Acua is the organizing theme of our research efforts, focusing on audience, customer, and user analytics to enhance understanding of these populations for an organization. Our efforts concentrate on research for collecting, measuring, analyzing, and reporting digital data to enhance insights into the behavior of audiences, customers, and users, with the development of systems to support these activities central to the research. The Acua goal is better analytics for better decisions for better results!
Our research and development efforts focus on four systems (“The Big 4”), making them viable commercially and research-wise. “The Big 4” are clustered in the digital analytics area and are strongly coupled for both research and development.
● Acua: https://acua.qcri.org/
● METRIC: https://metric.qcri.org/
● S2P: https://s2p.qcri.org/
● Persona Analytics: https://persona.qcri.org/
● Acua: From interaction with stakeholders and experiences with other digital analytics platforms, we have identified a need for a customer segmentation platform that is both robust and easy to use. The competitive advantages of Acua are that it does not rely on a single data stream (e.g., Google Analytics), and it masks the complexity of algorithmic analytics for segmentation, unlike many other tools (e.g., Enterprise SPSS). We already have a robust proto-type developed.
● METRIC: From interaction with stakeholders and experience with other user understanding platforms, we have identified a need for an inexpensive suite of tools for conducting user studies and real-time customer experiments. METRIC supports conducting user studies in our other systems and has been packaged as its own product. Based on the experience from Persona Analytics user studies, we will further develop a complete stand-alone system. We are currently employing METRIC with Acua and S2P in understanding user engagement.
● Survey2Persona (S2P): No extant system generates personas from survey data; yet, survey data is the most common form of data that organizations and researchers rely on. S2P conducts an automated analysis of survey data to create personas. We already have a robust proto-type developed.
● Persona Analytics: As personas have, until APG, been static (i.e., paper), there is no current research or development of measures and metrics to understand user interaction and user behavior in online persona systems. Given our work on APG, we have a significant headstart for understanding the challenges in this area.
● Acua: There are several research challenges, the major one being the alignment of Key Performance Indicators (KPIs) to appropriate algorithms and actionable metrics, along with many user interface issues of both initial user input and presentation of results. We target enterprises that desire complete segmentation services but lack the skills to implement complex algorithmic solutions. These enterprises may also lack the infrastructure and human resources for complex customer segmentation.
● METRIC: There are several research challenges, including measures, metrics, and methods of analysis, most notably the identification of interpage elements and integration with the range of customer-facing channels (e.g.. webpages, apps, systems, etc.). There are also significant challenges with measurement accuracy with gaze and mouse movements internal to page elements.
● S2P: There are several research challenges, including the conversion from survey questions to respondent statements, so this is a complex NLP challenge. Then, there are several challenges in the statistical and algorithmic analysis of various types of survey data (e.g., boolean, categorical, Likert, open-ended). Also, there is the challenge of realistic presentation of the personas in terms of image and name with demographic factors such as gender identity and ethnicity in heterogeneous societies.
● Persona Analytics: There are several research challenges, including measures, metrics, and analysis methods. To our knowledge, this is the first effort in this area, so there are no existing solutions.
● Acua: Robust prototype at https://acua.qcri.org/. The target end-user base is enterprise clients that desire complex customer segmentation within an easy-to-employ platform. Other impacts would be research publications and/or IP Disclosures.
● METRIC: Robust prototype at https://metric.qcri.org/. The target end-user base is organizations interested in conducting customer understanding experiments or studies of online sites or systems. Other impacts would be research publications and/or IP Disclosures.
● S2P: Robust prototype at https://s2p.qcri.org/. The target end-user base is organizations that employ surveys (which is nearly all organizations) and wish to better understand their survey data. Other impacts would be research publications and/or IP Disclosures.
● Persona Analytics: This is a backend effort for any online persona system. The target end-user base is organizations that employ online personas. Other impacts would be research publications and/or IP Disclosures.