Marketing Strategies in Equity Crowdfunding: A Comparative Study of Italian Platforms
Bibliographic record
Abstract
This paper explores equity crowdfunding platforms from a marketing perspective. The present exploratory study attempts to make a double contribution to the current literature on equity crowdfunding. Firstly, it analyzes the marketing strategies of the platforms by focusing on the well-known 4Ps marketing mix framework, i.e. product, price, promotion and placement. Each dimension presents three types of categories. Second, the study investigates the marketing strategies of both large platforms and small platforms, then the differences between these two types of platforms are examined in terms of campaigns’ outcomes, i.e. funding collected (in %), funding amount (in €) and number of investors. Platforms adopt a standardization strategy for pricing and placement, while a differentiation strategy is mainly adopted for promotion and products. Large platforms offer a wider range of services (in particular ongoing campaign services and post-campaign services) and promotional activities (in particular leverage many communication channels). The analyses disclose significant statistically differences between these two types of platforms. Projects posted on large platforms are more likely to get higher campaigns’ outcomes. In literature, little is known about marketing strategies in equity crowdfunding platforms, thus this study tries to fill this gap. The paper is the first to analyze the 4Ps of platforms and to conduct a comparative empirical study to determine the differences of campaigns’ outcomes between large and small platforms. The Italian context represents a significant case of developed country in theme of equity crowdfunding. The results are useful for platform managers, entrepreneurs, investors and authorities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".