首页推荐-直播访客对支付金额的影响分析数据

首页推荐-直播对支付金额的影响分析数据涵盖万相台推广访客及支付金额两个关键数据，万相台作为天猫店铺的关键指标，能具体表现出淘系店铺对智能场景访客的能力，万相台推广访客的降幅与支付金额的降幅对比结果，能够为品牌站内推广提供数据支撑，为其他同类或相关行业经营者在做预算分配上，提供数据参考，为业务增长提供数据支持。1.数据采集：数据采集公司天猫渠道系统数据2023年1-11月、2024年1-11月生意参谋数据，含渠道、来源、时间、流量来源、首页推荐-直播访客、支付金额，并对敏感信息加密处理，对数据进行加工
2.算法规则：访客成交弹性系数Ed=(△Q/Q)÷(△P/P），得到访客成交弹性系数，Q代表24年1-11月首页推荐-直播总访客，P代表24年1-11月总支付金额，△Q代表总访客同比变动值，△P表示总支付金额同比变动值，取访客成交弹性系数的绝对值|Ed|作为分析数据时的参考系数
3.数据分析：根据|Ed|的数值可分析首页推荐-直播访客和支付金额的弹性系数。（1）|Ed|=1（推广弹性适中），说明首页推荐-直播访客与支付金额变动幅度相同；（2）1<|Ed|（推广富有弹性），说明首页推荐-直播访客变动幅度大于支付金额变动幅度；（3）|Ed|<1（推广缺乏弹性），说明搜索访客变动幅度小于支付金额变动变动幅度。

The data for the impact analysis of homepage recommendation-live streaming on transaction amount covers two core metrics: Wanxiangtai promoted visitors and transaction amount. As a key tool for Tmall stores, Wanxiangtai specifically reflects the ability of Taobao ecosystem stores to acquire intelligent scenario-based visitors. The comparison between the decline rate of Wanxiangtai promoted visitors and that of transaction amount can provide data support for in-store brand promotion, data reference for budget allocation decisions of operators in similar or related industries, and data backing for business growth.

1. Data Collection: The data is sourced from Tmall channel system and Tmall Shopkeeper data from January to November 2023 and January to November 2024, including channel, source, time, traffic source, homepage recommendation-live streaming visitors, and transaction amount. Sensitive information is encrypted, and data preprocessing is conducted.

2. Algorithm Rules: The visitor-transaction elasticity coefficient $E_d$ is calculated as $E_d = frac{Delta Q / Q}{Delta P / P}$, where Q represents the total number of homepage recommendation-live streaming visitors from January to November 2024, P represents the total transaction amount over the same period, $Delta Q$ represents the year-over-year change in total visitors, and $Delta P$ represents the year-over-year change in total transaction amount. The absolute value of the elasticity coefficient $|E_d|$ is taken as the reference coefficient for data analysis.

3. Data Analysis: The elasticity coefficients between homepage recommendation-live streaming visitors and transaction amount can be analyzed based on the value of $|E_d|$:
(1) $|E_d|=1$ (moderate promotion elasticity): indicating that the change ranges of homepage recommendation-live streaming visitors and transaction amount are identical;
(2) $1<|E_d|$ (highly elastic promotion): indicating that the change range of homepage recommendation-live streaming visitors is greater than that of transaction amount;
(3) $|E_d|<1$ (low elasticity promotion): indicating that the change range of search visitors is smaller than that of transaction amount.