Our Data Store is the infrastructure where we collect and store the data generated by visitors across your data sources. By connecting data from online and offline sources, you can gain a comprehensive understanding of visitor behavior, which is immediately available for real-time analysis and reporting in the Qubit platform.
It combines a centralized data supply chain with high-volume data storage that is globally replicated with low-latency serving that allows us to serve data very fast, which is a key requirement for delivering highly-personalized experiences.
Our Data Store contains all the data about your customers. We call this an elastic record of the customer. Such a record can consist of data from various sources:
QProtocol events typically capture:
For example, if a site visitor is looking at a specific product, through QProtocol, we can collect:
The quantitative data collected allows us to create a comprehensive visitor history. This data is made available throughout the Qubit platform for analysis of visitor behavior, reporting on key vertical-relevant metrics, and designing ultra-relevant and personalized experiences.
Alongside quantitative data, Qubit enables you to collect qualitative data by collecting feedback from visitors through Qubit's Visitor pulse and Qubit's Opinions.
Visitor pulse enables you to create custom surveys to collect qualitative feedback from your visitors on any topic you wish to investigate. The data is stored in Qubit's Data Store and is always available to give you the very latest picture of visitor perception.
See Visitor pulse for more information.
Opinions surfaces qualitative data by collecting visitor sentiments in a free-form contact modal, triggered on the page to a set percentage of visitors, when we detect they are about to leave your site.
Opinions asks the visitor a single open question along the lines of How can we improve? and provides a free text area for them to input their thoughts.
The response is sent to the Data Store where it is first translated into English. The response is then run through our natural language processing system to break it down into statements.
Each of these statements is then categorized against a taxonomy relevant to your vertical. Finally, we perform sentiment analysis to determine whether it was a positive or negative comment.
See Opinions for more information.