The generated dataset simulates marketing interaction data for 500 users, including a range of engagement metrics and user behavior features. Below is a detailed description of the columns in the dataset:

Columns: User_ID: A unique identifier for each user (e.g., '001', '002', etc.).

Likes: The number of likes the user has given to posts, normalized to a range of 0 to 1.

Shares: The number of times the user has shared posts, normalized to a range of 0 to 1.

Comments: The number of comments the user has made on posts, normalized to a range of 0 to 1.

Clicks: The number of times the user has clicked on posts, ads, or links, normalized to a range of 0 to 1.

Engagement_with_Ads: The level of interaction the user has had with advertisements, normalized to a range of 0 to 1.

Time_Spent_on_Platform: The amount of time the user spends on the platform (in minutes), normalized to a range of 0 to 1.

Purchase_History: A binary value indicating whether the user has made a purchase (1 for purchased, 0 for not purchased).

Text_Features: Text data that simulates user interactions with marketing-related content (e.g., posts, advertisements). The text has been transformed using TF-IDF (Term Frequency-Inverse Document Frequency) to extract important keywords.

Engagement_Level: A categorical value indicating the level of user engagement with the platform, including "High", "Medium", and "Low".

Purchase_Likelihood: A binary target variable that indicates the likelihood of a user making a purchase. It is encoded as:

1 (Likely) if the user is predicted to make a purchase. 0 (Unlikely) if the user is predicted to not make a purchase.

📬 Open Email Marketing Q&A Dataset

This dataset contains 1,000 carefully written Q&A pairs about email list buying, lead generation, and cold email strategy. It's built for developers, marketers, AI trainers, and SEO professionals who want structured, high-quality content around the real-world challenges of email marketing.

Every entry in this set was manually created, based on actual industry knowledge and frequently asked questions from marketers and sales teams. The content is designed to be practical, accurate, and easy to reuse in training models, answering search queries, or powering automation tools.

🧾 Summary

• File name: leadsblue_qna_dataset.jsonl
• Entries: 1,000 Q&A pairs
• Format: JSONL
• Fields per entry:
  • question: Natural question about email lists or outreach
  • answer: Clear, accurate response based on industry standards
  • keywords: Relevant terms for indexing, SEO, or topic modeling
  • source_url: (Optional) link to related page on LeadsBlue.com

🧠 What It’s For

This dataset was created to support a wide range of use cases:

• LLM Training: Teach large language models to understand email marketing, data compliance, and lead buying questions with contextual accuracy.
• SEO Enrichment: Populate long-tail keyword clusters with human-sounding Q&A content that Google and other search engines can index.
• AI Assistants / Chatbots: Provide fast, contextual answers to common sales and compliance questions around email outreach.
• Market Research: Analyze recurring concerns and informational gaps among sales and marketing teams.

📌 Sample Entry

{
 "question": "Is it legal to buy an email list for marketing in the US?",
 "answer": "Yes, it is legal to buy email lists in the United States, provided the outreach complies with the CAN-SPAM Act. This law requires clear opt-out options, a valid sender address, and no deceptive subject lines.",
 "keywords": "buy email list, CAN-SPAM, email marketing compliance, US cold email laws",
 "source_url": "https://leadsblue.com/sales-leads/buy-business-email-list-and-sales-leads/"
}

The emergence of new media and the shift away from traditional media toward digital services has particularly prompted a change in media buying strategies. Since almost all companies are undergoing a digital transformation, media buying agencies must specialize in online advertising to adapt to the changing media landscape. Data-driven insights and programmatic advertising have propelled the industry forward. With rising consumer spending and corporate profit, businesses increasingly pour more resources into advertising to capture larger market shares. Media buying agencies have been riding this wave, capitalizing on the surging demand. Media Buying Agencies revenue has increased at a CAGR of 3.3% to a total of $13.8 billion in 2025, including an estimated 1.9% in the current year, while profit reaches 6.5%. The industry has witnessed rapid transformation driven by digital innovation and shifting consumer behaviors. Advertisers have gravitated toward digital platforms, spurred by the drastic transition from traditional media. This shift resulted in digital spending overtaking traditional media investments, with giants like Facebook, Google, and Amazon capturing significant market shares. The emergence of programmatic ad buying and data analytics has revolutionized how agencies target audiences, allowing for more precise and efficient campaigns. Amid this evolution, consolidation among major players like Omnicom and WPP has heightened competition, pushing smaller firms toward niche markets or out of the industry altogether. These dynamics have underscored the importance of adapting to technological advancements and economic changes to remain competitive. Over the next five years, businesses are poised to increase their advertising budgets to capitalize on rising consumer activity, providing significant opportunities for media buying agencies. The phase-out of third-party cookies and increasing emphasis on first-party data will drive agencies to focus on privacy-compliant strategies, while AI-driven programmatic advertising will continue to transform the industry. Agencies will expand services, offering integrated, multi-channel strategies and leveraging influencer marketing to tap into niche markets. The expansion of digital platforms has given access to niche markets that were harder to reach in the past. Companies increasingly turn to media buying agencies to seek integrated marketing solutions that harness cross-platform potential, driving revenue growth. Nonetheless, the proliferation of digital ad space, declining prices and waning demand for traditional advertising will limit industry growth. Overall, industry revenue is poised to hike at a CAGR of 1.8% to $15.1 billion in 2030.

On September 30, 2020, AB 323 (Ch. 341, Stats. 2020) was passed and chaptered into law thereby establishing the requirement for DGS to post on its Internet website purchases related to marketing and outreach advertising materials for every state office, officer, department, division, bureau, board, and commission identified in GC 11000, and including the California State University. These purchases must be further disaggregated to show placement of marketing and outreach advertising materials targeting communications with specific ethnic communities including but not limited to Latino, African American, Asian-Pacific Islander, Indigenous, Middle Eastern and LGTBQIA communities as outlined by Public Contract Code 11800-11804.

The dataset consists of Consumer buying behaviour of FMCG products in connection with cause-related marketing.Dataset is based on questionnaire having thirteen five point scale likert scale statements along with the demographic variables.The questionnaire is drafted based on factors contributing to consumer buying behaviour of cause-related marketing such as information available on product packaging,Brand image and Celebrity endorsement.The responses of likert scale statements were in the form of 'Strongly Agree', 'Agree', Neutral', 'Disagree', Strongly Disagree', and they were coded as 5,4,3,2,1 respectively for positive statements and 1,2,3,4,5 respectively for negative statements.

Social-Media Tips to Enhance Your Marketing

Are social media marketing and content marketing two disparate entities, or could they perhaps be a marriage made in heaven? Unfortunately, many brands approach both as if one has nothing to do with the other. The simple fact is that social media marketing buy instagram followers and likes package cannot function without content. If you have no content, you have nothing to share, tweet or post. Without valuable content, you cannot drive engagement on social media. Therefore, it only stands to reason that content serve as the heart of any successful social media marketing campaign.With that said, unlike traditional content marketing, content within the sphere of social media marketing must serve specific purposes. To be effective, content for social media marketing must be designed to fit the parameters of specific platforms and, furthermore, must be developed to either generate discussion or provide an open dialogue for current customers. The tips below will guide you through the process of bringing social media and content marketing together. Buy Instagram Likes Start with Content First

Social media is without a doubt vital to any successful marketing campaign. With that said, in order to develop a successful social media campaign, you need good content. Your prospective customers will not follow you on Twitter, Facebook or any other channel if you do not provide relevant, interesting and valuable content. This means that before you can even begin to think about launching a social media campaign, you must first have a solid content marketing plan that includes quality material. The key with a successful content marketing campaign is to make sure it does not come off as too promotional. You will not see much success if all of your content is about your company, your deals, and offers. The best course of action you can take is to position your company so that you are buy instagram likes cheap and fast recognized as an expert in your respective field. One way to do this is by producing content that includes helpful resources, tips, guides, etc. Many firms are hesitant to provide this type of information for free because they believe their customers will not want to pay for their services. The goal here is for your target customers to be so impressed by what you have to say that they will begin to follow you regularly and contact you. Additionally, it is important to remember that it is possible to give away some information but not everything. Test content for effectiveness with your audience.

Simply publishing content on social media and hoping it sticks is not an effective plan. Testing a variety of content and messages across different networks can help you to determine which type of content resonates best with your audience. If you only publish one piece where to buy instagram likes of content and you do not receive the response you expected, you may never know exactly what was wrong with it. A/B testing can give you the insight you need to determine how to best connect with specific audiences.

The online group buying market, encompassing deals on diverse products and services from books and music to home appliances and travel experiences, is experiencing robust growth. Driven by increasing internet penetration, smartphone usage, and a consumer preference for value-driven shopping, this sector demonstrates significant potential. The B2C segment currently dominates, fueled by individual consumers seeking discounts and deals. However, the B2B segment shows promising growth as businesses leverage group buying platforms for cost-effective procurement of goods and services. While North America and Europe currently hold significant market shares, the Asia-Pacific region, particularly China and India, is poised for rapid expansion due to the burgeoning middle class and increasing digital adoption. Competitive pressures among established players like Amazon and Alibaba, alongside smaller niche players, are driving innovation and the development of more sophisticated platforms offering personalized deals and enhanced user experiences. Challenges remain, such as managing logistics, ensuring vendor reliability, and combating fraudulent activities. The overall market trajectory suggests continued expansion, albeit with fluctuations influenced by economic conditions and evolving consumer preferences. The market's expansion is projected to continue throughout the forecast period (2025-2033), although the CAGR may moderate slightly as the market matures. Factors influencing this include increased competition, evolving consumer behaviors, and economic factors. While specific segment growth rates are not provided, we can reasonably assume higher growth in the Asia-Pacific region and within the B2B segment compared to more mature markets and the B2C segment, respectively. The success of individual players will depend on their ability to adapt to changing market conditions, innovate in areas such as personalized recommendations and loyalty programs, and effectively manage their supply chains. Effective marketing strategies focusing on value proposition and user experience will also be critical for sustained success.

TechCorner Mobile Purchase & Engagement Data (2024-2025)

Context

TechCorner Mobile Sales & Customer Insights is a real-world dataset capturing 10 months of mobile phone sales transactions from a retail shop in Bangladesh. This dataset was designed to analyze customer location, buying behavior, and the impact of Facebook marketing efforts.

The primary goal was to identify whether customers are from the local area (Rangamati Sadar, Inside Rangamati) or completely outside Rangamati. Since TechCorner operates a Facebook page, the dataset also includes insights into whether Facebook marketing is effectively reaching potential buyers.

Additionally, the dataset helps in determining: ✔ How many customers are new vs. returning buyers ✔ If customers are followers of the shop’s Facebook page ✔ Whether a customer was recommended by an existing buyer

This dataset is valuable for:

Retail sales analysis to understand product demand fluctuations.

Marketing impact measurement (Facebook engagement vs. actual purchase behavior).

Customer segmentation (local vs. non-local buyers, social media influence, word-of-mouth impact).

Sales trend analysis based on preferred phone models and price ranges.

With a realistic, non-uniform distribution of daily sales and some intentional missing values, this dataset reflects actual retail business conditions rather than artificially smooth AI-generated data.

Marketing & Customer Queries

Does he/she Come from Facebook Page? → Whether the customer came from a Facebook page (Yes/No). Used to analyze Facebook marketing reach.

Does he/she Followed Our Page? → Whether the customer is already a follower of the shop’s Facebook page (Yes/No). Helps measure brand loyalty and organic engagement.

Did he/she buy any mobile before? → Whether the customer is a repeat buyer (Yes/No). Determines the percentage of returning customers.

Did he/she hear of our shop before? → Whether the customer knew about the shop before purchasing (Yes/No). Identifies the impact of referrals or previous marketing efforts.

Was this customer recommended by an old customer? → Whether an existing customer referred them to the shop (Yes/No). Helps evaluate the effectiveness of word-of-mouth marketing.

Acknowledgements

This dataset is derived from real-world mobile sales transactions recorded at TechCorner, a retail shop in Bangladesh. It accurately reflects customer purchasing behavior, pricing trends, and the effectiveness of Facebook marketing in driving sales. Special appreciation to TechCorner for providing comprehensive insights into daily sales patterns, customer demographics, and market dynamics.

This dataset can be used for:

📊 Predictive modeling of sales trends based on customer demographics and marketing channels. 📈 Marketing effectiveness analysis (impact of Facebook promotions vs. organic sales). 🔍 Clustering customers based on purchasing habits (new vs. returning buyers, Facebook users vs. walk-ins). 📌 Understanding demand for different smartphone brands in a local retail market. 🚀 Analyzing how word-of-mouth recommendations influence new customer acquisition.

💡 Can you build a model to predict if a customer is likely to return? 💬 How effective is Facebook in driving actual sales compared to walk-ins? 🔍 Can we cluster customers based on behavior and brand preferences?

Success.ai’s Consumer Marketing Data for Food, Beverage & Consumer Goods Professionals Globally provides a comprehensive dataset tailored for businesses seeking to connect with decision-makers and marketing professionals in these dynamic industries. Covering roles such as brand managers, marketing strategists, and product developers, this dataset offers verified contact details, decision-maker insights, and actionable business data.

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Promotional posts of influencers showed a higher impact on consumer shopping behavior in Japan than referrals without a promotional character. As revealed in a survey conducted in ***********, fashion was the most common product category named by respondents who made purchases following posts published by influencers. While more than ** percent stated that they were influenced by fashion-related submissions tagged as promotional, the share was significantly lower for posts without any advertising intentions.

Buy real estate agent data in the USA with verified emails, phone numbers, and company details. Instantly download accurate, up-to-date realtor contact lists for marketing and lead generation.

Discover the booming Media Buying Services market! Our analysis reveals a $150B market in 2025, growing at a 12% CAGR. Explore key trends, regional insights, and leading companies shaping programmatic buying, digital advertising, and data-driven marketing strategies.

The aim of this research is to assess the main theories about consumer behavior/making decision from the social psychology perspective, to understand the intention to adopt electronic channel and, in this way, to determine the precursors of online purchase intention in an emerging economy, and to compare these precursors with the precursors in a developed economy through a cross-cultural study: H1. Consumer online purchase intention has a positive effect on online purchase behavior H1a. The effect of online purchase intention on online purchase behavior is moderated by culture H2. Attitude toward e-commerce has a positive effect on online purchase intention H2a. The effect of attitude toward e-commerce on online purchase intention is moderated by culture H3. Subjective norms have a positive effect on online purchase intention H3a. The effect of subjective norms on online purchase intention is moderated by culture H4. PBC has a positive effect on online purchase intention H4a. The effect of PBC on online purchase intention is moderated by culture H5. Self-efficacy in online stores has a positive effect on online purchase intention H5a. The effect of self-efficacy in online stores on online purchase intention is moderated by culture H6. EOU of e-stores has a positive effect on attitudes toward e-commerce H6a. The effect of EOU of e-stores on attitudes toward e-commerce is moderated by culture H7. The perceived usefulness of online stores has a positive effect on consumers’ attitudes towards online shopping H7a. National culture moderates the effect of perceived usefulness on attitudes toward online shopping H8. Buying impulse has a positive effect on online purchase intention H9. The perceived EOU of e-stores has a positive effect on buying impulse. H8a. The effect of buying impulse on online purchase intention is moderated by culture H9a. The effect of EOU on buying impulse is moderated by culture H10. Compatibility with e-commerce has a positive effect on online purchase intention H10a. The effect of compatibility on online purchase intention is moderated by culture H11. PIIT has a positive effect on online purchase intention H11a. The effect of PIIT on online purchase intention is moderated by culture

This statistic depicts social media platforms most commonly used in order to buy from artists from 2018 to 2019. According to the source, ** percent of respondents stated that they used Instagram in order to buy from artists in 2019.

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The global Media Buying Services market is booming, projected to reach $150 billion in 2025 with a 12% CAGR. Learn about key drivers, trends, and leading companies shaping this dynamic industry, including programmatic buying, digital advertising, and regional market analysis. Explore the future of media buying and its impact on various sectors.

Group Buying Market size was valued at USD 6.06 Billon in 2023 and is projected to reach USD 20.22 Billion by 2031, growing at a CAGR of 16.2% during the forecast period 2024-2031.

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The market drivers for the Group Buying Market can be influenced by various factors. These may include:

Consumer Demand for Discounts: One of the main motivators is the demand for sales and discounts. Because they may acquire goods and services at much lower prices by combining their purchasing power, consumers are drawn to group buying platforms. Social Media and Digital Marketing: Group buying has expanded thanks in part to the emergence of social media and digital marketing. Social media is a tool that platforms utilize to interact with people, advertise promotions, and increase traffic to their websites.

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Issues with consumer trust: Group buying platforms often require members to commit to a purchase before a critical mass is attained. If the group doesn't reach the required size, customers can feel misled or frustrated, which could erode trust and produce negative perceptions. Quality Control: With group purchasing, it can be challenging to ensure the standard of the products or services received. If customers are afraid they won't receive high-quality goods or services, they can be unwilling to participate.

Customers Purchase Behavior Dataset is a collection of data that tracks customer purchase behavior. The dataset includes information about customers, products, promotions, and the place where the purchases were made through. This data can be used to understand customer buying habits, identify trends, and develop marketing campaigns.

The dataset contains information about 400 customers, including their unique ID, gender, age, salary, and whether they decided to buy specific products or not. The dataset also includes information about 100 products, including their name, category, price, and whether they were promoted. Finally, the dataset includes information about 50 promotions, including their name, type, and start and end dates.

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