BETTER INVESTMENT DECISIONS
WITH RELEVANT DATA
ASTUTEX is an alternative data platform that helps you make better
investment decisions by tracking millions of data points and linking them
to a universe of around 1000 companies worldwide - mainly B2C -
where this data matters most. We back-test all our indices, where
possible, so that you can judge their relevance yourself.
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USE CASES
Aggregated Search
Kering is the the owner of Gucci, one of the most spectacular growth stories in the luxury sector, which experienced a dramatic turnaround under creative direction of Alessandro Michele. Around 55% of Gucci’s sales in 2017 were made to customers under 35. Michele seems to have figured out what young people want – bright, eclectic colours and patterns that are engaging, eye-catching and look good on Instagram. The growing number of web searches for Gucci was clearly signalling increase in popularity.
Social Media
In June 2018 Boohoo.com announced a new collection in collaboration with Paris Hilton, celebrity followed by 9 million people on Instagram. After Paris started posting under hashtag
#ParisHiltonXBoohoo around 1 million were added to Boohoo audience giving impulse for future revenue growth. We track all meaningful social media platforms for changes in fan numbers and engagement (post likes, comments, re-shares) data on a weekly basis.
NLP
NLP sentiment is a numerical expression of business conditions and helps to identify long-term trends influencing company stock price. We can see how Burberry recovered from the slump as the slide in GBP gave a tailwind for a stock price turnaround.
Online Retail
Online Retail rating shows the number of Nike products in e-commerce platforms bestseller lists in relevant categories. Rating increase in May 2018 shows that recent product launches are trending well with the customers.
We collect and analyse the following:
- Aggregated search data
- Social media
- Website traffic
- Online retail channels
- Company and CEO approval ratings
- Hiring patterns
- Natural language processed (NLP) sentiment
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