PredictSheets：利用机器学习进行业务预测分析
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
适用行业
- 水泥
- 设备与机械
用例
- 楼宇自动化与控制
- 预测性维护
服务
- 云规划/设计/实施服务
- 数据科学服务
关于客户
PredictSheets 的主要客户是不熟悉机器学习但需要分析复杂数据集并预测结果以增加业务价值的企业主。这些企业面临着减少客户流失、防止员工流失和提高销售获胜率等挑战。 PredictSheets 的客户涵盖各个行业和部门，因为该应用程序范围广泛，可用于所有预测分析案例。这些客户也是早期采用者，他们重视一对一的支持，并热衷于利用预测分析来推动业务增长。
挑战
PredictSheets 的创始人 Adam Roach 发现了市场中的一个空白，即不熟悉机器学习的企业主很难分析复杂的数据集并预测结果。这一挑战限制了企业增加价值的潜力。企业面临的常见问题包括减少客户流失、防止员工流失以及提高销售获胜率。此外，Adam 此前曾经历过雇用开发人员构建移动应用程序所需的高成本、耗时和压力。他正在寻找一种更高效、更具成本效益的解决方案，以自助服务的方式提供他的专业知识。
解决方案
Adam 开发了 PredictSheets，这是一款基于 Bubble（无代码平台）构建的应用程序。该应用程序允许用户将数据集（例如 Excel 文件）上传到平台。然后系统自动构建模型来生成预测。一旦建立了预测模型，用户就可以下载文件并对新数据进行“评分”。例如，如果用户有新的销售线索，他们可以使用该模型来预测每个线索的获胜概率。这使企业能够优先考虑外展并提高获胜率。为了帮助可能在格式化初始数据集时遇到问题的用户，Adam 向所有订阅者提供无限高级支持，引导他们逐步完成整个过程，以确保他们从数据中获得可操作的见解。
运营影响
数量效益
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