neural Networks, and Natural Language Processing to generate content in multiple languages for global audiences.

Market Dynamics

Our researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and c

AI in Influencer Marketing Market Outlook

According to our latest research, the AI in Influencer Marketing market size reached USD 2.47 billion in 2024 globally, reflecting the rapidly growing adoption of artificial intelligence in digital marketing strategies. The market is projected to expand at a robust CAGR of 29.1% from 2025 to 2033, reaching an estimated USD 21.24 billion by 2033. This impressive growth trajectory is primarily driven by the increasing demand for data-driven influencer selection, campaign optimization, and real-time performance analytics, as brands globally seek to maximize ROI and minimize fraud in their influencer marketing initiatives.

One of the primary growth engines for the AI in Influencer Marketing market is the exponential surge in digital content consumption and the proliferation of social media platforms. As consumers spend more time on platforms like Instagram, TikTok, and YouTube, brands are investing heavily in influencer collaborations to reach targeted audiences. Artificial intelligence is revolutionizing this space by enabling advanced audience segmentation, facilitating the identification of micro and nano influencers, and automating content personalization. AI-powered platforms are now capable of analyzing vast datasets from multiple social channels, helping marketers make informed decisions regarding influencer partnerships and campaign strategies. This technology-driven approach leads to more authentic engagements, higher conversion rates, and improved transparency, which further accelerates market expansion.

Another significant factor fueling the growth of the AI in Influencer Marketing market is the increasing need for fraud detection and brand safety. Influencer fraud, including fake followers and engagement manipulation, has been a persistent challenge for brands. AI solutions are now providing sophisticated fraud detection algorithms that analyze engagement patterns, audience authenticity, and influencer credibility in real-time. This not only helps in safeguarding brand reputation but also ensures that marketing budgets are allocated efficiently. Additionally, AI-driven content optimization tools are empowering marketers to refine messaging, predict campaign outcomes, and enhance creative assets, resulting in more impactful influencer collaborations and higher ROI.

The evolution of AI-driven analytics is also reshaping performance measurement in influencer marketing. Traditional metrics such as likes and shares are being replaced with deeper insights, including sentiment analysis, audience demographics, and conversion attribution. AI technologies enable continuous monitoring and optimization of campaigns, allowing marketers to pivot strategies dynamically based on real-time data. This level of granularity and agility is particularly attractive to large enterprises and SMEs alike, as it enables them to stay competitive in a rapidly changing digital landscape. The growing integration of AI tools with existing CRM, marketing automation, and e-commerce platforms further amplifies the value proposition, fostering widespread adoption across diverse industry verticals.

From a regional perspective, North America dominates the AI in Influencer Marketing market, accounting for the largest revenue share in 2024, driven by the presence of leading technology providers, advanced digital infrastructure, and a mature influencer ecosystem. However, Asia Pacific is emerging as the fastest-growing region, propelled by the rapid expansion of social media users, increasing smartphone penetration, and the rising influence of Gen Z and millennial consumers. Europe is also witnessing substantial growth, particularly in the fashion, beauty, and retail sectors, as brands seek to leverage AI for enhanced customer engagement and regulatory compliance. Meanwhile, Latin America and the Middle East & Africa are gradually embracing AI-powered influencer marketing, supported by growing investments in digital transformation and evolving consumer preferences.

Component Analysis

The AI in Influencer Marketing market by component is segmented into Software and Services, each playing a pivotal role in shaping the overall market dynamics. The software segment comprises AI-powered platforms and tools that automate influencer identification, campaign management, fraud detection, and content optimization. These solutions are increasingly preferred by brands and agencies due to their scalability, accuracy, and ability to process massive datasets in real-time. Th