Datasets:
Please request access to receive a free sample of 1000 reviews as well as information about larger datasets or custom datasets.
Contact us at [email protected] for more details and questions. Visit https://fantastic.app to learn more about how our dataset is created.
What problem is Fantastic solving?
Personalization is no longer a nice to have for today's consumers, it is now a necessity. According to a recent study by Mckinsey "Seventy-one percent of consumers expect companies to deliver personalized interactions. And seventy-six percent get frustrated when this doesn’t happen!"
Although the stakes for personalization have never been higher, it is increasingly difficult for businesses to deliver personalized interactions while respecting consumer data privacy. As explained in a recent Zendesk CX Trends Report "62% of consumers want more personalized experiences, but only 21% strongly agree that businesses are doing enough to protect their data." Investing in architecture to collect, clean, categorize, and preserve consumer interest data is a costly process for many businesses and often creates friction for consumers expecting instant personalization.
Fantastic closes this personalization gap for businesses by collecting consumer interest data from a panel of users who are rewarded for sharing their favorite products, content, and services. This data is cleaned and categorized by age, gender, city, and country to make it easy for businesses to uncover patterns. This data is granted full consent for commercial use and anonymized for user privacy, ready for instant use in delivering personalized interactions.
How is Fantastic unique?
Since 2017, Fantastic has been building a platform that makes it easy and rewarding for consumers to share their favorite products and content. This leads to an authentic and detailed dataset shaped directly by the voice of the consumer. Our dataset is dynamic and continuously growing, enabling businesses to stay up to date with shifting consumer trends.
Use cases
Instant Personalization – Overcome the cold start problem for recommender systems by enriching your users' profiles with interest data from our dataset.
Improve Ad Conversions – Create more effective advertisements by understanding shifting consumer preferences. Use insights from various audiences' favorite content, products, and media to reach your intended audience with marketing that resonates.
Create Products & Content People Love – Leverage consumer interests from outside your ecosystem to gain insights on shifitng market trends, gather competitive intelligence, and adopt highly-requested product features.
Product details
Consumer interests represented in fantastic_insights_dataset table
Free sample dataset consists of 1000 user reviews
Full dataset consists of 30,000+ user reviews from ~1000 audience panel members
Audience panel members are located in the United States and represent all major U.S. regions and demographics. The most represented demographics in the dataset are 18-35 males and 18-35 females in Southern California.
Each dataset row is a positive review of a product/service/content from users on our platform. Each row includes the following fields:
- gender (User gender: Male | Female | Non-Binary)
- age_range (User age range: 13-17 | 18-24 | 25-34 | 35-44 | 45-54 | 55-64 |65+ )
- city_name (User city)
- state_name (User state)
- country_name (User country)
- user_count (Number of users that endorse the review, for multiple endorsers)
- subject (Product, service, or content endorsed)
- description (Description of product, service, or content)
- link (Link to product, service, or content)
- image_link (Link to image of product, service, or content)
- tag_1 (User provided category for product, service or content)
- tag_2 (User provided category for product, service or content)
- tag_3 (User provided category for product, service or content)
- tag_4 (User provided category for product, service or content)
- tag_5 (User provided category for product, service or content)
- tag_6 (User provided category for product, service or content)
- tag_7 (User provided category for product, service or content)
- tag_8 (User provided category for product, service or content)
- tag_9 (User provided category for product, service or content)
- tag_10 (User provided category for product, service or content)
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