About

Ubiqitum is an AI-native brand health platform designed to make real-time brand data accessible to every size of business.

Ubiqitum (ubiqitum.com) is a lightweight, self-service brand sentiment platform that generates:

Targeted survey insights across 20 key brand metrics.

Results are returned within 48 hours, and reports can be filtered by demography, geography and business sector/type (B2B/B2C).

It is designed to deliver +/-1% survey accuracy under the headings of: 1. Brand awareness, 2. Brand credibility, 3. Brand- spend efficiency

Why Ubiqitum?

Until now, the process of calibrating brand performance and health was time consuming and expensive – well out of reach for all but the largest and most experienced brand owners. By affordably and responsively enabling brands to test 20+ facets of brand performance with a high degree of certainty, Ubiqitum places power in the hands of brand owners to act with confidence – proving good decisions or informing course corrections in almost real time. For these brands and their marketing partners (agencies and brand consultancies), Ubiqitum enables the establishment of control data, then provides a longitudinal brand performance assessment to guide and prove the effectiveness of their work.

Problems Ubiqitum addresses

Incomplete, slow or biased data

Ubiqitum eliminates data gaps, delays, and biases by seamlessly collating and analysing real-time behavioural signals and audience sentiment from diverse sources. By combining declared data (“what people say”) with observed actions (“what people do”), we deliver a comprehensive perspective of a brand’s health.

Difficulty measuring brand propensity and trust

Ubiqitum quantifies intangible attributes like brand propensity, trust, and credibility. This gives leaders a clear understanding of the factors that truly fuel growth and customer loyalty

Synthetic data lacking real-world context

Ubiqitum ensures accuracy by cross-referencing cached insights with live behaviour analytics and market signals. This approach reduces noise and grounds our findings in real-world dynamics, providing clarity and relevance.

Skewed metrics from relatively small samples

Quantitativeonline ‘panel’ surveys are conducted using paid respondents. These people are often unrepresentative of target audiences (how many panels are you on?). Instead, Ubiqitum scans entire markets of ‘normal’ consumers to uncover the true drivers behind brand strength, market momentum, and genuine purchase intent. This could involve thousands or even millions of consumers.

The high cost of traditional research

As our name suggests, Ubiqitum is priced to make deep, insightful and accurate data ubiquitous.

How Ubiqitum works

Ubiqitum fuses behavioural data with brand sentiment signals to deliver real-time insights into brand awareness, credibility, and marketing spend efficiency. Its architecture combines dynamic neural network frameworks, Bayesian predictive analytics, adaptive natural language processing (NLP), and self-learning algorithmic refinement mechanisms, operating through a proprietary five-layer neural network architecture, as follows:

Ubiqitum bias mitigation

Real-time outlier and anomaly detection

Adaptive weighting and normalization

Demographic representation adjustment

Algorithmic fairness testing

Real-time feedback and self-corrective learning

Ubiqitum accuracy

Testing the accuracy of quantitative research has long been challenging. Reliance on panels of volunteer respondents often raises concerns around data accuracy and panel bias. We chose to test Ubiqitum in a truly unique and verifiable way – by testing political brands on the eve of elections, then comparing our results with the actual election outcome.In every test case, Ubiqitum has predicted a result that is within 1% of the actual election outcome. This provides us with what we believe to be a higher level of confidence than would be achieved through all but the largest quantitative test audiences.