Combining your digital footprint and technology to predict your morning coffee
Utilizing IBM’s Personality Insights API and an algorithm that analyzes different types of coffee roasts, Barista predicts coffee selections and provides personalized coffee recommendations based on personality insights.
Initial hypothesizing was crucial to develop a strategy for data collection, in order to effectively build a machine learning experiment that optimizes from feedback provided to enhance our AI strategy.
Using AI, this API maps data from Twitter to the “Big Five” personality traits, also known as OCEAN (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism).
In order to effectively create a product that uses the feedback provided by users to learn, adapt, and improve recommendations, a stellar data storage selection was crucial.
Using these two modern web technologies, we are able to push the boundaries for product integrations and drive success within AI experiments.
Personality analysis and a learning algorithm can be used to prioritize content being presented to a user. If a consumer makes their social profile available to an e-commerce site, a consumer needs analysis could be used in a product sort to filter out products that the user is not likely to consume, while suggesting products that could drive them to a higher quality or quantity of consumption. Along the same lines, understanding how a consumer behaves online could help to target them with relevant content in data-driven marketing campaigns.