莫干山旅游线路游客偏好分析数据

莫干山旅游线路游客偏好分析数据可以帮助公司分析游客对莫干山旅游线路的偏好，优化旅游资源的配置，可以合理安排人力、物力资源 ，通过数据分析，了解客户对莫干山线路的偏好，实现精准广告投放和市场推广，提高营销效果和资源利用效率，根据低中高偏好分别精准定位目标客群，通过数据分析优化产品设计，避免同质化，例如传统景区升级、挖掘在地文化，淡旺季动态调整、配套设施升级，数据驱动的精准营销，例如渠道选择、内容策略、会员体系等，结合 0TA 平台和短视频营销。1.数据收集：收集企业近一年公司的内部后台数据，包括订单号、线路类别、线路名称、时间、平台、出行人数、游客偏好指数R等数据进行分析，计算出莫干山旅游线路出行人数的偏好指数。 2.数据预处理：对采集的数据进行整理，去除重复记录，将单位进行修改并统一。 3.数据计算：总线路出行人数=所有线路出行人数之和；游客偏好指数R=莫干山线路出行人数/总线路出行人数； 4.数据分级应用：根据公式计算对游客偏好指数R进行分级评价：当0%<R≤10%时，为低偏好； 当10%＜R≤25%时为中偏好； R＞25%时，为高偏好。 如：高偏好线路可提升营收与利润空间、优化资源倾斜效率、塑造品牌核心竞争力，中偏好线路可挖掘升级为高偏好线路的潜力、平衡产品结构风险、满足细分客户需求，低偏好线路可减少无效成本投入、决定线路淘汰或改造、反向指导市场调研方向等。

The tourist preference analysis data for Mogan Mountain tourist routes can help enterprises analyze tourists' preferences for Mogan Mountain tourist routes, optimize the allocation of tourism resources, and rationally arrange human and material resources. Through data analysis, enterprises can understand customers' preferences for Mogan Mountain routes, realize precise advertising placement and market promotion, improve marketing effectiveness and resource utilization efficiency, and accurately target customer groups based on low, medium and high preference levels. Optimize product design via data analysis to avoid homogenization, such as upgrading traditional scenic spots, excavating local culture, dynamically adjusting operations according to peak and off-peak tourist seasons, upgrading supporting facilities, and implementing data-driven precision marketing (including channel selection, content strategy, membership system, etc.) in combination with OTA platforms and short-video marketing.

1. Data Collection: Collect the enterprise's internal backend data from the past year, including order numbers, route categories, route names, travel dates, sales platforms, number of travelers, tourist preference index R and other related data for analysis, and calculate the preference index of the number of travelers on Mogan Mountain tourist routes.
2. Data Preprocessing: Organize the collected data, remove duplicate records, and modify and unify the measurement units.
3. Data Calculation: Total number of travelers across all routes = sum of the number of travelers on each individual route; Tourist preference index R = number of travelers on Mogan Mountain routes / total number of travelers across all routes.
4. Data Grading and Application: Grade and evaluate the tourist preference index R using the calculated formula: when 0% < R ≤ 10%, it is classified as low preference; when 10% < R ≤ 25%, it is classified as medium preference; when R > 25%, it is classified as high preference.
For example, high-preference routes can help increase revenue and profit margins, optimize resource tilting efficiency, and shape the core competitiveness of the brand; medium-preference routes have the potential to be upgraded to high-preference ones, balance product structure risks, and meet the needs of segmented customer groups; low-preference routes can help reduce invalid cost investments, guide decisions on route elimination or transformation, and reversely direct market research directions, etc.

搜集汇总

背景与挑战

背景概述

该数据集由湖州山河汇旅游有限公司提供，包含513条记录，用于分析莫干山旅游线路的游客偏好，通过计算偏好指数并分级评价，帮助优化资源配置和精准营销。数据来源于企业内部后台，覆盖2024年8月至2025年7月的统计周期，支持旅游产品改进和市场推广决策。