BloomReach pulled the curtains back on its new Continuous Quality Management (CQM) platform, a so-called web relevance engine that leverages machine learning to gauge the quality of web pages.

CQM breaks down quality into four metrics: relevance, popularity, uniqueness and freshness. Relevance is determined based on the correlation between the content of a page and its designated topic, popularity is determined by user engagement indicators, and uniqueness is measured using BloomReach’s homegrown Dynamic Duplication Reduction (DDR) technology. The company defines freshness, or content dynamism, as the “rate of change of products on the page.” These metrics are continuously updated so that marketers can integrate the data into their decision-making process and act upon it in a timely fashion.

“The perfect balance of technology for generating high-quality content and relevant experiences are those that let machines do what they do best – interpret and act on unmanageable amounts of data – and leave the fine tuning and quality assurance to humans,” said Raj De Datta, the CEO of BloomReach. “The launch of CQM gives e-tail marketers a proven tool to manageably capture the long tale at scale with high quality, relevant content.”

De Datta discussed the role of Big Data in quantifying consumer happiness, a metric as complex as it is abstract, in a recent interview with SiliconAngle news editor Kristen Nicole. The CEO explained that the key to understanding whether the quality of your content meets users’ expectations is to look at multiple data points and contextualize accordingly. He illustrates this by noting that a prospective customer who stumbles upon an ad on Facebook behaves differently than a person who visits a site from a search engine.

De Datta says that measuring and addressing customer happiness is like diagnosing a rare disease. It’s difficult, but Big Data offers a cure.