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三四线城市地产开发商如何做好节点活动

三四线城市地产开发商如何做好节点活动

  三四线城市地产开发商如何做好节点活动

  “推广不够,暖场来凑,周周暖场,人走茶凉”这应该大多数三四线地产开发商做活动的现状,一次成功的开盘热销,不是开盘当天的推广和活动做得多声势浩大换来的,而是经过长期的艰苦卓绝住准备带来的,从广撒网的推广,到大规模拓客储客,到收网筛选客户,到最后收割客户是一个完整而浩瀚的工程。

  现在地产活动,应该是一个传播的闭环,从品牌落地到项目入市、认筹、开盘、热销一系列的节点活动,都应该是有思路有联系的,打好每一枪,才是一场完美的战争。对!请看我的手势——“完美”。(此处请脑补金星老师标准手势)

  下面笔者以之式公关活动为例,简单介绍三四线城市开发商节点活动事宜。

  █ 第一阶段:品牌导入期——“我来了”

  进入一个新市场,距离产品面世还有一段时间,所以最先开始品牌导入,累积第一批客户。这时候大多数开发商都是搞个品牌发布会,请请当地媒体,发个通稿完事。第一炮打得响不响,就看你用什么枪了。如果你不是万科、绿城等大佬开发商,就不要玩“xx你好,xx我来了”的套近乎,再说只凭一两句拉家常的客套话也无法打动当地老百姓,老百姓不买账,广告白瞎。

  之式公关案例:今年7月,江苏新城地产进驻济南,需要一场另泉城人记忆深刻的品牌认知活动。之式公关秉承让幸福变简单的企业理念,为新城品牌量身打造从品牌推广—品牌发布—公益活动—城市营销四个步骤完成品牌落地的整个公关传播闭环。

  第一环:全城派发新城品牌发布会英雄帖,借助新媒体力量,进行炒作,形成病毒式传播。第二环:全城送清凉公益活动,新城地产化身公益形象,向“为济南做出杰出贡献”的人群(交警、环卫)奉献清凉饮品。第三环:活动爆破品牌发布,经过前期的预热传播,发布会现场到场人气爆棚,发布会现场全城派发明信片环节将活动推向高&潮。第四环:后期传播延续幸福巴士亮相,新城幸福泉城精彩呈现。

  █ 第二阶段:推广期——营销中心开放

  由于三四线城市小,集中一次力量全面推广,通过一次亮相活动,全方位立体广告轰炸,便可达到家喻户晓,人人皆知的效果。

  之式公关案例:西城“时代首映礼 开门纳京沪”轨道交通与城市发展高峰论坛暨西城营销中心开放仪式。

  █ 第三阶段:蓄客期——上街拉客-推介会

  推广是推动客户主动联系项目,为了尽可能多的增加客户积累,还要走出去上街拉客,推动与拉动相结合,实现大规模客户储备。

  蓄客期活动主要为了增加客户量,所以以推介会为主,主要联系医院、学校、银行VIP客户、企业工厂等举办专场推介,同时,为一些意见领袖,如政府官员、行业会长等举办圈层活动或定制活动,如生日晚宴等。

  之式公关案例:针对泰安首席豪宅国山墅,之式公关提出为老客户定制圈层活动,因该项目有大批客户来自周边钢材企业,之式公关在其项目老客户中,针对钢材集团的一位女领导,以《女主人的下午茶》为主题,为其定制私人宴会、生日派对等系列活动,老带新近20人,为项目实现近亿元成交。

  第四阶段:筛客期——穿越人海找到你-认筹

  客户储备太多,跟客成本也相应提高,所以需要通过认筹来筛选客户,找出精准客户,同时强化购买意向。传统的三四线开发商做认筹活动通常是现场举办吃喝玩乐、歌舞表演、抽送礼等活动,利用“白吃白喝白拿”让现场“躁起来”,逼迫意向客户,吸引新客户。除非你是土豪开发商,否则只会劳民伤财费力不讨好。

  笔者认为本阶段宣传重点可具体到产品上,户型及工艺等,还可针对意向客户和媒体举办产品发布会,详细解读产品,展示产品工艺。

  之式公关案例: 泰安华新地产18年品牌深耕之作——奥源时代。1期产品认筹暨产品说明会嫁接品牌印象展,将华新18年来的品牌项目做成艺术展,吸引了大批客户驻足,现场人气气火爆异常,一期房源认筹近750,活动火爆程度出乎甲方预期,整个认筹完美收官。

  █ 第五阶段:收客期——开盘引爆

  精准客户已经找到,产品已经取得预售资格,要在客户最意向最高的时候,通过开盘集中快速分割客户,以免夜长梦多。但开盘活动绝对不是走走过场的流水席,虽然说现场多以流程化为主,但是一场别具风格开盘仪式,必定会让我们的准业主好感度上升一格。同时对于现场流程的把控更是一长开盘活动的重中之重。

  之式公关案例:开盘期间会分内场和外场活动,内场以开盘流程为主,是重点,主要是轮候区和认购区,轮候区有表演抽&奖,并不断播报认购情况,制造紧张氛围,认购区会控制选房时间,制造紧迫感,全程单向,不能返回,分批放人。外场一方面是宣传,线上线下全面展开造势,就连喇叭车腰鼓队巡游都会用上,制造浩大声势,吸引人到场围观热销场面,现场示范区、样板房照常开放,吸引新客户和犹豫观望客户,特设新客通道,以便新客购房。

  █ 第六阶段:热销期——业主答谢会

  热销之后还需清尾货,热销信息告诉消费者市场的信任和选择,抢夺观望犹豫客户,同时利用业主人际圈层,进行老带新,实现快速取尾货。这个时候的活动也不可小窥,如果仅仅以暖场收尾,那活动的意义也不大,答谢会做出温情与诚意才能让老业主有更多归属,促进业主的口碑传播。

  之式公关案例:济南高新万达客户答谢会,以酒会,中间穿插业主庆生环节,将整个答谢会做成一次温馨的家庭聚会。

  从拿地到最后开盘,是一个整体的长期过程,三四线城市开发商由于地域属性及市场环境或因预算问题,节点活动容易草草了事。处在互联网时代的地产推广没有活动的声势,更容易被这个信息轰炸的市场淹没,之式公关善于推陈出新,推出更多适应市场发展为客户解决实际问题的活动方案,让开发商与置业者有更好的沟通。

Contents
Preface
1 Introduction
1.1 Background
1.2 Scope
1.3 Notation
1.4 Distributions related to the Normal distribution
1.5 Quadratic forms
1.6 Estimation
1.7 Exercises
2 Model Fitting
2.1 Introduction
2.2 Examples
2.3 Some principles ofstatistica l modelling
2.4 Notation and coding for explanatory variables
2.5 Exercises
3 Exponential Family and Generalized Linear Models
3.1 Introduction
3.2 Exponential family of distributions
3.3 Properties ofdistribution s in the exponential family
3.4 Generalized linear models
3.5 Examples
3.6 Exercises
4 Estimation
4.1 Introduction
4.2 Example: Failure times for pressure vessels
4.3 Maximum likelihood estimation
4.4 Poisson regression example
4.5 Exercises
5 Inference
5.1 Introduction
5.2 Sampling distribution for score statistics
? 2002 by Chapman & Hall/CRC
5
5.3 Taylor series approximations
5.4 Sampling distribution for maximum likelihood estimators
5.5 Log-likelihood ratio statistic
5.6 Sampling distribution for the deviance
5.7 Hypothesis testing
5.8 Exercises
6 Normal Linear Models
6.1 Introduction
6.2 Basic results
6.3 Multiple linear regression
6.4 Analysis of variance
6.5 Analysis ofc ovariance
6.6 General linear models
6.7 Exercises
7 Binary Variables and Logistic Regression
7.1 Probability distributions
7.2 Generalized linear models
7.3 Dose response models
7.4 General logistic regression model
7.5 Goodness offi t statistics
7.6 Residuals
7.7 Other diagnostics
7.8 Example: Senility and WAIS
7.9 Exercises
8 Nominal and Ordinal Logistic Regression
8.1 Introduction
8.2 Multinomial distribution
8.3 Nominal logistic regression
8.4 Ordinal logistic regression
8.5 General comments
8.6 Exercises
9 Count Data, Poisson Regression and Log-Linear Models
9.1 Introduction
9.2 Poisson regression
9.3 Examples ofco ntingency tables
9.4 Probability models for contingency tables
9.5 Log-linear models
9.6 Inference for log-linear models
9.7 Numerical examples
9.8 Remarks
9.9 Exercises
? 2002 by Chapman & Hall/CRC
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10 Survival Analysis
10.1 Introduction
10.2 Survivor functions and hazard functions
10.3 Empirical survivor function
10.4 Estimation
10.5 Inference
10.6 Model checking
10.7 Example: remission times
10.8 Exercises
11 Clustered and Longitudinal Data
11.1 Introduction
11.2 Example: Recovery from stroke
11.3 Repeated measures models for Normal data
11.4 Repeated measures models for non-Normal data
11.5 Multilevel models
11.6 Stroke example continued
11.7 Comments
11.8 Exercises
Software
References
? 2002 by Chapman & Hall/CRC
7
Preface
Statistical tools for analyzing data are developing rapidly so that the 1990
edition ofthis book is now out ofdate.
The original purpose ofthe book was to present a unified theoretical and
conceptual framework for statistical modelling in a way that was accessible
to undergraduate students and researchers in other fields. This new edition
has been expanded to include nominal (or multinomial) and ordinal logistic
regression, survival analysis and analysis oflongitudinal and clustered data.
Although these topics do not fall strictly within the definition of generalized
linear models, the underlying principles and methods are very similar and
their inclusion is consistent with the original purpose ofthe book.
The new edition relies on numerical methods more than the previous edition
did. Some ofthe calculations can be performed with a spreadsheet while others
require statistical software. There is an emphasis on graphical methods for
exploratory data analysis, visualizing numerical optimization (for example,
ofthe likelihood function) and plotting residuals to check the adequacy of
models.

Introduction
1.1 Background
This book is designed to introduce the reader to generalized linear models;
these provide a unifying framework for many commonly used statistical techniques.
They also illustrate the ideas ofstatistical modelling.
The reader is assumed to have some familiarity with statistical principles
and methods. In particular, understanding the concepts ofestimation, sampling
distributions and hypothesis testing is necessary. Experience in the use
oft-tests, analysis ofv ariance, simple linear regression and chi-squared tests of
independence for two-dimensional contingency tables is assumed. In addition,
some knowledge ofmatrix algebra and calculus is required.
The reader will find it necessary to have access to statistical computing
facilities. Many statistical programs, languages or packages can now perform
the analyses discussed in this book. Often, however, they do so with a different
program or procedure for each type of analysis so that the unifying structure
is not apparent.
Some programs or languages which have procedures consistent with the
approach used in this book are: Stata, S-PLUS, Glim, Genstat and SYSTAT.
This list is not comprehensive as appropriate modules are continually
being added to other programs.
In addition, anyone working through this book may find it helpful to be able
to use mathematical software that can perform matrix algebra, differentiation
and iterative calculations.
1.2 Scope
The statistical methods considered in this

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