In theory, the AI based Pricing Process may provide a technical framework for building an economically efficient Dynamic and Personalised Pricing, capturing the perceived value of the product for every single customer. In practice, such Pricing tactics might cause some unwanted serious side-effects, and raise huge concerns among customers regarding the fairness of such decisions.
In other words, the experience of Pricing Optimisation and Revenue Management, developed in the Airline, showed that customers are not radically against the principle of price discrimination but are rather concerned with the discrimination criteria.
Therefore, the massive use of more and more complicated Machine learning and AI models, such as Ensemble Models and Neural Networks , with a unique objective of capturing the maximum value from each single customer transaction, makes it difficult to interpret and to understand the discrimination criteria. This may be badly perceived by the customers and considered as an ambiguous, arbitrary and unfair decisions.
To drive growth, businesses often prioritise scaling acquisition and paid advertising. However, neglecting to analyse and fix product mechanics can hinder scalability. Prioritising product mechanics before aggressive acquisition and paid ads is crucial for success.
This blog post compares four of the best open-source BI and dashboarding tools: Apache Spark, Metabase, Grafana, and Redash. We'll explore their features, target audience, strengths, and licensing models to help you make an informed decision.
In this blog post, we'll compare top open-source product analytics tools - PostHog, Matomo, Countly, OpenReplay, Plausible Analytics, Unami, and GrowthBook - that empower businesses to understand user behaviour, optimize experiences, and make data-driven decisions for product growth and development in today's data-centric landscape.