Using Q Analytics Software for Predictive Analytics, Data Mining, and Data Visualization
Predictive Analytics With Q Analytics Software
1. Predictive Analytics
Whether it’s forecasting future sales, detecting credit risks, optimizing marketing campaigns or saving lives, predictive analytics is all about using data to spot trends and predict the outcome. It combines descriptive statistical models of existing data with algorithms to determine relationships and structures that can be used to identify risks or opportunities.
For example, when a machine-learning algorithm is trained to recognize the vibrations of bees buzzing inside their hives, it can detect upcoming swarms with 91% accuracy. This kind of prediction can save businesses thousands if not millions in damage and repair costs.
It can also prevent costly mistakes and improve operational efficiency. For instance, when an algorithm can identify the early physiological signs of an allergic reaction to a bee sting far faster than a human can, it can send out an alert to a staff member to administer epinephrine to the patient. This can help reduce fatalities caused by missed or delayed treatment.
3. Data Mining
Q is software designed to help market researchers quickly find the story in their survey data. It automates much of the grunt work – cleaning and formatting data, statistical testing, generating tables and updating analyses and reports.
Q methodology combines the richness of qualitative data with the rigour of statistical analysis. It enables researchers to explore collective views on a topic while highlighting areas of consensus and disagreement in a structured and systematic way.
The key to the Q method is that participants rank order statements of opinion, values or beliefs on a grid, so they can be easily grouped and analysed. This enables the identification of common viewpoints in a sample that would otherwise be difficult to identify. This enables a deeper understanding of the nature and causes of differences in the population of individuals. The ability to understand individual differences is important for developing targeted interventions. It is also important for the design of products that are more responsive to customers’ needs.
4. Data Visualization
The analytics models developed by data analysts must be communicated in a form that business executives and other end users will understand. This often involves incorporating charts and other infographics that make the results easier to grasp.
Q is a new market research data analysis program from Australia-based Numbers International that’s designed to let researchers reveal hidden depths in their survey data without expecting them to become advanced statisticians. It borrows from SPSS Base statistics, but does so in a way that’s far more market research-savvy.
Q deals intelligently with every kind of question a researcher might encounter, from single-coded to multi-coded questions and even grids. It’s also not afraid to allow researchers (the primary audience for this software) to get eyeball-close to the case data, with all the case tables only a mouse click away on dedicated data tabs. Alternative statistics for each table can be displayed within the cell, to the right of the table or below the table.