智慧景区大数据体系（2）-景区大数据应用场景：采集到的数据能够做什么？

作者：低代码架构师Kaiwen

阅读数：159

更新时间：2024-02-23 20:34:50

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二、景区大数据应用场景：采集到的数据能够做什么？

1.预测景区流量

预测景区流量能够更好的保证景区安全，做好景区配套设施的准备。

下面介绍三种景区预测常用的算法：

①时间序列法，根据以往观测到的数据观测到的时间序列数据，发现其历史的趋势和模式，并依据这种历史的趋势和模型，通过曲线拟合和参数估计来建立数学模型的理论和方法。

优点：适用于绝大部分旅游景区，且考虑了景区客流量变化的历史趋势，预测过程相对简单，对中长期旅游客流量的预测效果较好。

缺点：对旅游客流量生成与影响因素的内在运作机理分析不够，没有考虑到现实存在或新产生的各种干扰因素；且对历史数据的准确性要求较高，偏差较大的数据对结果影响较大。

②回归分析法

采用计量经济学方法揭示经济活动中各个因素之间的定量关系，通过对景区客流量相关因素，如人均可支配收入、景区门票价格、景区知名度、交通通达度、景区所在城市吸引力系数（该城市游客人数/全国游客数量）、景区吸引力系数（景区游客人数/该城市游客人数）等相关数据的收集，建立多元线性回归模型，达到预测效果。

优点：将影响客流量的各种因素作为解释变量，以大数据作为支撑，预测精度较高，且克服了时间序列法的解释变量单一的缺陷。

缺点：前期数据收集耗时耗费大，影响因子较多，难以确定；所收集的数据多为二手数据，容易失真。

③大数据统计分析法

通过景区闸机门、售票系统、车流量、视屏监控、红外设备等获取相关数据，并采用一定的技术手段进行处理，最终统计出当前景区客流量，并用以预测未来的客流量。

优点：所获取的资料均为一手资料，数据真实性高。

缺点：数据收集周期长，过程繁琐；只适用于已建成的景区，无法对新建景区进行预测。

实际操作中，应考虑数据的可获得性、时效性、全面性，综合使用一种或多种预测方法，并通过设置调节因子，尽可能提高预测精度。

2.精细化用户运营

通过收集到的数据，搭建用户画像体系，精细化用户运营，构建景区的私域流量池。

这种方法只适用于景区本身有自己的数字化智慧景区软件，能够或者到各ota平台的用户个人信息，可以根据用户的画像特征，分别进行不同的运营策略，例如可以给已经结婚有小孩的游客推荐亲子票，提高转化率，给大学生提供大学生专用时段团购票、情侣票、宿舍四人游票等优惠，错开景区游玩高峰期，从而提高景区的客流量，提升景区的总营业额。

也可以通过手机号码短信推荐或者平台推荐信息的方法，进行用户的召回（让老用户或者已经流失的用户再次来景区游玩，或者通过游客景区新上了哪些新景点和娱乐设施激活用户）。