Before the 1990s, China's retail market has long maintained a single pattern department stores dominate the world. Since opening, as market competition and changes in consumer demand for retail, supermarkets, convenience stores, specialty stores and other new retail formats developed rapidly to become the main driving force of China's retail scale. However, due to the development of various retail formats vary, some rapid development of retail formats, and quickly became the new darling of the people, but some retail formats or are in a state of exploration, well not fully developed.
Chinese commercial enterprises still mainly rely on traditional management methods and management methods, lack of innovation. Lack of market segments, in terms of product mix, service standards, price, promotion and store layout, etc., using the lack of standardized management. Especially prominent is no tech support, mainly low degree of information technology. Management does not use modern electronic information technology. Foreign business enterprise management information system, electronic data processing systems, decision support systems as the core, formed a network to support the automated management, which is the traditional retail operation in very different ways.
In response to this phenomenon is no high-tech support and low level of information, this paper intends to use business intelligence to the retail industry professional, the scale of analysis, data-based, software as a means to provide the most cost data for the retail industry analysis and information technology solutions. So specifically how to do it?
This paper from a different point of view of data (such as sales data, inventory data, promotion data, etc.), the establishment of different methods of analysis, fundamental analysis and then thinking according to the retail industry, the establishment of the system of diversified analysis program.
First, the yard man is the retail industry's basic mode of thinking
Most of the problems in the retail industry can come from thinking people, goods, farm three dimensions. Whether it is the core of the three elements of online or offline, people, goods, games are retail operations. Give an example of people thinking this yard, shown below, which is a customer of the supermarket chain membership growth in turnover structure diagram, X-axis is the growth rate of membership, Y axis is the turnover of members, then from the people, angle cargo, field analysis, what are the reasons of the loss of influence of membership growth it?
1.1 Retail common analytical indicators
Commonly used in the retail industry analysis indicators but also from the perspective of people, goods, establishment of field starting mode of thinking. From the perspective of people, mainly employees and customers; from the perspective of goods, mainly from the production, procurement, supply chain, sales and after-sales aspects of these links mode; from the perspective of the field, there are shops and channels.
"People" section
1.2 Members of customer data management
For the retail industry, the customer is the source of profits, and membership is the core part of the customer, so the retail industry, is a member of analysis to help retailers fast insight into changes in customer demand and the changes in the market of doors and windows, in shopping economy growing prosperity of the moment, the importance of analysis of the more prominent members, but in the final analysis, how retailers should make members of the data analysis, leading to faster, more accurate picture of data information to guide the actual sales operations, increase sales? This section expanded from three aspects.
Based on the analysis of data 1.2.1 Member
Daily or weekly data to be tracked: the newly opened card number, a newly opened card rate, the contribution rate, membership customer price, unit price of membership, members of the joint rate. This part of the data to track based, supplemented by analysis, focusing on the analysis of what happened levels of business.
We need analysis of monthly and quarterly indicators: the average age of members, gender, contribution rate, the total number of active members, the loss of membership growth rate. This part of the data analysis mainly focuses on research trends, understand the overall business situation, identify the key issues.
In the data Index: including members of the newly opened card rate, turnover and retention rates and so on. End data analysis research-based, used to guide strategic planning for next year.
Based on the above analysis of ideas, were made members of the basic indicators analysis of year, month, daily newspaper, tracking and analysis from a combination of methods to track the chain of each region, store and analyze metrics can be specific to the day.
From the above three basic reporting analysis indicators can be seen in the basic analysis indicator members did not particularly large, almost three reports have used the sales, orders, valid membership number to track these indicators, the difference is more than daily daily membership to specific values and contrast presentation; monthly and multi-month trend to specific values presented; multi-annual presentation to the contrast between the stores. Specific analysis is as follows:
日报：以年月日和门店为筛选按钮，可以观察每个门店每一天的追踪指标，主要指标包括我们每天都需要盯住的会员销售额及占比情况、有效会员及占比情况、件单价、客单价这些点分析。从上图筛选出豫城时尚商场店7月份第二天的日报分析，从图中可以看出本店的日销额以及件单价和客单价，件单价比昨日增长了20%，客单价比昨日增长了22.16%。这一日消费最多的会员是YC4，他的连带率为5，可以看出YC4 会员的的购买单价比较大；连带率最高的会员是YC2，即一次性购买中买了11件产品，他的消费额处于中位，可以粗略的判断此会员当属中等产品消费的重要顾客。
月报：以月和门店为筛选按钮，可观察每个门店下每个月的销售额、有效会员和新增会员的占比或者环比这些追踪指标，以及加入了连带率和贡献率。从上图中可以看出，在2006年6月份的铜锣湾店基本销售的点指标以及环比和占比的分析，并能发现在这个月份TL5在本门店的消费最多，贡献最大；从趋势分析中观察到会员的贡献率在3月份的时候最高，5月份最低；会员的连带率在上半年的时候不稳定，起伏比较大，在后半年中，连带率呈现稳定上升的趋势，有可能的原因是员工的销售能力提高或者管理者实施了政策，迫于压力，这是一个好现象；从年龄的趋势分析中，本门店的年龄有趋于上涨的趋势，在本零售行业影响可能不大，但若对于服装行业来说，这是一个危险的信号，以为着服装的设计趋于向年长方向发展，是不太有利于品牌的发展的。
年报：以门店为筛选按钮，点指标跟日报和月报一样，以新洲店为例，X2会员的年消费最高，对于这样的会员顾客，是门店的经济支柱，一定要紧紧抓住这样的顾客，可以适当搞一些年奖励或者消费额达到一定标准打折等等；从增长流失结构来看，处在第四象限的门店是最好的，增长率高流失率低，铁西分店处于在这个位置，相反第二象限是流失比增长大的，员村店的增长率为10%，流失率为17%，对于员村店来说，会员是在一个下降的状况；从右边两个饼图可看豫城时尚商城店和万达店的订单数和有效会员数是比较大的，可达连锁超市的60%左右，这两个应该是该企业的重点门店，应重点关注。
1.2.2会员的价值指标分析
策略的制定提供数据支持。所以零售企业总是想尽一切办法去吸引更多的人成为会员，并且尽可能提高他们的忠诚度。忠诚度高的顾客表现为经常光顾，有较高的价格忍耐度，愿意支付更高的价格，也愿意向其他人推荐，对品牌满意度较高等。会员的忠诚度高不一定会员的价值就高，还得看他的消费能力。总结来看，有三个维度，六个指标，如下：
这六项指标，其中前三项是著名的顾客价值研究的RFM模型，分别是R-Recency（最近购买时间），F-Frequency（消费频率），M-Monetary（消费金额）。这三个指标来自于美国数据库营销机构的研究，现在逐渐成为会员价值研究以及会员营销的通用模型了。除此之外，我们还加入了价格容忍度指标，特价商品消费占比和高单价商品消费占比，特价商品占比值越低，价格容忍度越高，高单价商品消费占比越高，价格的容忍度越高，反之亦然。那么该如何量化这六项指标呢，一般采用建立标准打分制的方法，根据零售企业的行业特点，我们制定了这样一个参考标准：
根据这个标准，整理数据，最终雷达图的形式呈现。如下图会员价值分析。
会员价值分析以时间年月日进行筛选，再加入门店筛选，门店筛选以会员销售额占比分析汇总，联动到会员的消费额和价值得分排行榜，可观察对各个会员对门店贡献和忠诚度怎么样，更加深刻的了解我们的会员顾客；再者像查看每个会员价值的分布情况，可联动到会员雷达图，具体会员的价值评估就呈现了。由图中我们可观察，员村店的销售业绩是最差的，会员TI的消费额最高，可知是我们的重要顾客。
1.2.3会员的生命周期管理
从销售最大化的角度来讲，会员管理既要把会员技术做大，还要提高会员的购买频次，同事还需要防止顾客离你而去，所以顾客的生命周期管理就意义重大。如今我们可以通过数据去管理顾客的生命周期。
其中定义
活跃会员：在最近3个月内有过消费的会员。
沉默会员：最后一次消费发生在最近的4-6个月内，已经沉默了3个月。
睡眠会员：最后一次消费发生在最近的7-12个月内，已经睡眠了6个月。
流失会员：最近12个月均没有消费的会员。
上述定义仅基于一般的零售行业情况，针对具体的企业可制定不同的标准。
本文针对会员生命周期的定义结合零售行业的基本情况，制定的会员生命周期管理的报表。本报表以时间年月日和汇总值是活跃会员数的门店为筛选按钮，可观察每个不同的时期和门店下面会员各个生命周期以及活跃会员的重复购买次数的占比情况；还可观察到会员和活跃会员结构趋势分析。
从上图中可以看出，在2006年4月份，中心城店的活跃会员数最少，中心城店的要注重会员的后续管理；铜锣湾店活跃会员数也是比较多的，占总会员数的54%，活跃会员中，单次购买和重复一次购买的会员比重比较大，达73%左右；再从趋势上看，铜锣湾店的会员趋势，再4月份和7月份的会员总数比较大，7月份属最低，可针对具体的月份进行细节分析；再从4月份看，活跃会员占的比重比较大，1/2左右，沉默会员次之，流失会员和睡眠会员的人数最少，我们的目的是增大活跃会员、减少其他会员的比重。
1.2.4 销售额之会员的生命周期
On the basis of membership of the life cycle analysis of individual members of the life cycle of the structure and membership sales, compared to total sales of relations, specific reports as follows:
As can be seen from the figure, the largest member of the Center City store sales in the proportion of total sales, which is the center point of the city and the number of active members is repeated many times to buy than the major and member of the relevant conversion rate.
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