Digital Marketing Analytics

This dataset provides an in-depth look at customer interactions and campaign performance within the digital marketing realm. It includes key metrics and demographic information that are crucial for analyzing marketing effectiveness and customer engagement. The dataset comprises the following columns:

Customer Id:

Unique identifier for each customer, facilitating individual tracking and analysis.

Age:

Customer's age, offering insights into demographic segmentation and targeting strategies.

Gender:

Customer's gender, useful for understanding gender-based preferences and behavior.

Income:

Customer's income level, providing context on purchasing power and conversion potential.

Campaign Channel:

The medium through which the marketing campaign was delivered (e.g., email, social media).

Campaign Type:

The nature of the marketing campaign (e.g., promotional offer, product launch), helping to assess campaign effectiveness.

Ad Spend:

Amount spent on advertisements, crucial for evaluating cost-efficiency and ROI.

Click Through Rate (CTR):

Ratio of clicks to impressions, indicating ad engagement and effectiveness.

Conversion Rate:

Percentage of users who complete a desired action after interacting with an ad, reflecting the success of the campaign in driving actual sales or goals.

Website Visit:

Number of visits to the website by the customer, measuring engagement and interest.

This dataset is ideal for exploring customer behavior, optimizing marketing strategies, and evaluating the success of various campaigns. Perfect for data scientists and marketers looking to derive actionable insights from digital marketing data.

Social media companies are starting to offer users the option to subscribe to their platforms in exchange for monthly fees. Until recently, social media has been predominantly free to use, with tech companies relying on advertising as their main revenue generator. However, advertising revenues have been dropping following the COVID-induced boom. As of July 2023, Meta Verified is the most costly of the subscription services, setting users back almost 15 U.S. dollars per month on iOS or Android. Twitter Blue costs between eight and 11 U.S. dollars per month and ensures users will receive the blue check mark, and have the ability to edit tweets and have NFT profile pictures. Snapchat+, drawing in four million users as of the second quarter of 2023, boasts a Story re-watch function, custom app icons, and a Snapchat+ badge.

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Description:

The advertising dataset captures the sales revenue generated with respect to advertisement costs across multiple channels like radio, tv, and newspapers.

It is required to understand the impact of ad budgets on the overall sales.

Acknowledgement:

The dataset is taken from Kaggle

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t a single & multiple features.
• Also evaluate the models & compare their respective scores like R2, RMSE, etc.

During a 2024 survey among marketers worldwide, approximately 83 percent selected increased exposure as a benefit of social media marketing. Increased traffic followed, mentioned by 73 percent of the respondents, while 65 percent cited generated leads.

              The multibillion-dollar social media ad industry

              Between 2019 – the last year before the pandemic – and 2024, global social media advertising spending skyrocketed by 140 percent, surpassing an estimated 230 billion U.S. dollars in the latter year. That figure was forecast to increase by nearly 50 percent by the end of the decade, exceeding 345 billion dollars in 2029. As of 2024, the social media networks with the most monthly active users were Facebook, with over three billion, and YouTube, with more than 2.5 billion.

              Pros and cons of GenAI for social media marketing

              According to another 2024 survey, generative artificial intelligence's (GenAI) leading benefits for social media marketing according to professionals worldwide included increased efficiency and easier idea generation. The third place was a tie between increased content production and enhanced creativity. All those advantages were cited by between 33 and 38 percent of the interviewees. As for GenAI's top challenges for global social media marketing,
              maintaining authenticity and the value of human creativity ranked first, mentioned by 43 and 40 percent of the respondents, respectively. Another 35 percent deemed ensuring the content resonates as an obstacle.

During a 2024 survey among marketers worldwide, around 86 percent reported using Facebook for marketing purposes. Instagram and LinkedIn followed, respectively mentioned by 79 and 65 percent of the respondents.

              The global social media marketing segment

              According to the same study, 59 percent of responding marketers intended to increase their organic use of YouTube for marketing purposes throughout that year. LinkedIn and Instagram followed with similar shares, rounding up the top three social media platforms attracting a planned growth in organic use among global marketers in 2024. Their main driver is increasing brand exposure and traffic, which led the ranking of benefits of social media marketing worldwide.

              Social media for B2B marketing

              Social media platform adoption rates among business-to-consumer (B2C) and business-to-business (B2B) marketers vary according to each subsegment's focus. While B2C professionals prioritize Facebook and Instagram – both run by Meta, Inc. – due to their popularity among online audiences, B2B marketers concentrate their endeavors on Microsoft-owned LinkedIn due to its goal to connect people and companies in a corporate context.

Marketing Analytics Market Outlook

According to our latest research, the global marketing analytics market size in 2024 stands at USD 5.8 billion, demonstrating robust momentum driven by the increasing adoption of data-driven decision-making across industries. The market is projected to register a CAGR of 13.2% from 2025 to 2033, reaching an estimated market size of USD 17.1 billion by 2033. This accelerated growth is primarily attributed to the proliferation of digital channels, the surge in big data, and the imperative for organizations to achieve higher ROI from their marketing investments. The marketing analytics market is evolving rapidly, with advanced analytics tools enabling businesses to gain actionable insights, optimize campaigns, and enhance customer engagement across diverse sectors.

One of the most significant growth factors for the marketing analytics market is the exponential increase in data generation from multiple digital touchpoints. The rise of omnichannel marketing strategies has resulted in vast and complex datasets, encompassing customer interactions from social media, websites, mobile applications, and email campaigns. Businesses are increasingly leveraging marketing analytics solutions to aggregate, process, and analyze this data in real time, gaining deeper insights into customer behavior, preferences, and purchase patterns. The ability to transform raw data into actionable intelligence is empowering marketers to personalize campaigns, improve targeting accuracy, and maximize conversion rates, thereby fueling the demand for sophisticated analytics platforms.

Another critical driver is the growing emphasis on measuring marketing effectiveness and optimizing marketing spend. As organizations face mounting pressure to justify marketing budgets and demonstrate tangible ROI, marketing analytics tools have become indispensable. These solutions enable marketers to track key performance indicators (KPIs), attribute revenue to specific channels, and identify underperforming campaigns. The integration of artificial intelligence and machine learning into marketing analytics platforms is further enhancing predictive capabilities, allowing businesses to forecast trends, automate campaign adjustments, and refine customer segmentation. This technological evolution is driving widespread adoption across both large enterprises and small and medium businesses.

The surge in regulatory requirements and data privacy concerns is also shaping the marketing analytics market. With the implementation of stringent data protection regulations such as GDPR and CCPA, organizations are compelled to adopt analytics solutions that ensure compliance while maintaining data integrity and security. Modern marketing analytics platforms are incorporating advanced data governance features, encryption, and anonymization techniques, enabling businesses to harness the power of analytics without compromising customer trust. This focus on compliance, coupled with the increasing need for transparency in marketing practices, is accelerating the adoption of analytics tools across regulated industries such as BFSI and healthcare.

Regionally, North America dominates the marketing analytics market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The United States, in particular, is at the forefront due to the presence of major analytics vendors, high digital adoption, and substantial marketing expenditure by enterprises. However, the Asia Pacific region is poised for the fastest growth over the forecast period, driven by rapid digital transformation, expanding e-commerce ecosystems, and increasing investments in marketing technology. Latin America and the Middle East & Africa are also witnessing steady growth as organizations in these regions recognize the strategic value of data-driven marketing.

Component Analysis

The marketing analytics market is segmented by component into software and services, each playing a vital role in the overall ecosystem. The software segment dominates th

Gain valuable insights with our comprehensive Social Media Dataset, designed to help businesses, marketers, and analysts track trends, monitor engagement, and optimize strategies. This dataset provides structured and reliable social media data from multiple platforms.

Dataset Features

User Profiles: Access public social media profiles, including usernames, bios, follower counts, engagement metrics, and more. Ideal for audience analysis, influencer marketing, and competitive research. Posts & Content: Extract posts, captions, hashtags, media (images/videos), timestamps, and engagement metrics such as likes, shares, and comments. Useful for trend analysis, sentiment tracking, and content strategy optimization. Comments & Interactions: Analyze user interactions, including replies, mentions, and discussions. This data helps brands understand audience sentiment and engagement patterns. Hashtag & Trend Tracking: Monitor trending hashtags, topics, and viral content across platforms to stay ahead of industry trends and consumer interests.

Customizable Subsets for Specific Needs Our Social Media Dataset is fully customizable, allowing you to filter data based on platform, region, keywords, engagement levels, or specific user profiles. Whether you need a broad dataset for market research or a focused subset for brand monitoring, we tailor the dataset to your needs.

Popular Use Cases

Brand Monitoring & Reputation Management: Track brand mentions, customer feedback, and sentiment analysis to manage online reputation effectively. Influencer Marketing & Audience Analysis: Identify key influencers, analyze engagement metrics, and optimize influencer partnerships. Competitive Intelligence: Monitor competitor activity, content performance, and audience engagement to refine marketing strategies. Market Research & Consumer Insights: Analyze social media trends, customer preferences, and emerging topics to inform business decisions. AI & Predictive Analytics: Leverage structured social media data for AI-driven trend forecasting, sentiment analysis, and automated content recommendations.

Whether you're tracking brand sentiment, analyzing audience engagement, or monitoring industry trends, our Social Media Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

Context

Digital advertising is promotional material delivered to a target audience through digital platforms, including social media, email, search engines, mobile apps, affiliate programs, and websites. One of the main benefits of digital advertising is an advertiser can track in real-time the success of the campaign. The goal of digital advertising is to inorganically advertise where consumers are and to customize ads to the target audience's preferences.

Content

Technology in the digital advertising space is fast-paced and evolving at lightning speed. Competing effectively requires every company to constantly transform itself with the latest industry trends and standards. Whether you want to integrate new technology solutions into your existing advertising cloud or want to build a proprietary solution from scratch. Through this Dataset, you will be able to explore how different attributes of Digital Advertisement are impacting the revenue of a product company. There are many factors of Digital advertisement which impact business campaign such as WebSites, Geographical conditions, Type of devices, Monetization channels, total impressions, etc. In this Ad-Tech Dataset, we have 17 columns which will help you to explore the world of Digital Advertisement - date

site_id

ad_type_id

geo_id

device_category_id

advertiser_id

order_id

line_item_type_id

os_id

integration_type_id

monetization_channel_id

ad_unit_id

total_impressions

total_revenue

viewable_impressions

measurable_impressions

revenue_share_percent

Use your Analytical and ML skills to explore data, find corelation between fields, and predict total revenue in order to know the Impact of Digital Advertisement for any Business.

Inspiration

By exploring this dataset you will able to understand how much digital advertisement can impact your business in today's world.

Context

In the last decade, new ways of shopping online have increased the possibility of buying products and services more easily and faster than ever. In this new context, personality is a key determinant in the decision making of the consumer when shopping. A person's buying choices are influenced by psychological factors like impulsiveness; indeed some consumers may be more susceptible to making impulse purchases than others. Since affective metadata are more closely related to the user's experience than generic parameters, accurate predictions reveal important aspects of user's attitudes, social life, including attitude of others and social identity. This work proposes a highly innovative research that uses a personality perspective to determine the unique associations among the consumer's buying tendency and advert recommendations. In fact, the lack of a publicly available benchmark for computational advertising do not allow both the exploration of this intriguing research direction and the evaluation of recent algorithms. We present the ADS Dataset, a publicly available benchmark consisting of 300 real advertisements (i.e., Rich Media Ads, Image Ads, Text Ads) rated by 120 unacquainted individuals, enriched with Big-Five users' personality factors and 1,200 personal users' pictures.

Content

The content of the zip files are folders. The directory tree of this disk is as follows:

20 Ads folder: Ads belong to 20 product/service categories. all the ads are here. 120 Users Folders: Each folder contains data for one of the involved subjects. 300 real advertisements have been scored, Ratings according to the users’ interests (1 star to 5 stars), ~1,200 personal pictures (labelled as positive/negative), Big-Five personality scores (O-C-E-A-N).

Data can be easily analysed in Matlab, or Python

Acknowledgements

If you use our dataset please cite:

[1] Roffo, G., & Vinciarelli, A. (2016, August). Personality in computational advertising: A benchmark. In 4 th Workshop on Emotions and Personality in Personalized Systems (EMPIRE) 2016 (p. 18).

Inspiration

We collected and introduced a representative benchmark for computational advertising enriched with affective-like metadata such as personality factors. The benchmark allows to (i) explore the relationship between consumer characteristics, attitude toward online shopping and advert recommendation, (ii) identify the underlying dimensions of consumer shopping motivations and attitudes toward online in-store conversions, and (iii) have a reference benchmark for comparison of state-of-the-art advertisement recommender systems (ARSs). To the best of our knowledge, the ADS dataset is the first attempt at providing a set of advertisements scored by the users according to their interest into the content. We hope that this work motivates researchers to take into account the use of personality factors as an integral part of their future work, since there is a high potential that incorporating these kind of users' characteristics into ARS could enhance recommendation quality and user experience.

During a January 2024 global survey among marketers, nearly 60 percent reported plans to increase their organic use of YouTube for marketing purposes in the following 12 months. LinkedIn and Instagram followed, respectively mentioned by 57 and 56 percent of the respondents intending to use them more. According to the same survey, Facebook was the most important social media platform for marketers worldwide.

Salutary Data is a boutique, B2B contact and company data provider that's committed to delivering high quality data for sales intelligence, lead generation, marketing, recruiting / HR, identity resolution, and ML / AI. Our database currently consists of 148MM+ highly curated B2B Contacts ( US only), along with over 4M+ companies, and is updated regularly to ensure we have the most up-to-date information.

We can enrich your in-house data ( CRM Enrichment, Lead Enrichment, etc.) and provide you with a custom dataset ( such as a lead list) tailored to your target audience specifications and data use-case. We also support large-scale data licensing to software providers and agencies that intend to redistribute our data to their customers and end-users.

What makes Salutary unique? - We offer our clients a truly unique, one-stop aggregation of the best-of-breed quality data sources. Our supplier network consists of numerous, established high quality suppliers that are rigorously vetted. - We leverage third party verification vendors to ensure phone numbers and emails are accurate and connect to the right person. Additionally, we deploy automated and manual verification techniques to ensure we have the latest job information for contacts. - We're reasonably priced and easy to work with.

Products: API Suite Web UI Full and Custom Data Feeds

Services: Data Enrichment - We assess the fill rate gaps and profile your customer file for the purpose of appending fields, updating information, and/or rendering net new “look alike” prospects for your campaigns. ABM Match & Append - Send us your domain or other company related files, and we’ll match your Account Based Marketing targets and provide you with B2B contacts to campaign. Optionally throw in your suppression file to avoid any redundant records. Verification (“Cleaning/Hygiene”) Services - Address the 2% per month aging issue on contact records! We will identify duplicate records, contacts no longer at the company, rid your email hard bounces, and update/replace titles or phones. This is right up our alley and levers our existing internal and external processes and systems.

Here are a few use cases for this project:

1. Brand Analysis: Marketing teams can use LA-LDN to analyze the presence and visibility of specific brands in public spaces, events, or social media posts. This information can help businesses understand the success of advertising campaigns, consumer trends, and brand recognition.

2. Counterfeit Detection: Retailers, designers, and manufacturers can use LA-LDN to detect counterfeit products by identifying inconsistencies or discrepancies in logos on clothing, accessories, and other items. Reducing counterfeits can help protect brand integrity and customer experience.

3. Sponsorship Measurement: Companies and event organizers can use LA-LDN to measure the impact of sponsorship deals by analyzing the visibility and frequency of sponsored logos in event photos, videos, or online media coverage. This can help them evaluate the return on investment for sponsorships and make data-driven decisions for future partnerships.

4. Customer Behavior Insights: By analyzing customer-generated content (such as social media posts), businesses can gain insights into customer behavior and preferences, such as favorite brands, brand associations, and purchase motivations. This information can guide marketing strategies and product development.

5. Logo Redesign Evaluation: Companies planning to update or redesign their logo can use LA-LDN to compare the performance of the updated logo against the old one in terms of visibility and recognition in real-world scenarios, like in-store displays, billboards, or website traffic. This can help them determine the effectiveness of the redesign and gather feedback for further refinements.

How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              Who uses social media?
              Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
              when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

              How much time do people spend on social media?
              Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

              What are the most popular social media platforms?
              Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

Here are a few use cases for this project:

Use Case 1: Gender-Based Retail Analytics By analyzing customer demographics in retail stores, the "man vrouw dataset 1" can help retailers understand the gender distribution of their shoppers, empowering them to make informed decisions on store layout, marketing strategies, and product placements.

Use Case 2: Crowd Monitoring and Event Management This model can help enhance safety and optimize visitor experience at crowded events, such as concerts or festivals, by identifying the gender distribution of attendees, enabling promoters to customize services, restrooms allocation, and security measures accordingly.

Use Case 3: Digital Advertising and Marketing Using the "man vrouw dataset 1" model, businesses can better target their digital advertisements by understanding the key demographic visiting specific websites or engaging with specific content, allowing for tailored ad campaigns designed to target male or female audiences.

Use Case 4: Smart Surveillance and Security Systems The model can be used in surveillance and security systems to help identify and track people by their HU classes (man or vrouw) in premises like airports or corporate buildings, allowing security teams to analyze patterns and prevent potential threats.

Use Case 5: Social Media Image Analysis The "man vrouw dataset 1" model can be used to analyze the gender composition of social media images, providing insights into trends, preferences, and behaviors of different gender groups on social platforms. This information can then be used for targeted marketing or social research purposes.

This dataset is about book subjects. It has 1 row and is filtered where the books is Like follow engage : 7 digital marketing strategies that your business must use today. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

It facilitates monitoring of the EU’s digital targets for 2030 set by the Digital Compass for the EU's Digital Decade, evolving around four cardinal points: skills, digital transformation of businesses, secure and sustainable digital infrastructures, and digitalization of public services.

The aim of the European ICT usage survey is to collect and disseminate harmonised and comparable information on the use of Information and Communication Technologies and e-commerce in enterprises at European level.

Coverage:

The characteristics to be provided are drawn from the following list of subjects:

• ICT systems and their usage in enterprises,
• use of the internet and other electronic networks by enterprises,
• e-commerce,
• e-business processes and organisational aspects,
• ICT competence in the enterprise and the need for ICT skills,
• barriers to the use of ICT, the internet and other electronic networks, e-commerce and e-business processes,
• ICT security and incidents,
• access to and use of the internet and other network technologies for connecting objects and devices (Internet of Things),
• access to and use of technologies providing the ability to connect to the internet or other networks from anywhere at any time (ubiquitous connectivity),
• use of Artificial Intelligence,
• use of Cloud computing,
• data analytics,
• use of 3D printing,
• use of robotics,
• use of social media,
• internet advertising
• ICT and the environment.

Breakdowns:

• by size class,
• by NACE Rev. 2 categories,
• by NUTS 2 regions (until 2010 and on optional basis since 2023)

Here are a few use cases for this project:

1. E-commerce Catalog Categorization: Online retail platforms can use the 'al items' model to automatically classify and tag images of products within the decor category such as stockings, ribbons, tree skirts, etc. This will aid in enhancing product searching and filtering, improving overall user experience.

2. Interior Design Planning: The model could be used in apps or software that help its users visualize and plan their interior design. By identifying different decor items in real or virtual spaces, it can provide suggestions for improvement or create a shopping list.

3. Automated Retail Inventory Management: Retail stores can utilize this model to scan their inventory, keeping track of decor items. This would automate the process of inventory management and decrease human errors.

4. Augmented Reality Shopping Apps: AR shopping apps can use this model to recognize decor items at the user's home and suggest similar or matching products from their inventory. It could help to personalize the shopping experience.

5. Social Media Advertising: Businesses could use this model to monitor user-uploaded images on social media, identify their product's usage or preference and accordingly run targeted advertising campaigns.

How much time do people spend on social media?

              As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in
              the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively.
              People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general.
              During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

Success.ai’s Ecommerce Store Data for the APAC E-commerce Sector provides a reliable and accurate dataset tailored for businesses aiming to connect with e-commerce professionals and organizations across the Asia-Pacific region. Covering roles and businesses involved in online retail, marketplace management, logistics, and digital commerce, this dataset includes verified business profiles, decision-maker contact details, and actionable insights.

With access to continuously updated, AI-validated data and over 700 million global profiles, Success.ai ensures your outreach, market analysis, and partnership strategies are effective and data-driven. Backed by our Best Price Guarantee, this solution helps you excel in one of the world’s fastest-growing e-commerce markets.

Why Choose Success.ai’s Ecommerce Store Data?

1. Verified Profiles for Precision Engagement

   • Access verified profiles, business locations, employee counts, and decision-maker details for e-commerce businesses across APAC.
   • AI-driven validation ensures 99% accuracy, improving engagement rates and reducing outreach inefficiencies.

2. Comprehensive Coverage of the APAC E-commerce Sector

   • Includes businesses from major e-commerce hubs such as China, India, Japan, South Korea, Australia, and Southeast Asia.
   • Gain insights into regional e-commerce trends, digital transformation efforts, and logistics innovations.

3. Continuously Updated Datasets

   • Real-time updates ensure that business profiles, employee roles, and operational insights remain accurate and relevant.
   • Stay aligned with dynamic market conditions and emerging opportunities in the APAC region.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 700M+ Verified Global Profiles: Access business profiles for e-commerce professionals and organizations across APAC.
• Firmographic Insights: Gain detailed information, including business locations, employee counts, and operational details.
• Decision-maker Profiles: Connect with key e-commerce leaders, managers, and strategists driving online retail innovation.
• Industry Trends: Understand emerging e-commerce trends, consumer behavior, and market dynamics in the APAC region.

Key Features of the Dataset:

1. Comprehensive E-commerce Business Profiles

   • Identify and connect with businesses specializing in online retail, marketplace management, and digital commerce logistics.
   • Target decision-makers involved in supply chain optimization, digital marketing, and platform development.

2. Advanced Filters for Precision Campaigns

   • Filter businesses and professionals by industry focus (fashion, electronics, grocery), geographic location, or employee size.
   • Tailor campaigns to address specific goals, such as promoting technology adoption, enhancing customer engagement, or expanding supply chains.

3. Regional and Sector-specific Insights

   • Leverage data on APAC’s fast-growing e-commerce markets, consumer purchasing trends, and regional challenges.
   • Refine your marketing strategies and outreach efforts to align with market priorities.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Outreach

   • Promote e-commerce solutions, logistics services, or digital commerce tools to businesses and professionals in the APAC region.
   • Use verified contact data for multi-channel outreach, including email, phone, and social media campaigns.

2. Partnership Development and Vendor Collaboration

   • Build relationships with e-commerce marketplaces, logistics providers, and payment solution companies seeking strategic partnerships.
   • Foster collaborations that drive operational efficiency, enhance customer experiences, or expand market reach.

3. Market Research and Competitive Analysis

   • Analyze regional e-commerce trends, consumer preferences, and logistics challenges to refine product offerings and business strategies.
   • Benchmark against competitors to identify growth opportunities and high-demand solutions.

4. Recruitment and Talent Acquisition

   • Target HR professionals and hiring managers in the e-commerce industry recruiting for roles in operations, logistics, and digital marketing.
   • Provide workforce optimization platforms or training solutions tailored to the digital commerce sector.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality e-commerce store data at competitive prices, ensuring strong ROI for your marketing, sales, and strategic initiatives.

2. Seamless Integration

   • Integrate verified e-commerce data into CRM systems, analytics platforms, or market...

Introduction Paid Search is one of the most efficient way to promote your products or services nowadays as we all know. Companies ''fight'' to get the best keywords to appear in the first google search page.

About the dataset The dataset is a real dataset contanining data of a 2021 5 months paid search campaign of a Us shopping mall (that for privacy reasons for this project the name has been anonymized) promoting through paid search coupons and promo codes. The dataset contains information about the ad group advertising, clicks, conversions, impressions, revenues and costs(see below the dataset dictionary).

Dataset Dictionary:

Ad Group: category of the advert (coupon/promo code, desktop ad/mobile ad etc...)

Month: month of the campaign. The campaign started in July 2021 and ended in November 2021.

Impressions: metric used in digital marketing to quantify the number of digital views or engagements of an advertisement. Impressions are also referred to as an "ad view.

Clicks: how many clicks the ad received

CTR: Click Through Rate, the number of clicks that your ad receives divided by the number of times your ad is shown: clicks ÷ impressions = CTR.

Conversions: Conversions are those valuable actions that users take on your site like buying something or filling in a form. The success can be measured in the number of conversions generated at a particular cost.

Conv Rate: Conversion Rate. It is the percentage of people who convert after clicking on your ads. Depending on your goals, a conversion may mean they make a purchase, complete a contact form, request a free trial, or take another desired action.

Cost: Cost is the actual money spent by the advertiser (the "shop") for the related ad group.

CPC: Cost Per Click, it is the cost of the specific ads divided by the click. It is one of the metrics used to evaluate the effectiveness of the campaign in terms of ROI (Return on Investment), therefore a low or decreasing CPC is better than a high or increasing CPC.

Revenue: Revenue is the total amount of income generated by advertisment.

Sale Amount: Sale Amount for this dataset means the quantity of sales derived by the single ad group.

P&L: Profit and Loss, based on the formula Revenue - Cost. For this dataset mesaures the profit of the specific Ad Group.