软件用户在软件体验不佳（例如持续存在问题、频繁崩溃或错误或不直观的用户界面）后切换到其他应用程序的可能性要高出四倍。为了满足用户不断变化的需求，软件开发人员可以利用预测分析。这是使用用户数据历史、统计建模和机器学习来预测或影响未来的决策。数字化转型网szhzxw.cn

最近的研究还表明，只有23%的公司使用预测分析来预测客户需求。当目标是获得可靠且可操作的信息时，软件开发人员可以遵循以下四个步骤。让我们深入了解：

1. 确定所有要求

为了在预测模型中获得更好的结果，软件工程师应识别当前的软件问题。例如，与不同设备的软件兼容性等问题。数字化转型网szhzxw.cn

软件开发人员应列出已识别的问题并对其进行排名，以确定每个检测到的问题的优先级。

此步骤还涉及定义数据收集的参数。例如，您可以决定收集有关软件使用时间、加载时间、用户兴趣、年龄和地区等信息的信息。数字化转型网szhzxw.cn

定义数据收集范围后，确定收集所需信息的可能解决方案。为您寻求实现的目标设定目标。最终目标是改进您的软件产品。

选择数据收集模式。有各种各样的软件和数据收集公司，如IBM SPSS Statistics，TIBCO spotfire和Bold BI。确定是要将软件与系统集成还是独立使用。数字化转型网szhzxw.cn

2. 检查符合目标的有用数据

确定需要改进的领域后，下一步是选择所需的用户数据类型。规划获取相关和高质量数据的方法，以帮助做出最佳决策。例如，通过在线调查收集用户对软件的反馈或评论。数字化转型网szhzxw.cn

可以从用户那里收集广泛的数据，例如生物数据、使用时间和设备数据等。必须将其缩小到与想要实现的目标相关的信息。筛选数据以实施可改善用户体验的软件更改。

选择一种以适当和可持续的方式收集可用数据以进行软件改进的方法。数据收集是一个持续的过程 – 计划如何存储信息以进行分析。数字化转型网szhzxw.cn

3. 创建并实现数据收集模型

预测分析使用不同的数据收集方法，例如时序分析、机器学习和回归算法。选择一个分析工具，该工具将从收集的信息中考虑所有变量，以提供更准确的可能结果。数字化转型网szhzxw.cn

质量保证在软件开发中很重要，因此请创建或采用数据收集模型，以帮助构建原始数据以获得最有益的结果。软件市场竞争激烈，软件开发人员需要及时为客户群提供解决方案。为了获得竞争优势，可以使用预测分析来收集有关软件功能、安全性和性能问题的信息。数字化转型网szhzxw.cn

选择易于整个团队使用的模型。解释它是如何工作的以及做出准确预测所需的数据应该很容易。该模型有助于评估用户体验的影响、识别可能的缺陷、显示问题的重复模式等等。

有效的模型检索数据，处理数据以删除任何不需要的信息，并对其进行转换以帮助决策。它还提供了软件工程师可用于更改软件的可操作建议。

4. 检查结果的准确性

检查数据分析模型的输出的准确性。它是否收集所需的数据并提供准确的预测？如果模型的建议是可操作的，精度分析将指导软件工程师。数字化转型网szhzxw.cn

使用已知数据和结果测试模型，以检查其评估和数据处理的准确性。确保任何恶意活动都不会破坏模型的准确性。该方法应识别任何意外数据并将其筛选掉，因此不会影响建议。数字化转型网szhzxw.cn

在软件安全方面，数据泄露等恶意活动会影响用户的信任。消除任何旨在误导软件开发人员改进内容的数据至关重要。人们可以预测可能的威胁并采取积极措施保护用户和公司免受潜在损害。

研究表明，数据泄露平均给企业造成 3 万美元的损失。此外，处理负面公关也是一个挑战。 使用预测分析将有助于识别可能的黑客威胁并提供解决方案，从而防止代价高昂的数据泄露。

结论

由于预测分析，决策变得容易。通过准确预测用户的需求，软件开发人员可以确定需要更改的领域，以改善消费者体验，收集相关数据，并实施有效的解决方案，以防止数据泄露等未来威胁。

英文原文：

How Predictive Analytics can Help Developers Anticipate Users Needs

Predictive analytics helps software developers make informed decisions on future software changes to ensure a seamless customer experience.数字化转型网szhzxw.cn

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:数字化转型网szhzxw.cn

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.数字化转型网szhzxw.cn

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.数字化转型网szhzxw.cn

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.数字化转型网szhzxw.cn

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.

本文由数字化转型网（www.szhzxw.cn）翻译而成，来源于informationweek.com；编辑/翻译：数字化转型网默然。

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