This dataset tracks influencer marketing campaigns across major social media platforms, providing a robust foundation for analyzing campaign effectiveness, engagement, reach, and sales outcomes. Each record represents a unique campaign and includes details such as the campaign’s platform (Instagram, YouTube, TikTok, Twitter), influencer category (e.g., Fashion, Tech, Fitness), campaign type (Product Launch, Brand Awareness, Giveaway, etc.), start and end dates, total user engagements, estimated reach, product sales, and campaign duration. The dataset structure supports diverse analyses, including ROI calculation, campaign benchmarking, and influencer performance comparison.

Columns: - campaign_id: Unique identifier for each campaign
- platform: Social media platform where the campaign ran
- influencer_category: Niche or industry focus of the influencer
- campaign_type: Objective or style of the campaign
- start_date, end_date: Campaign time frame
- engagements: Total user interactions (likes, comments, shares, etc.)
- estimated_reach: Estimated number of unique users exposed to the campaign
- product_sales: Number of products sold as a result of the campaign
- campaign_duration_days: Duration of the campaign in days

Getting Started with the Data

1. Load and Inspect the Dataset

import pandas as pd

df = pd.read_csv('influencer_marketing_roi_dataset.csv', parse_dates=['start_date', 'end_date'])
print(df.head())
print(df.info())

2. Basic Exploration

# Overview of campaign types and platforms
print(df['campaign_type'].value_counts())
print(df['platform'].value_counts())

# Summary statistics
print(df[['engagements', 'estimated_reach', 'product_sales']].describe())

3. Engagement and Sales Analysis

# Average engagements and sales by platform
platform_stats = df.groupby('platform')[['engagements', 'product_sales']].mean()
print(platform_stats)

# Top influencer categories by product sales
top_categories = df.groupby('influencer_category')['product_sales'].sum().sort_values(ascending=False)
print(top_categories)

4. ROI Calculation Example

# Assume a fixed campaign cost for demonstration
df['campaign_cost'] = 500 + df['estimated_reach'] * 0.01 # Example formula

# Calculate ROI: (Revenue - Cost) / Cost
# Assume each product sold yields $40 revenue
df['revenue'] = df['product_sales'] * 40
df['roi'] = (df['revenue'] - df['campaign_cost']) / df['campaign_cost']

# View campaigns with highest ROI
top_roi = df.sort_values('roi', ascending=False).head(10)
print(top_roi[['campaign_id', 'platform', 'roi']])

5. Visualizing Campaign Performance

import matplotlib.pyplot as plt
import seaborn as sns

# Engagements vs. Product Sales scatter plot
plt.figure(figsize=(8,6))
sns.scatterplot(data=df, x='engagements', y='product_sales', hue='platform', alpha=0.6)
plt.title('Engagements vs. Product Sales by Platform')
plt.xlabel('Engagements')
plt.ylabel('Product Sales')
plt.legend()
plt.show()

# Average ROI by Influencer Category
category_roi = df.groupby('influencer_category')['roi'].mean().sort_values()
category_roi.plot(kind='barh', color='teal')
plt.title('Average ROI by Influencer Category')
plt.xlabel('Average ROI')
plt.show()

6. Time-Based Analysis

# Campaigns over time
df['month'] = df['start_date'].dt.to_period('M')
monthly_sales = df.groupby('month')['product_sales'].sum()
monthly_sales.plot(figsize=(10,4), marker='o', title='Monthly Product Sales from Influencer Campaigns')
plt.ylabel('Product Sales')
plt.show()

Use Cases

• ROI Analysis: Quantify the return on investment for influencer campaigns across platforms and categories.
• Campaign Benchmarking: Compare campaign performance by type, influencer niche, or platform.
• Trend Analysis: Track engagement, reach, and sales trends over time.
• Influencer Selection: Identify high-performing influencer categories and campaign types for future partnerships.

1) Data Introduction • The Direct Marketing Campaigns (Bank Marketing) Dataset is a dataset built to predict time deposits (deposit) based on customer characteristics and campaign history in Portuguese banks' phone-based direct marketing campaigns.

2) Data Utilization (1) Direct Marketing Campaigns (Bank Marketing) Dataset has characteristics that: • Consisting of 41,188 rows, individual case data for calls made to customers during each row marketing campaign. • This dataset contains 21 columns (characteristics) that provide detailed information about each phone and attributes related to customers and campaigns. (2) Direct Marketing Campaigns (Bank Marketing) Dataset can be used to: • Marketing Campaign Performance Forecasting and Customer Targeting: Using customer characteristics and historical campaign data, it can be used to predict customers who are likely to sign up for time deposits and to establish effective marketing targeting strategies. • Customer Behavior Analysis and Marketing Strategy Optimization: You can optimize marketing strategies by analyzing campaign response patterns, characteristics by customer group, and correlations with economic indicators, and use them for customer segmentation and customized product suggestions.

This dataset provides detailed analytics on digital marketing campaigns, capturing key performance indicators such as impressions, clicks, conversions, costs, and ROI. It enables marketing teams to evaluate campaign effectiveness, optimize spending, and make data-driven decisions to improve future campaign outcomes. The dataset is ideal for campaign performance analysis, ROI tracking, and marketing strategy optimization.

This dataset provides a daily breakdown of conversion metrics for active marketing campaigns across multiple channels, including impressions, clicks, conversions, costs, revenue, and ROI. It enables detailed channel performance analysis, supports resource allocation decisions, and facilitates cross-campaign and cross-channel comparisons for data-driven marketing optimization.

Rachmad, Yoesoep Edhie. 2020. Measuring Marketing Campaign Effectiveness: Methodologies and Case Studies. International Journal of Campaign Effectiveness, Volume 18, No 1. https://doi.org/10.17605/osf.io/w6dpx

In the 2020 research publication "Measuring Marketing Campaign Effectiveness: Methodologies and Case Studies," featured in the "International Journal of Campaign Effectiveness," Volume 18, Issue 1, Yoesoep Edhie Rachmad explores the diverse methodologies used to assess the impact and effectiveness of marketing campaigns. The study also includes case studies that demonstrate these methodologies in action. Background: Situated within the broader context of marketing performance analysis, the research addresses the critical need for businesses to quantify the success of their marketing initiatives. As companies invest heavily in diverse marketing strategies, accurately measuring their effectiveness becomes essential for justifying expenditures and optimizing future campaigns. Definition and Basic Concepts: The study defines marketing campaign effectiveness as the degree to which marketing activities meet predefined objectives, such as increasing brand awareness, generating leads, or boosting sales. The research emphasizes the importance of selecting appropriate metrics and methodologies that align with the campaign's goals. Phenomenon: The phenomenon driving this research is the increasingly complex nature of marketing campaigns, which often span multiple channels and utilize various digital and traditional media. This complexity necessitates sophisticated measurement techniques that can provide a holistic view of campaign performance. Problem Formulation: The main challenge explored in the study is identifying the most effective methodologies for measuring the impact of marketing campaigns across different channels and media types. The research aims to determine which methods provide the most reliable and actionable insights. Research Objectives: The goal of the research is to evaluate and compare various methodologies for measuring marketing campaign effectiveness, and to illustrate these methodologies through real-world case studies. Qualitative Research Methodology: The methodology includes qualitative analyses of case studies from businesses that have implemented innovative measurement strategies. It also features expert interviews with marketing professionals who provide insights into the practical challenges and successes of different measurement approaches. Criteria and Respondent Selection: Respondents were selected based on their involvement in designing, implementing, or analyzing marketing campaigns with noted effectiveness. The study features 12 marketing professionals from a range of industries, including digital marketing, consumer goods, and B2B services. Research Indicators: Indicators of successful marketing campaign measurement include the accuracy of performance tracking, the ability to attribute results to specific activities, and the generation of insights that lead to tangible improvements in future campaigns. Operational Variables: These variables consist of the types of metrics used (e.g., ROI, conversion rates, engagement metrics), the tools and technologies employed for data collection and analysis, and the integration of data across various platforms. Determining Factors: Key factors identified include the clarity of campaign objectives, the alignment of measurement methods with these objectives, and the adaptability of measurement strategies to changing market conditions and technologies. Research Findings: The findings highlight that no single methodology fits all types of campaigns; instead, effectiveness measurement should be tailored to the specific goals and media of each campaign. The case studies illustrate how different companies successfully adapted measurement strategies to their unique needs. Conclusion and Recommendations: The study concludes that measuring marketing campaign effectiveness requires a combination of quantitative and qualitative methods. Recommendations for businesses include developing clear campaign objectives, selecting appropriate metrics, and continually refining measurement approaches based on past performance and evolving marketing landscapes. This research provides a comprehensive overview of methodologies for measuring marketing campaign effectiveness, offering valuable guidance for marketers seeking to enhance the accountability and performance of their campaigns.

This dataset provides detailed, synthetic logs of online ad campaign performance across multiple channels, capturing impressions, clicks, conversions, spend, and targeting details for each ad. It enables robust machine learning applications in ad optimization, audience targeting, and ROI prediction, supporting granular analysis by campaign, channel, device, and placement. Ideal for marketers, analysts, and data scientists seeking actionable insights into digital advertising effectiveness.

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.

The global campaign management software market is experiencing robust growth, driven by the increasing need for businesses to streamline their marketing efforts, improve campaign performance, and enhance customer engagement. The market's expansion is fueled by several key factors: the rising adoption of digital marketing strategies, the proliferation of data-driven decision-making, the increasing demand for automation and efficiency in campaign execution, and the growing complexity of modern marketing campaigns across multiple channels. A significant portion of this growth stems from enterprises adopting cloud-based solutions offering scalability, flexibility, and cost-effectiveness compared to on-premise systems. Furthermore, the market is witnessing a shift towards integrated platforms that unify various marketing functions, enabling seamless campaign management and data analysis. This consolidation simplifies workflows, reduces data silos, and improves overall campaign effectiveness. We estimate the market size in 2025 to be approximately $15 billion, with a Compound Annual Growth Rate (CAGR) of 12% projected from 2025 to 2033. This sustained growth trajectory reflects a continuous demand for innovative solutions that address the ever-evolving needs of marketers in a dynamic digital landscape. Key players like Adobe, IBM, Salesforce, SAS, and Oracle are actively shaping the market through continuous product innovation and strategic acquisitions. Geographic expansion, particularly in developing economies with burgeoning digital adoption rates, further contributes to the market's upward trend. However, the market faces challenges such as data security concerns, the need for skilled professionals to manage these sophisticated systems, and the ongoing evolution of marketing technologies, requiring constant adaptation and updates. The segmentation of the campaign management software market is primarily defined by software type (e.g., email marketing, social media marketing, search engine marketing, and multi-channel marketing platforms) and application (e.g., small and medium-sized businesses (SMBs), large enterprises, and various industry verticals like retail, healthcare, and finance). The North American market currently holds a substantial share, primarily due to higher technology adoption rates and a strong presence of major software vendors. However, rapid digitalization in regions like Asia-Pacific and Europe is expected to significantly boost market growth in these areas over the forecast period. While competition is intense, the market's vast size and potential for further innovation will continue to attract both established players and new entrants, creating a highly dynamic and competitive landscape. The increasing focus on data analytics and personalized marketing experiences will further fuel innovation and drive market expansion in the coming years.

The rankings looks at the performance of advertising campaigns. The index helps agencies measure the effectiveness of their campaigns as compared to their rivals. In 2023, the McCann Manchester / UM Manchester-led campaign for Aldi "Kevin versus John: How a humble carrot usurped a national treasure to win the UK’s Christmas Ad crown" was the most effective advertising campaign with an score of **** points. Advertisers continue to spend In 2021, advertisers were forecast to spend almost ***** billion British pounds in the United Kingdom. Digital ad spending has long overtaken the TV expenditures and has a good grip of four of five pounds invested in advertising in the country. The turn away from traditional spending channels and towards more digital areas has seen social media spend reach **** billion British pounds by 2019. Digital spending Digital advertising, also referred to as online, internet or web advertising, allows advertisers to bring promotional content to consumers using online technologies. It includes, among others, advertisements placed on social media platforms and search engine websites, banner ads on desktop as well as mobile websites and promotional messages delivered via email. 2021 has been estimated to see approximately **** billion British pounds in digital advertising spend in the UK.

This project involves analyzing a dataset of 2,240 customers from Maven Marketing to improve marketing strategies. By segmenting customers based on demographics and behavior, evaluating the success of past campaigns, and assessing channel performance, the goal is to uncover actionable insights that can drive future marketing efforts. The analysis will focus on identifying customer preferences, optimizing campaign strategies, and maximizing ROI. Using tools like Excel and Power BI, the project aims to create data-driven solutions for better customer engagement and business growth.

The performance marketing software market is experiencing robust growth, driven by the increasing adoption of digital marketing strategies and the need for efficient campaign tracking and optimization. The market, currently valued at approximately $8 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant expansion is fueled by several key factors. Firstly, the rise of e-commerce and the increasing reliance on affiliate marketing are boosting demand for sophisticated software solutions that manage complex marketing campaigns. Secondly, the growing adoption of data-driven decision-making in marketing necessitates tools that provide detailed analytics and insights into campaign performance, fostering the use of these platforms. Finally, the continuous evolution of marketing technologies and the integration of artificial intelligence (AI) and machine learning (ML) are enhancing the capabilities of performance marketing software, making it more efficient and effective. The market is segmented by various functionalities, pricing models, and deployment methods. Key players, including Offer18, Scaleo, Offerslook, CAKE Software, Affise, Refersion, TUNE, Trackier, Everflow, Voluum, Binom, Post Affiliate Pro, and Clickmeter, are actively engaged in product innovation and strategic partnerships to gain a competitive edge. While challenges remain, including data security concerns and the complexity of integrating different marketing channels, the overall outlook for the performance marketing software market remains highly positive, driven by ongoing technological advancements and the persistent demand for effective digital marketing solutions. The market’s sustained growth is expected to continue as businesses increasingly leverage data analytics for better marketing ROI.

During a 2022 survey carried out among marketing decision-makers worldwide, ** percent of responding advertisers said that time spent with content was the main key performance indicator (KPI) for the most recent content-led ad campaign they worked on; a shift in brand perceptions was named by an equal percentage of responding advertisers.

The Marketing Performance Management (MPM) software market is experiencing robust growth, projected to reach $574.2 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 6.5% from 2025 to 2033. This expansion is fueled by several key drivers. Increasing demand for data-driven marketing strategies across diverse industries necessitates sophisticated tools for measuring and optimizing campaign effectiveness. Businesses are increasingly adopting cloud-based solutions for enhanced scalability, accessibility, and cost-effectiveness, contributing significantly to market growth. Furthermore, the rising adoption of advanced analytics and AI-powered features within MPM software is enabling more precise targeting, personalized campaigns, and improved ROI measurement. The market segmentation reveals a strong preference for cloud-based MPM solutions, reflecting the broader industry trend towards cloud adoption. While on-premise solutions still hold a segment of the market, the cloud's flexibility and cost-efficiency are proving increasingly attractive. The application of MPM software in assessing marketing performance is paramount, driving the demand for tools capable of providing comprehensive insights into campaign efficiency, customer acquisition costs, and overall marketing ROI. The competitive landscape is characterized by a mix of established players like IBM, Microsoft, and Gartner, alongside specialized MPM vendors such as Bizible, Heinz Marketing, and Allocadia. These companies are continually innovating to enhance their offerings, including integrating advanced analytics, enhancing user experience, and expanding their product portfolios to cater to specific industry needs. Geographic distribution shows a significant market presence in North America and Europe, driven by higher digital marketing adoption rates and greater technological advancement in these regions. However, Asia Pacific is expected to exhibit strong growth potential in the coming years as digital marketing matures and businesses across developing economies increasingly prioritize data-driven marketing strategies. The continued expansion of digital marketing and the evolving needs of businesses will likely propel the MPM software market towards continued growth throughout the forecast period. The industry's focus on improving integration capabilities and providing more insightful data visualization tools will further accelerate market expansion.

Research data used in the paper entitled "The use of digital data analytics in the performance of advertising campaigns: the effect of absorptive capacity" published in Revista Brasileira de Gestão de Negócios (RBGN) V25, n3 (2023) Acess: https://rbgn.fecap.br/RBGN

The Marketing Performance Management (MPM) Software market is experiencing robust growth, projected to reach a market size of $892.7 million in 2025. While the exact CAGR isn't provided, considering the rapid adoption of cloud-based solutions and the increasing need for data-driven marketing decisions across various sectors, a conservative estimate of the CAGR for the forecast period (2025-2033) would be around 12-15%. This growth is fueled by several key drivers. Businesses are increasingly recognizing the importance of measuring and optimizing marketing ROI, leading to a surge in demand for sophisticated MPM software. The shift towards cloud-based solutions offers scalability, accessibility, and cost-effectiveness, further accelerating market expansion. The diverse application segments, including collaboration on marketing plans, expense management, and performance assessment, cater to a broad spectrum of marketing needs, driving adoption across various industry verticals. Competition is strong, with established players like IBM, Microsoft, and Gartner alongside specialized MPM vendors like Heinz Marketing and Bizible vying for market share. Future growth will likely be influenced by the continued development of AI-powered analytics capabilities within MPM platforms, enabling more precise campaign optimization and predictive modeling. The expansion of the software into emerging markets, particularly in Asia Pacific, will also play a significant role in shaping the market landscape in the coming years. The segmentation of the MPM software market into cloud-based and on-premise solutions, along with applications focused on marketing plan collaboration, expense management, and performance assessment, provides valuable insights into user needs and preferences. The geographical distribution reveals strong growth potential across North America and Europe, driven by high technological adoption rates and a mature marketing landscape. However, emerging markets in Asia Pacific and other regions present significant opportunities for future growth. Market restraints could include the initial investment costs associated with implementing MPM software and the need for robust data integration capabilities across various marketing platforms. Overcoming these hurdles through strategic partnerships and the development of user-friendly interfaces will be crucial for sustained market expansion.

Campaign Management Software Market size was valued at USD 3.64 Billion in 2023 and is projected to reach USD 8.12 Billion By 2030, growing at a CAGR of 12.2% during the forecast period 2024 to 2030.

Global Campaign Management Software Market Drivers

The market drivers for the Campaign Management Software Market can be influenced by various factors. These may include:

Growing Digitalization: As companies move more and more to digital platforms for customer involvement and marketing, there is a growing need for campaign management software. To effectively manage their digital marketing operations across several channels, including social media, email, search engines, and websites, businesses require efficient tools. The rise of omnichannel marketing: Is a result of consumers connecting with brands via a variety of channels, necessitating the smooth coordination and integration of various channels. Marketers may plan multichannel marketing campaigns with the help of campaign management software, which guarantees a unified brand experience at every touchpoint. Data-Driven Marketing: The use of data-driven marketing tactics has been prompted by the abundance of data from multiple sources, including as transactions, consumer interactions, and behavioral patterns. With campaign management software that combines machine learning and advanced analytics, marketers can tailor their messages for increased engagement and conversion, optimize campaigns in real-time, and extract actionable insights from data. Increasing Emphasis on Customer Experience: Businesses in all sectors now use customer experience as a major differentiation. With the use of campaign management software, businesses can provide their target audience with relevant and targeted content, increasing customer happiness and engagement. Developments in AI and Automation: By combining automation and artificial intelligence (AI) into campaign management software, marketers may more efficiently scale their campaigns, increase efficiency, and streamline operations. Marketing professionals can now make data-driven decisions and create more effective campaigns thanks to AI-powered capabilities like automatic campaign optimization, natural language processing, and predictive analytics. Demand for ROI tracking and performance measurement: Marketers are facing more and more demand to prove their campaigns' efficacy and defend their expenditures. With the help of powerful reporting and analytics features offered by campaign management software, marketers can monitor key performance indicators (KPIs), calculate return on investment, and adjust campaigns based on useful information Transition to Subscription-Based and Cloud-Based Solutions: The campaign management software industry is currently observing a change in favor of subscription-based pricing structures and cloud-based software deployment. This trend makes advanced marketing tools more accessible to companies of all sizes by enabling them to use them without having to make large upfront investments in software or hardware infrastructure.

In today's fast-paced digital landscape, the key to successful marketing lies in data-driven decision-making. At VisitIQ™, we empower businesses to maximize their marketing returns by leveraging the most comprehensive datasets and databases available. With VisitIQ™, you gain access to cutting-edge marketing data and insights that enable you to identify your ideal customers, optimize your targeting efforts, and accelerate your path to success.

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The performance marketing software market is experiencing robust growth, driven by the increasing adoption of digital marketing strategies and the need for efficient tracking and attribution of marketing campaigns. The market, estimated at $5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% during the forecast period (2025-2033), reaching an estimated value of $15 billion by 2033. This growth is fueled by several key factors: the rising demand for data-driven marketing decisions, the expansion of e-commerce and online advertising, and the increasing sophistication of marketing technologies. Key trends include the integration of artificial intelligence (AI) and machine learning (ML) for campaign optimization, the growing adoption of cross-channel attribution models, and the increasing focus on mobile and programmatic advertising. However, challenges remain, such as data privacy concerns, the complexity of integrating various marketing technologies, and the need for skilled professionals to manage these sophisticated software solutions. The market is segmented by deployment type (cloud-based, on-premise), by business size (small, medium, large enterprises), and by application (affiliate marketing, display advertising, email marketing, social media marketing). Leading players like Scaleo, Offerslook, CAKE Software, and Affise are constantly innovating and expanding their product offerings to maintain their market share in this dynamic landscape. The competitive landscape is characterized by a mix of established players and emerging startups, leading to a continuous evolution of features and functionalities. The market is highly fragmented, with both large multinational corporations and smaller specialized companies vying for market share. Geographical expansion, particularly in emerging markets with growing internet penetration, presents significant growth opportunities. To succeed, vendors must focus on providing user-friendly interfaces, seamless integrations with other marketing tools, robust analytics and reporting capabilities, and a commitment to data security and privacy. The integration of advanced analytics and predictive modeling will play a crucial role in shaping the future of performance marketing software, enabling marketers to make more data-driven decisions and optimize their campaigns for maximum return on investment (ROI).

This dataset contains 10,000 rows of marketing interaction data, designed to support multi-touch attribution (MTA) analysis. It records user interactions with various marketing channels and campaigns over a two-day period (February 10-11, 2025), along with conversion outcomes. The dataset is structured to help marketers analyze how different touchpoints contribute to customer conversions.

Purpose The dataset is useful for: • Multi-touch attribution modelling – Understanding the impact of each touchpoint in the customer journey. • Marketing performance analysis – Evaluating the effectiveness of different marketing channels and campaigns. • Machine learning applications – Training models to predict user conversion likelihood based on interaction patterns.

Data Structure The dataset consists of five columns, described below: • User ID: A unique identifier for each customer. • Timestamp: The exact date and time of the interaction. • Channel: The marketing channel where the interaction occurred. • Campaign: The specific marketing campaign associated with the interaction. ‘-’ indicates no campaign. • Conversion: Indicates whether the user converted (Yes) or not (No).

Key Insights: • Unique Users: 2,847 • Most Frequent Channel: Direct Traffic (~17.2%) • Campaign Involvement: 31.3% of interactions had no campaign assigned. • Conversion Rate: 49.44% of interactions resulted in a conversion.

Potential Use Cases: • Identifying the most influential marketing channels in driving conversions. • Using machine learning algorithms to predict user conversion probability. • Comparing rule-based attribution models (e.g., linear, time decay) with data-driven approaches (e.g., Markov Chains, Shapley Value).

This dataset is well-suited for marketing analytics, machine learning experiments, and data-driven decision-making.

Performance-driven influencer marketing campaigns were more common than awareness-driven ones in Indonesia, Malaysia, and Thailand in 2021. In Indonesia, performance-driven influencer marketing campaigns took up ** percent in 2021.