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Marketing Promotion Mix Sales Data
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About
Offers crucial insights into the effectiveness of various marketing channels—specifically TV, social media, radio, and influencer promotions—in driving sales revenue. Each row corresponds to an independent marketing promotion where the promotional budget allocated across different channels is recorded alongside the resulting sales figures. This data allows for robust analysis and prediction modeling of marketing promotion mix strategies.
Columns
- TV: The promotional budget allocated for television campaigns, categorized into discrete levels: Low, Medium, and High.
- Social Media: The financial budget dedicated to social media promotions, measured in millions of dollars.
- Radio: The budget assigned for radio advertising campaigns, measured in millions of dollars.
- Sales: The total sales revenue generated as a result of the promotion, measured in millions of dollars.
- Influencer: The size or tier of the influencer involved in the promotion, categorized as Nano, Micro, Macro, or Mega.
Distribution
The data is structured in a tabular format, typically distributed as a CSV file (marketing_sales_data.csv). The dataset contains 5 columns and 572 valid records. The file size is approximately 26.65 kB. The expected update frequency is Never.
Usage
This data is ideal for developing predictive models, such as linear regression, to forecast sales outcomes based on promotional input variables. Key applications include optimising marketing budget allocation, evaluating the return on investment (ROI) for individual marketing channels (e.g., assessing if Medium TV budgets are more effective than High Social Media budgets), and quantifying the impact of different influencer size tiers on revenue generation.
Coverage
The source material focuses exclusively on promotional budget levels and subsequent sales outcomes. Specific geographic locations, time ranges, or demographic details of the consumer base are not included in the dataset features.
License
CC0: Public Domain
Who Can Use It
- Data Scientists and Machine Learning Engineers: For training statistical and machine learning models aimed at sales prediction and variable importance analysis.
- Marketing Strategists: To inform strategic decisions regarding media spend distribution and channel prioritisation.
- Business Analysts: To conduct exploratory data analysis (EDA) and understand correlations between specific marketing efforts and sales performance.
Dataset Name Suggestions
- Marketing Promotion Mix Sales Data
- Media Budget Allocation and Sales Metrics
- Advertising ROI Prediction Dataset
- Influencer and Traditional Media Sales Analysis
Attributes
Original Data Source: Marketing Promotion Mix Sales Data
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