Software users are four times more likely to switch to other applications after a poor experience with the software, such as persistent issues, frequent crashes or errors, or an unintuitive user interface. To meet the ever-changing needs of users, software developers can leverage predictive analytics. This is the use of user data history, statistical modeling, and machine learning to predict or influence future decision-making.

Recent studies also show that only 23% of companies use predictive analytics to predict customer demands. When the goal is getting reliable and actionable information, a software developer can be guided by the following four steps. Let's dive in:

1. Determine all the requirements

For better results in a predictive model, a software engineer should identify current software issues. For instance, problems such as software compatibility with different devices.

Software developers should list the identified problems and rank them to determine the priority of each detected issue.

This step also involves defining the parameters of data collection. For instance, you can decide to gather information on software usage time, loading time, user interest, age, and region, among other information.

After defining the scope of data collection, identify possible solutions for collecting the information you need. Set a goal for what you seek to achieve. The ultimate objective is to improve your software product.

Select a mode of data collection. There is a wide range of software and data collection companies, such as IBM SPSS Statistics, TIBCO spotfire, and Bold BI. Determine whether you want to integrate the software with your system or use it independently.

2. Examine useful data that meets the goal

After identifying areas for improvement, the next step is selecting the type of user data needed. Plan the methods for getting relevant and quality data to help make the best decision. For instance, gather user feedback or comments about the software through online surveys.

There is a wide range of data that can be collected from users such as bio data, usage time, and device data among others. It is essential to narrow it down to the information that relates to what one wants to achieve. Sieve through the data to implement a software change that improves user experience.

Select a method that collects available data for software improvement suitably and sustainably. Data collection is a continuous process -- plan how the information will be stored for analysis.

3. Create and implement a data collection model

Predictive analytics uses different data collection methods, such as time series analysis, machine learning, and regression algorithms. Select an analysis tool that will factor all variables from the information gathered to provide a more accurate probable outcome.

Quality assurance is important in software development, so create or adopt a data collection model that helps structure raw data for the most beneficial outcome. The software market is competitive, and a software developer needs to deliver a timely solution to the customer base. For a competitive advantage, one can use predictive analytics to gather information on the software's functional, security, and performance issues.

Select a model that is easy to use for the whole team. It should be easy to explain how it works and the data needed to make an accurate prediction. The model aids in areas such as evaluating the effects of user experience, identifying possible defects, showing repeated patterns of issues, and much more.

An effective model retrieves data, processes it to remove any unwanted information, and transforms it to help in decision-making. It also offers actionable recommendations a software engineer can use to make changes to the software.

4. Check results accuracy

Check the output of the data analysis model for accuracy. Is it collecting the needed data and offering accurate predictions? The precision analysis will guide a software engineer if the model’s recommendations are actionable.

Test the model using known data and outcomes to check its evaluation and data processing accuracy. Ensure that any malicious activity does not subvert the accuracy of the model. The approach should identify any unexpected data and filter it out, so it doesn’t affect the recommendation.

In the case of software security, malicious activities such as data leaks affect users’ trust. Eliminating any data meant to misguide a software developer on what to improve is vital. One can predict possible threats and take proactive measures to protect the users and the company from potential damage.

Studies show that data breaches cost businesses, on average, $3.9 million. In addition, it is a challenge to handle negative PR. Using predictive analytics will help identify possible hacking threats and offer solutions, which prevent costly data leakage.

Conclusion

Decision-making has been made easy thanks to predictive analysis. By accurately predicting users’ needs, software developers can determine areas requiring changes to improve the consumer experience, gather relevant data, and implement effective solutions to prevent future threats such as data leaks.