Sales analysis
同比：和去年同期
环比：和今年上（月/季/周）比
.
Larry Goldman此文相当有水准，提到难于从销售数据（库）得到的两种东西：产品相关性（product correlation),活动不存在的描述（nonexistense of activities)
Database Design is Difficult
Customer intelligence
Larry Goldman
DM Review Magazine, January 2008
I regularly hear the complaint, “I can’t get basic information” from the business side - particularly from sales and marketing individuals. Often, the phrase is distorted. Sales departments typically have access but haven’t spent the time to learn how to download the report or use the cube. Often, they have forgotten their training, don’t remember where or how to access the reports and have given up. Marketing departments can usually get the basic information, but “simple” or “basic” is often defined differently between the customer intelligence team and marketing.
Customer analysis is very difficult. Many relational databases and database designs are not able to handle some of the difficult concepts supported by customer analysis. Time series, product correlation and the nonexistence/existence of activities make analysis very difficult. Many times these requirements come across as ad hoc requirements that may be ignored in order to optimize for scheduled management reports.
To avoid being blindsided by these requirements and requests after implementation, those responsible for marketing databases should consider the following types of analysis:
Multiproduct or service relationship to the customer.
Identifying specific events that have occurred over time.
The nonexistence of events, transactions or behavior within the database.
Product Correlations
Though all sales and marketing databases are customer-centric at their core, the first question from a customer analyst is, “What did they buy?” This simple question is usually easy to address, as is the management report that shows sales, quantities and profitability broken down by product category or division. The more difficult questions include:
How many product categories do specific customers use or purchase?
Which product categories do specific customers use or purchase?
What combination of product categories do specific customers use or purchase?
How many customers use a specific combination of products?
What type of customers use specific combinations of products?
Many organizations focus on the breadth of their product lines. Typically, there is a huge opportunity to drive more usage across product lines rather than push single products to individual customers. This is true for telecommunications companies who want their customers to use landline, wireless and DSL or technology companies who want to sell computers, printers, modems and other accessories.
Time-series events and time series help us understand how fast customers adopt or respond to new marketing pitches. Common questions include:
How fast did the customer get to a certain revenue level?
How long did it take the customer to buy their second product?
How long has it been since the customer’s last purchase?
What is the average time between purchases?
After introducing a marketing program into the field, how long did it take customers to adopt?
Like product relationships, it is difficult for databases and query tools to compare dates in between records in an efficient manner. It can also be difficult to know which record to compare to in a high-transaction environment. Clickstream analysis poses this problem. Correlating browsing to buying is a very disconnected process. It is difficult for query tools to correlate massive amounts of clickstream data together without help from the database.
All dates need to be documented across transaction and customer records. Last purchase date, last login date and last clickthrough date should be precalculated instead of calculated on the fly. You may find yourself attaching dates to a high percentage of fields in your customer record. It is just as important to know when a customer purchased from a specific category as the last time they purchased from that specific product category.
Nonexistence of Activities
As already stated, it is hard enough to identify specific events in the database. It becomes even more difficult to identify what the customer has not done yet. For example:
Who has purchased product category A but not category B?
Who has not responded to any of the cross-sell campaigns?
Who has not logged into the Web site in a while?
These types of queries make it very difficult to discern inaction - which is just as important as actions that have happened.
Database designers must predict difficult queries from the beginning. Scenario and query prototyping should be standard operating procedure so you don’t find out during testing that your data model can’t support the queries. Hardware and software changes and modifications are not the answer. We have seen not-so-sophisticated marketing departments bringing large-scale IBM and Teradata systems to their knees with straightforward data models. To avoid the “I can’t get basic information” complaint, you must simulate these difficult queries and work with the business on how they will approach certain list selections or analysis. You can’t defend against creative marketers because they will always find the killer query. Just try to make sure everyone agrees on “killer” versus “basic
Granted, the target systems (DW, data marts and cubes) contain the data obtained from the source systems, so it does make sense that the content is similar. Similar content is not the problem; a similar physical design, however, is. Rather than applying the best practice design techniques they’ve learned to support DW and BI, people copy the underlying enterprise applications’ designs to the DW. This process propagates the limitations of the enterprise applications for reporting and analysis without taking advantage of DW best practices such as dimensional models or the hub-and-spoke architecture. History keeps repeating itself, resulting in frustrated businesspeople who aren’t getting the information they need. Meanwhile, IT is wondering why BI is not yet pervasive
Reporting and analytics require two data areas: hub and spoke. Most people understand the need for and benefits of building a data warehouse - the hub. You gather all your source data, cleanse it and make it consistent. You store your historical data once in the data warehouse and then distribute that data many times throughout your enterprise. “Create once and use many times” is the mantra you should follow in creating applications, data and services. It is efficient, and it is the most productive approach to support reporting and analysis. Most enterprises accept this as a best practice.
But it is equally important to create data marts or cubes from the data warehouse to enable reporting and analysis. These are the spokes. The benefit is that it is more efficient to create data marts oriented toward a business process or a group of businesspeople than to continually reinvent the wheel every time you create a report. Once again, I am talking about “create once and use many times.”
Integrating Web analytics with CRM system should come as no surprise as an important step to tying the entire lead generation and sales process together. The three main reasons for integrating Web analytics with CRM are:
Better marketing investment prioritization (both time and money ROI)
Measure marketing’s contribution to the sales pipeline, and
Enable sales intelligence for improved selling context.
Businesses and their marketing organizations that promote products or services with complex sales cycles often lack visibility beyond generating the initial sales leads from their Web sites, trade shows or other offline initiatives. For example, once Web leads are generated, they move into the black hole of the sales force automation (SFA)/CRM system with almost no ability to tie results such as closed deal quantities and sales values back to the marketing campaign costs, thus leaving fully measured ROI (or return on marketing) unanswered. To provide concrete answers to these ROI and other related questions, marketers should endeavor to integrate their Web analytics/campaign management solutions together with their SFA/CRM applications
Mikel Chertudi is the senior director of Demand and Online Marketing at Omniture. He oversees global strategy and execution of demand creation for new customer acquisition and cross selling strategies including the tactics of search (both paid and SEO), email, newsletters, display, content syndication, direct and dimensional mail. Chertudi is responsible for thought leadership-based marketing including best practice guides, Web seminars and reports. He and his team have deployed a comprehensive marketing technology-based solution for increasing response effectiveness by intertwining search marketing automation, email and direct mail lead nurturing automation, progressive telephony, on-site behavioral targeting, ad serving, A/B and multivariable testing, personalized prospect portals with Web analytics as the anchor technology to deploy a highly relevant prospect and customer experience.
For more information on related topics, visit the following channels:
Analytics
Customer Relationship Management (CRM)
Web Analytics
Retailers Using Analytics are Outperforming Rivals此文提到了几个大企业tesco, best buy, walmart等的实践
Dealing with Data
Greg Todd
Clive Humby Scoring Points : How Tesco is winning customer loyalty，此书有点意思，讲到了会员卡属于零和游戏. Club card之后，如何从昂贵，被新父母信任的boots那里抢婴儿市场：获取inner circle信任感，通过建立baby club card
Turning Customer Data into Profits
In summary, the consolidation is disappointing as much as it is exciting, and you may or may not benefit as a result
After a positive premiere, the marketing system started to lose steam. Within three months, some common themes started surfacing:
Standard metrics on customer usage reports were not matching between the data warehouse and other reporting systems.
Reports from the analytical data mart were not matching to the data warehouse.
Nobody seemed to be looking into the above issues.
Nobody seemed to know whom to call to have these issues investigated.
数据定义需要注明加载周期，以免用户误会For instance, customer count may be defined as the number of customers for a specific set of criteria. However, further information such as, "this field is only populated once a month" is also needed, or the user may assume it is loaded daily like the rest of the data warehouse. Many instances of this type of missing "use" information had users running erroneous queries
销售经理管理工作的十大忌语
该提问已过征答时限 悬赏点数 10 征答截止时间
外企面试之前让我先交一份该产品的销售分析报告，请问怎样写，通常包括哪些内容？谢谢
举报 60.19.201.*
独爱人山
评价较低的回答。 点击可以将其展开。
是什么产品啊？
这里有个彩电市场销售报告，
可以免费下载的。
你只要把产品名称，还有一些特征之类的稍作修改就可以了。
引用:
举报 222.128.6.*
四条棍99
评价较低的回答。 点击可以将其展开。
市场竞争状况和
销售数据的收集
无论是文字报告或者口头报告，最令人“无地自容”的就是被上级领导问时一问三不知，满口“也许”、“可能”、“应该”、“大概”和“似乎”。
一份标准的销售报告可能用到的数据有：
1.市场规模、市场容量以及增长率；
2.主要竞争产品（最好是分品项）的销量和增长率；
3.自己产品各品项的销售目标、实际销售量和增长率；
4.主要竞争品牌最近的新产品上市、促销及陈列动态（越细越好，至少把活动的通路、区域、产品和活动效果搞清楚）；
5.各经销商各产品的进货、销售和库存状况（别忘了把在途的产品也考虑进去）；
6.各经销商和直营客户的账款情况明细；
7.报告期区域内主要促销活动、陈列活动、铺货行动的执行状况、效果评估；
8.辖区内的营销预算及实际使用、节余状况。
真是好大一堆资料呀！难怪很多一线销售主管反映：不怕跑断腿、就怕做报告。
其实，以上这些数据收集起来并没有那么困难，关键是个习惯问题，因为很多资料根本就不用自己去整理。有些“老道”的销售主管会在销售会议前很早就把需要的资料、表格列一个单子，交给内勤或副手“打理”，甚至可以让他们进行一些初步的分析，他们可能比你更清
绩效与销售分析
Chapter 8
本章学习重点
建立资料清单.
使用表单功能修改或增删记录.
资料的排序与筛选.
Excel的小计功能.
使用条件加总精灵完成加总运算.
绘制枢纽分析表与枢纽分析图.
导读
本章将会利用 Excel 来分析与统计产品销售资料, 并建立销售业绩排行榜和销售统计图表, 以便公司的高级主管能够根据这些统计分析的结果, 了解各项产品的销售状况, 进而研拟适当的行销策略.
8-1 建立业绩资料清单
介绍的功能:
建立Excel清单资料.
在清单中新增订单资料.
认识自动延续清单的公式与格式功能.
使用的范例档案:Ch08-01
执行结果:
此处可加上冻结线, 让标题栏保持显示在画面上
在清单中新增两笔记录
8-2 利用表单功能增删记录
介绍的功能:
使用表单功能在清单中新增记录.
利用表单功能来编辑清单资料.
使用的范例档案:Ch08-01
执行结果:
使用表单功能所增加的一笔记录
8-3 制作业务员业绩排行榜
介绍的功能:
使用自动筛选功能找出2月份的订单记录.
计算业务员的销售总额
依业务员编号做排序.
依业务员编号做分组小计.
制作业绩排行榜
使用储存格参照.
自动填满数列.
使用的范例档案:Ch08-02,Ch08-03
执行结果:
观看成果档:h08-04
依照业务员的销售总额来排名次
8-4 使用条件式加总精灵
统计销售量
介绍的功能:
从增益集安装条件式加总精灵
执行条件式加总精灵来计算产品销售量
步骤一:指定加总范围
步骤二:决定要加总的栏位与设定加总条件
步骤三:选择计算结果项目
步骤四:指定计算结果的存放位置
使用的范例档案:Ch08-05
8-5 建立销售统计枢纽分析表
介绍的功能:
使用 枢纽分析表及图报表 功能建立一份「产品-地区」销售统计表
改变枢纽分析表栏位的资料显示方式
如何以滑鼠拉曳的方式, 新增与删除枢纽
分析表的栏位
使用的范例档案:Ch08-06
执行结果:
产品-地区销售统计枢纽分析表制作完成
8-6 绘制销售统计枢纽分析图
介绍的功能:
使用 枢纽分析表及图报表 功能建立一份「产品-地区」销售统计横条图.
美化枢纽分析图
加上图表标题.
加上资料表格.
改变标题的对齐方式.
使用的范例档案:Ch08-07
执行结果:
观看成果档:Ch08-08
产品-地区销售统计横条图制作完成
中国加盟网 > 环保 > 日常经营 > 环保行业：销前的销售分析环保行业：销前的销售分析品牌加盟网 2008年3月24日 谈到促销，多数企划人员首先想到的是搞什么活动来吸引人气。把活动放在**位，而不是把销售分析放在**位，笔者认为这是错误的做法，很可能会成为因促销而促销。
那么，该如何进行销售分析呢？
市场环境分析
假如企业所处的市场是地市级市场，那么市场一定会有一个参照市场。何谓参照市场？就是销售节奏稍微快于本区域的市场。简单地说，上一期参照市场的销售结构和商品价格就是下一期本市场的销售结构和价格。那么当你做销售分析时，参照市场的上期表现就是重要的分析参照依据。
这样的分析对于连锁企业来讲可能好办一些，因为销售记录是很保密的商业数据，任何一个企业不会轻易地透漏。所以地方上的单体零售企业就只能获得一些零碎的价格信息，通过这些信息预测市场的未来价格变化。
销售的历史数据分析
历史数据的分析一般只向上分析一个周期，比如今年“十一”的销售预测分析，只分析今年“五一”和去年“五一”、“十一”就可以了。历史数据分析的项目有：
1、每个品类的各个品牌的销售占比
2、每个品类中各个细分类的销售占比
3、每个品牌的细分类销售占比
4、每个品类的价位段的销售占比
5、每个品牌的价位段销售占比
通过分析找出每个品类的优势品牌、优势细分类和优势价位段、每个品牌的优势细分类和优势价位段，为找出重点促销商品做好基础。这些分析有助于操作者掌握消费者需求的结构和变化规律，不但对销售的静态结构有一个深入的认识，还要对销售的动态变化有一个方向上的把握。
那么，历史数据分析常采用哪些指标呢？在用这些指标分析的时候应该注意以下问题：
商品销售结构
向细分层面理解销售的**个维度就是商品销售结构。商品销售结构是指企业在销的各种商品在销售额中的比重，它需要计算单类商品在销售总额中的比例，然后将所有商品的销售占比汇总成一张表格，就形成了企业的商品销售结构。
计算公式为： 单品销售占比=单类商品销售额÷同期企业销售总额×100%
计算企业的商品销售结构首先要对企业经营的商品进行分类，对于综合类的公司，可以按商品大类划分，如国美，可以按商品类别划分为冰箱类商品、洗衣机类商品、电视机类商品、电脑类商品、手机类商品、厨卫类商品、数码商品等；对于某一类商品，还可以按商品的细分类划分，如电视机类商品，就可以按显像原理划分为显像管电视、液晶电视、等离子电视、背投电视等，还可以按屏幕大小划分为21寸、25寸、29寸、32寸、42寸等。根据不同维度计算出来的商品销售结构反映出来的问题不一样。
同比增长率
同比增长率是指某一方面（销售、利润等）实现的结果和去年同期对比的增长情况。
计算公式为：同比增长率=（今年数据-去年同期数据）÷去年同期数据×100%
很多商品的销售都有季节性，或者说具有周期性，今年5月和去年5月影响销售因素的作用力的结构和强度大致相当，这是使用同比增长率指标的前提假设条件。
但是，事实上这种假设条件只是在一定范围内成立，今年和去年总是有某些方面存在不一致，所以在使用同比增长率指标分析销售时，一定要附加定性的文字分析，辅助说明具体情况。
例如，2006年春节是在2006年1月29日，2007年春节是在2007年2月18日，这样，2007年1月的销售肯定与2006年1月的销售差很多，而2007年2月的销售又比2006年2月好很多，所以不能只看到2007年1月比2006年1月差很多就做出销售很差的判断。这个例子很明显，还有一些情况比较隐蔽，不太容易判断。比如，2006年8月某家电商场的厨卫商品销售比2005年8月下滑一大节，怎么分析也找不出原因，最后找到原店长和柜长才弄明白，原来2005年8月这个门店周边有几个小区交付使用，业主都到这家家电商场购买厨卫商品装修新房，使得销售冲得较高。所以在使用同比增长率指标分析销售时，一定要仔细对比前后的具体情况，然后再做判断。
用同比增长率指标分析销售的缺陷就是时间上相隔较远。
环比增长率
环比增长率是指某一方面（销售、利润等）实现的结果和上一期对比的增长情况。
计算公式为： 环比增长率=（本期数据-上期数据）÷上期数据×100%
因为时间，前后相隔不远，影响销售的因素变化不大，使得销售前后期有一定的可比性，这是使用环比增长率指标分析销售的前提假设条件。环比增长率指标克服了同比增长率指标时间相隔太远和一些隐性的变化无法辨明的缺陷。但是它也有不足：不适合周期性强的商品的销售分析。
另外，节假日销售明显的商品也不适合环比增长率指标的分析。如北京市的家电商品大都集中在周六日促销，使得周末两天的销售在一周销售中占比较重。如果一个月周末数较上个月少，环比增长率指标就会受到影响。
因此，不管是同比增长率和环比增长率指标，都有一定的缺陷，仅用指标本身的分析不能反映问题的全貌，都必须结合实际情况才能深入理解销售的变化。
同期环比增长率
同期环比增长率是指去年同期某一方面（销售、利润等）实现的结果和去年上一期对比的增长情况。
计算公式为： 同期环比增长率=（去年同期数据-去年上期数据）÷去年上期数据×100%
同期环比增长率实际上是评价环比增长率的参照数据。
【例】某企业2006年8月销售额是60万元，9月销售额是80万元，而2005年8月销售额是50万元，9月销售额是70万元，计算2006年9月的同比增长率、环比增长率和同期环比增长率，以及2006年8月的同比增长率。
2006年9月同比增长率、环比增长率和同期环比增长率分别为：同比增长率=（80-70）÷70×100%=14%；环比增长率=（80-60）÷60×100%=33%；同期环比增长率=（70-50）÷50×100%=40%。2006年8月的同比增长率：同比增长率=（60-50）÷50×100%=20%
显然，在3个指标中，环比增长率和同期环比增长率更具可比性，2006年8月的同比增长率和2006年9月的同比增长率具有可比性。上例中，2006年9月的同比增长率看似很高，但是和2006年8月的同比增长率相比不是很理想；2006年9月的环比增长率为33％，和同期环比增长率40％相比，还是有差距。所以，对销售的评价应该是多方位的。
环比：和今年上（月/季/周）比
.
Larry Goldman此文相当有水准，提到难于从销售数据（库）得到的两种东西：产品相关性（product correlation),活动不存在的描述（nonexistense of activities)
Database Design is Difficult
Customer intelligence
Larry Goldman
DM Review Magazine, January 2008
I regularly hear the complaint, “I can’t get basic information” from the business side - particularly from sales and marketing individuals. Often, the phrase is distorted. Sales departments typically have access but haven’t spent the time to learn how to download the report or use the cube. Often, they have forgotten their training, don’t remember where or how to access the reports and have given up. Marketing departments can usually get the basic information, but “simple” or “basic” is often defined differently between the customer intelligence team and marketing.
Customer analysis is very difficult. Many relational databases and database designs are not able to handle some of the difficult concepts supported by customer analysis. Time series, product correlation and the nonexistence/existence of activities make analysis very difficult. Many times these requirements come across as ad hoc requirements that may be ignored in order to optimize for scheduled management reports.
To avoid being blindsided by these requirements and requests after implementation, those responsible for marketing databases should consider the following types of analysis:
Multiproduct or service relationship to the customer.
Identifying specific events that have occurred over time.
The nonexistence of events, transactions or behavior within the database.
Product Correlations
Though all sales and marketing databases are customer-centric at their core, the first question from a customer analyst is, “What did they buy?” This simple question is usually easy to address, as is the management report that shows sales, quantities and profitability broken down by product category or division. The more difficult questions include:
How many product categories do specific customers use or purchase?
Which product categories do specific customers use or purchase?
What combination of product categories do specific customers use or purchase?
How many customers use a specific combination of products?
What type of customers use specific combinations of products?
Many organizations focus on the breadth of their product lines. Typically, there is a huge opportunity to drive more usage across product lines rather than push single products to individual customers. This is true for telecommunications companies who want their customers to use landline, wireless and DSL or technology companies who want to sell computers, printers, modems and other accessories.
Time-series events and time series help us understand how fast customers adopt or respond to new marketing pitches. Common questions include:
How fast did the customer get to a certain revenue level?
How long did it take the customer to buy their second product?
How long has it been since the customer’s last purchase?
What is the average time between purchases?
After introducing a marketing program into the field, how long did it take customers to adopt?
Like product relationships, it is difficult for databases and query tools to compare dates in between records in an efficient manner. It can also be difficult to know which record to compare to in a high-transaction environment. Clickstream analysis poses this problem. Correlating browsing to buying is a very disconnected process. It is difficult for query tools to correlate massive amounts of clickstream data together without help from the database.
All dates need to be documented across transaction and customer records. Last purchase date, last login date and last clickthrough date should be precalculated instead of calculated on the fly. You may find yourself attaching dates to a high percentage of fields in your customer record. It is just as important to know when a customer purchased from a specific category as the last time they purchased from that specific product category.
Nonexistence of Activities
As already stated, it is hard enough to identify specific events in the database. It becomes even more difficult to identify what the customer has not done yet. For example:
Who has purchased product category A but not category B?
Who has not responded to any of the cross-sell campaigns?
Who has not logged into the Web site in a while?
These types of queries make it very difficult to discern inaction - which is just as important as actions that have happened.
Database designers must predict difficult queries from the beginning. Scenario and query prototyping should be standard operating procedure so you don’t find out during testing that your data model can’t support the queries. Hardware and software changes and modifications are not the answer. We have seen not-so-sophisticated marketing departments bringing large-scale IBM and Teradata systems to their knees with straightforward data models. To avoid the “I can’t get basic information” complaint, you must simulate these difficult queries and work with the business on how they will approach certain list selections or analysis. You can’t defend against creative marketers because they will always find the killer query. Just try to make sure everyone agrees on “killer” versus “basic
Granted, the target systems (DW, data marts and cubes) contain the data obtained from the source systems, so it does make sense that the content is similar. Similar content is not the problem; a similar physical design, however, is. Rather than applying the best practice design techniques they’ve learned to support DW and BI, people copy the underlying enterprise applications’ designs to the DW. This process propagates the limitations of the enterprise applications for reporting and analysis without taking advantage of DW best practices such as dimensional models or the hub-and-spoke architecture. History keeps repeating itself, resulting in frustrated businesspeople who aren’t getting the information they need. Meanwhile, IT is wondering why BI is not yet pervasive
Reporting and analytics require two data areas: hub and spoke. Most people understand the need for and benefits of building a data warehouse - the hub. You gather all your source data, cleanse it and make it consistent. You store your historical data once in the data warehouse and then distribute that data many times throughout your enterprise. “Create once and use many times” is the mantra you should follow in creating applications, data and services. It is efficient, and it is the most productive approach to support reporting and analysis. Most enterprises accept this as a best practice.
But it is equally important to create data marts or cubes from the data warehouse to enable reporting and analysis. These are the spokes. The benefit is that it is more efficient to create data marts oriented toward a business process or a group of businesspeople than to continually reinvent the wheel every time you create a report. Once again, I am talking about “create once and use many times.”
Integrating Web analytics with CRM system should come as no surprise as an important step to tying the entire lead generation and sales process together. The three main reasons for integrating Web analytics with CRM are:
Better marketing investment prioritization (both time and money ROI)
Measure marketing’s contribution to the sales pipeline, and
Enable sales intelligence for improved selling context.
Businesses and their marketing organizations that promote products or services with complex sales cycles often lack visibility beyond generating the initial sales leads from their Web sites, trade shows or other offline initiatives. For example, once Web leads are generated, they move into the black hole of the sales force automation (SFA)/CRM system with almost no ability to tie results such as closed deal quantities and sales values back to the marketing campaign costs, thus leaving fully measured ROI (or return on marketing) unanswered. To provide concrete answers to these ROI and other related questions, marketers should endeavor to integrate their Web analytics/campaign management solutions together with their SFA/CRM applications
Mikel Chertudi is the senior director of Demand and Online Marketing at Omniture. He oversees global strategy and execution of demand creation for new customer acquisition and cross selling strategies including the tactics of search (both paid and SEO), email, newsletters, display, content syndication, direct and dimensional mail. Chertudi is responsible for thought leadership-based marketing including best practice guides, Web seminars and reports. He and his team have deployed a comprehensive marketing technology-based solution for increasing response effectiveness by intertwining search marketing automation, email and direct mail lead nurturing automation, progressive telephony, on-site behavioral targeting, ad serving, A/B and multivariable testing, personalized prospect portals with Web analytics as the anchor technology to deploy a highly relevant prospect and customer experience.
For more information on related topics, visit the following channels:
Analytics
Customer Relationship Management (CRM)
Web Analytics
Retailers Using Analytics are Outperforming Rivals此文提到了几个大企业tesco, best buy, walmart等的实践
Dealing with Data
Greg Todd
Clive Humby Scoring Points : How Tesco is winning customer loyalty，此书有点意思，讲到了会员卡属于零和游戏. Club card之后，如何从昂贵，被新父母信任的boots那里抢婴儿市场：获取inner circle信任感，通过建立baby club card
Turning Customer Data into Profits
In summary, the consolidation is disappointing as much as it is exciting, and you may or may not benefit as a result
After a positive premiere, the marketing system started to lose steam. Within three months, some common themes started surfacing:
Standard metrics on customer usage reports were not matching between the data warehouse and other reporting systems.
Reports from the analytical data mart were not matching to the data warehouse.
Nobody seemed to be looking into the above issues.
Nobody seemed to know whom to call to have these issues investigated.
数据定义需要注明加载周期，以免用户误会For instance, customer count may be defined as the number of customers for a specific set of criteria. However, further information such as, "this field is only populated once a month" is also needed, or the user may assume it is loaded daily like the rest of the data warehouse. Many instances of this type of missing "use" information had users running erroneous queries
销售经理管理工作的十大忌语
该提问已过征答时限 悬赏点数 10 征答截止时间
外企面试之前让我先交一份该产品的销售分析报告，请问怎样写，通常包括哪些内容？谢谢
举报 60.19.201.*
独爱人山
评价较低的回答。 点击可以将其展开。
是什么产品啊？
这里有个彩电市场销售报告，
可以免费下载的。
你只要把产品名称，还有一些特征之类的稍作修改就可以了。
引用:
举报 222.128.6.*
四条棍99
评价较低的回答。 点击可以将其展开。
市场竞争状况和
销售数据的收集
无论是文字报告或者口头报告，最令人“无地自容”的就是被上级领导问时一问三不知，满口“也许”、“可能”、“应该”、“大概”和“似乎”。
一份标准的销售报告可能用到的数据有：
1.市场规模、市场容量以及增长率；
2.主要竞争产品（最好是分品项）的销量和增长率；
3.自己产品各品项的销售目标、实际销售量和增长率；
4.主要竞争品牌最近的新产品上市、促销及陈列动态（越细越好，至少把活动的通路、区域、产品和活动效果搞清楚）；
5.各经销商各产品的进货、销售和库存状况（别忘了把在途的产品也考虑进去）；
6.各经销商和直营客户的账款情况明细；
7.报告期区域内主要促销活动、陈列活动、铺货行动的执行状况、效果评估；
8.辖区内的营销预算及实际使用、节余状况。
真是好大一堆资料呀！难怪很多一线销售主管反映：不怕跑断腿、就怕做报告。
其实，以上这些数据收集起来并没有那么困难，关键是个习惯问题，因为很多资料根本就不用自己去整理。有些“老道”的销售主管会在销售会议前很早就把需要的资料、表格列一个单子，交给内勤或副手“打理”，甚至可以让他们进行一些初步的分析，他们可能比你更清
绩效与销售分析
Chapter 8
本章学习重点
建立资料清单.
使用表单功能修改或增删记录.
资料的排序与筛选.
Excel的小计功能.
使用条件加总精灵完成加总运算.
绘制枢纽分析表与枢纽分析图.
导读
本章将会利用 Excel 来分析与统计产品销售资料, 并建立销售业绩排行榜和销售统计图表, 以便公司的高级主管能够根据这些统计分析的结果, 了解各项产品的销售状况, 进而研拟适当的行销策略.
8-1 建立业绩资料清单
介绍的功能:
建立Excel清单资料.
在清单中新增订单资料.
认识自动延续清单的公式与格式功能.
使用的范例档案:Ch08-01
执行结果:
此处可加上冻结线, 让标题栏保持显示在画面上
在清单中新增两笔记录
8-2 利用表单功能增删记录
介绍的功能:
使用表单功能在清单中新增记录.
利用表单功能来编辑清单资料.
使用的范例档案:Ch08-01
执行结果:
使用表单功能所增加的一笔记录
8-3 制作业务员业绩排行榜
介绍的功能:
使用自动筛选功能找出2月份的订单记录.
计算业务员的销售总额
依业务员编号做排序.
依业务员编号做分组小计.
制作业绩排行榜
使用储存格参照.
自动填满数列.
使用的范例档案:Ch08-02,Ch08-03
执行结果:
观看成果档:h08-04
依照业务员的销售总额来排名次
8-4 使用条件式加总精灵
统计销售量
介绍的功能:
从增益集安装条件式加总精灵
执行条件式加总精灵来计算产品销售量
步骤一:指定加总范围
步骤二:决定要加总的栏位与设定加总条件
步骤三:选择计算结果项目
步骤四:指定计算结果的存放位置
使用的范例档案:Ch08-05
8-5 建立销售统计枢纽分析表
介绍的功能:
使用 枢纽分析表及图报表 功能建立一份「产品-地区」销售统计表
改变枢纽分析表栏位的资料显示方式
如何以滑鼠拉曳的方式, 新增与删除枢纽
分析表的栏位
使用的范例档案:Ch08-06
执行结果:
产品-地区销售统计枢纽分析表制作完成
8-6 绘制销售统计枢纽分析图
介绍的功能:
使用 枢纽分析表及图报表 功能建立一份「产品-地区」销售统计横条图.
美化枢纽分析图
加上图表标题.
加上资料表格.
改变标题的对齐方式.
使用的范例档案:Ch08-07
执行结果:
观看成果档:Ch08-08
产品-地区销售统计横条图制作完成
中国加盟网 > 环保 > 日常经营 > 环保行业：销前的销售分析环保行业：销前的销售分析品牌加盟网 2008年3月24日 谈到促销，多数企划人员首先想到的是搞什么活动来吸引人气。把活动放在**位，而不是把销售分析放在**位，笔者认为这是错误的做法，很可能会成为因促销而促销。
那么，该如何进行销售分析呢？
市场环境分析
假如企业所处的市场是地市级市场，那么市场一定会有一个参照市场。何谓参照市场？就是销售节奏稍微快于本区域的市场。简单地说，上一期参照市场的销售结构和商品价格就是下一期本市场的销售结构和价格。那么当你做销售分析时，参照市场的上期表现就是重要的分析参照依据。
这样的分析对于连锁企业来讲可能好办一些，因为销售记录是很保密的商业数据，任何一个企业不会轻易地透漏。所以地方上的单体零售企业就只能获得一些零碎的价格信息，通过这些信息预测市场的未来价格变化。
销售的历史数据分析
历史数据的分析一般只向上分析一个周期，比如今年“十一”的销售预测分析，只分析今年“五一”和去年“五一”、“十一”就可以了。历史数据分析的项目有：
1、每个品类的各个品牌的销售占比
2、每个品类中各个细分类的销售占比
3、每个品牌的细分类销售占比
4、每个品类的价位段的销售占比
5、每个品牌的价位段销售占比
通过分析找出每个品类的优势品牌、优势细分类和优势价位段、每个品牌的优势细分类和优势价位段，为找出重点促销商品做好基础。这些分析有助于操作者掌握消费者需求的结构和变化规律，不但对销售的静态结构有一个深入的认识，还要对销售的动态变化有一个方向上的把握。
那么，历史数据分析常采用哪些指标呢？在用这些指标分析的时候应该注意以下问题：
商品销售结构
向细分层面理解销售的**个维度就是商品销售结构。商品销售结构是指企业在销的各种商品在销售额中的比重，它需要计算单类商品在销售总额中的比例，然后将所有商品的销售占比汇总成一张表格，就形成了企业的商品销售结构。
计算公式为： 单品销售占比=单类商品销售额÷同期企业销售总额×100%
计算企业的商品销售结构首先要对企业经营的商品进行分类，对于综合类的公司，可以按商品大类划分，如国美，可以按商品类别划分为冰箱类商品、洗衣机类商品、电视机类商品、电脑类商品、手机类商品、厨卫类商品、数码商品等；对于某一类商品，还可以按商品的细分类划分，如电视机类商品，就可以按显像原理划分为显像管电视、液晶电视、等离子电视、背投电视等，还可以按屏幕大小划分为21寸、25寸、29寸、32寸、42寸等。根据不同维度计算出来的商品销售结构反映出来的问题不一样。
同比增长率
同比增长率是指某一方面（销售、利润等）实现的结果和去年同期对比的增长情况。
计算公式为：同比增长率=（今年数据-去年同期数据）÷去年同期数据×100%
很多商品的销售都有季节性，或者说具有周期性，今年5月和去年5月影响销售因素的作用力的结构和强度大致相当，这是使用同比增长率指标的前提假设条件。
但是，事实上这种假设条件只是在一定范围内成立，今年和去年总是有某些方面存在不一致，所以在使用同比增长率指标分析销售时，一定要附加定性的文字分析，辅助说明具体情况。
例如，2006年春节是在2006年1月29日，2007年春节是在2007年2月18日，这样，2007年1月的销售肯定与2006年1月的销售差很多，而2007年2月的销售又比2006年2月好很多，所以不能只看到2007年1月比2006年1月差很多就做出销售很差的判断。这个例子很明显，还有一些情况比较隐蔽，不太容易判断。比如，2006年8月某家电商场的厨卫商品销售比2005年8月下滑一大节，怎么分析也找不出原因，最后找到原店长和柜长才弄明白，原来2005年8月这个门店周边有几个小区交付使用，业主都到这家家电商场购买厨卫商品装修新房，使得销售冲得较高。所以在使用同比增长率指标分析销售时，一定要仔细对比前后的具体情况，然后再做判断。
用同比增长率指标分析销售的缺陷就是时间上相隔较远。
环比增长率
环比增长率是指某一方面（销售、利润等）实现的结果和上一期对比的增长情况。
计算公式为： 环比增长率=（本期数据-上期数据）÷上期数据×100%
因为时间，前后相隔不远，影响销售的因素变化不大，使得销售前后期有一定的可比性，这是使用环比增长率指标分析销售的前提假设条件。环比增长率指标克服了同比增长率指标时间相隔太远和一些隐性的变化无法辨明的缺陷。但是它也有不足：不适合周期性强的商品的销售分析。
另外，节假日销售明显的商品也不适合环比增长率指标的分析。如北京市的家电商品大都集中在周六日促销，使得周末两天的销售在一周销售中占比较重。如果一个月周末数较上个月少，环比增长率指标就会受到影响。
因此，不管是同比增长率和环比增长率指标，都有一定的缺陷，仅用指标本身的分析不能反映问题的全貌，都必须结合实际情况才能深入理解销售的变化。
同期环比增长率
同期环比增长率是指去年同期某一方面（销售、利润等）实现的结果和去年上一期对比的增长情况。
计算公式为： 同期环比增长率=（去年同期数据-去年上期数据）÷去年上期数据×100%
同期环比增长率实际上是评价环比增长率的参照数据。
【例】某企业2006年8月销售额是60万元，9月销售额是80万元，而2005年8月销售额是50万元，9月销售额是70万元，计算2006年9月的同比增长率、环比增长率和同期环比增长率，以及2006年8月的同比增长率。
2006年9月同比增长率、环比增长率和同期环比增长率分别为：同比增长率=（80-70）÷70×100%=14%；环比增长率=（80-60）÷60×100%=33%；同期环比增长率=（70-50）÷50×100%=40%。2006年8月的同比增长率：同比增长率=（60-50）÷50×100%=20%
显然，在3个指标中，环比增长率和同期环比增长率更具可比性，2006年8月的同比增长率和2006年9月的同比增长率具有可比性。上例中，2006年9月的同比增长率看似很高，但是和2006年8月的同比增长率相比不是很理想；2006年9月的环比增长率为33％，和同期环比增长率40％相比，还是有差距。所以，对销售的评价应该是多方位的。
posted by hunter at 4:01 AM
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