LikeFolio Consumer Insights

LikeFolio  surfaces consumer buying trends in the retail space by looking at shifts in consumer demand and sentiment at the brand and company level, as well as macro behavior shifts that impact holiday shopping.

Understand in real-time: HOW, WHERE, and WHY consumers are spending money.

Leverage social media data to answer critical questions and surface opportunities BEFORE the market catches on...including: is eCommerce here to stay? What are consumer expectations related to shipping? What brands and companies are exhibiting unusually high (or low) levels of consumer demand? How is event-based spending (think back-to-school and Holiday travel plans) impacting where consumers are spending their money and to what degree.

Spot major winners and losers this Holiday season, BEFORE earnings are released.

Data Description

This listing consists of one dataset that contains Company-level, Brand-level, and Trend-level data related to multiple facets of consumer spending: essentials, discretionary, retail, services, and transactions. Company- and Brand-level data contain 4 metrics (Mention Count, Purchase Intent, Positive Sentiment, Negative Sentiment) from 2012 to present. Trend-level data contains 1 metric (Mention Count) from 2012 to present. The dataset covers 32 Companies (publicly traded equities), 22 brands, and 29 macro trends.

Purchase Intent Data is generated when a social media user tweets about a brand or product in a manner that indicates the user has very recently, is currently, or is definitely planning to do business with the company. For AAPL this might be "got a new iPhone", for YUM it might be "drive thru at taco bell is", for SFIX it might be "got my first stichfix".

Positive Consumer Sentiment Data is generated when a social media user tweets something positive about a brand or product owned by the covered company.

Negative Consumer Sentiment Data is generated when a social media user tweets something negative about a brand or product owned by the covered company

Mention Count Data is generated when a social media user tweets about any brand or product (including common slang and misspellings).

Proof

LikeFolio's 2019 data performance white paper gives examples of how this data may be used to effectively predict future stock price movement, especially leading into quarterly earnings reports.

A study by Georgetown University , based purely on LikeFolio Purchase Intent data, concluded that it is "predictive of upcoming sales, and the unexpected component of sales growth at the firm level."