"Massive sales… just for you!" - The Art of Personalized Marketing

The world of retail is going
through a transformation. In the past, retailers would employ mass marketing
campaigns to attract customers. But in the present era, where consumer tastes
are becoming more diverse and wide-ranging by the day, mass marketing is a much
less effective tool than it once was. As a result, retailers are now focusing
less on what the masses want, and more on what the individual wants. Personalized
marketing in retail is what’s vogue nowadays, and it’s driven by the cloud and
big data science.

What’s
personalized marketing all about?

Personalized marketing
attempts to generate customer loyalty by offering customers items, deals or
promotions which correspond to their preferences. If you’re a regular user of
Amazon or Taobao, you’ll be familiar with the concept. These popular online
storefronts present you with suggestions on what items to buy via analyzing
your past behavior, such as your purchasing history or what products you’ve
tended to search for when visiting those sites. In this way, each customer has
a unique shopping experience which matches their interests.

Personalized marketing is not
just the domain of e-commerce giants. Smaller retailers are also rapidly
embracing this trend, since a failure to do so could risk losing a large
proportion of shoppers who enjoy a personalized experience. According to a survey, 73% of customers like to do
business with retailers that use personal data to make their shopping
experience more relevant.

The technology that powers
personalized marketing in retail is usually a cloud service and big data analytics.
The behavior of potentially thousands of customers is tracked and uploaded to a
cloud database in real-time as they shop or browse. The data is then analyzed
by algorithms which produce recommendations for customers, again in real-time.
In addition to behavioral data, other types of data such as demographics or
location are also used to form customer recommendations.

The
future of personalized marketing in retail – Online 2 Offline

The kind of personalized
marketing described so far is actually the tip of the iceberg. Leveraging the
capabilities of the cloud, personalized marketing is projected to develop in
very exciting ways in the future, especially in the area of Online 2 Offline
(O2O).

O2O refers to when retailers
use online channels to enhance the experience of shoppers in brick and mortar stores.
O2O used to be fairly simplistic, such as offering customers coupons on social
media platforms which could be used in physical stores. But O2O will become
more sophisticated as retailers realize they can use the cloud to transfer the
personalized shopping experience people have online to physical shops as well. For example, the online
shopping behavior of customers which is uploaded to the cloud for analysis could
be used to suggest items or deals for shoppers when they enter brick and mortar
stores. Let’s say a person searches for a particular sweater at the online
store of a retailer but ultimately chooses not to buy the sweater. At a later
date, the person enters the retailer’s physical store, which retrieves data on
how that customer behaved on the online store from the cloud. Based on this
data, the person is provided with a large discount on the same sweater which
entices them to purchase it from the physical store.

The above example could work
the other way around as well. How customers behave in brick and mortar stores could
be uploaded to the cloud and analyzed in order to shape the recommendations
they are given online. In this way, the cloud can help blur the boundaries
between online and in-store retail by enabling customers to have a personalized
shopping experience across both channels.

Transparency
is important

Personalized marketing
ensures customers have a more intimate rather than cookie-cutter experience
when going to shop, which should drive sales and profitability. However, a
marketing expert recently warned that personalized marketing
could get ‘creepy’ and backfire if companies gather and use the personal data
of customers without acquiring their consent.

Microsoft crossed the
creepiness threshold when it launched Windows 10. The Operating System
collected some personal data from users by default in order to help “improve”
the product. This triggered a huge uproar from many users who thought the data
was being collected for nefarious purposes, and forced Microsoft to be more
transparent about its data collection policies and how users could opt out.

So if your business plans to collect
personal data to provide a tailored shopping experience for your customers, it’s
better to be up front about it and obtain their permission first.

时间: 2024-12-10 13:31:37

"Massive sales… just for you!" - The Art of Personalized Marketing的相关文章

设计数据仓库的注意事项(Part I)

设计|数据 IntroductionIdentifying and understanding the business and operational requirements that drive any data warehousing project are essential to the project抯 successful completion. You cannot meet your clients?needs if you have not assessed and ana

谈谈23种设计模式在Android项目中的应用

前言 本文将结合实际谈谈23种设计模式,每种设计模式涉及 定义:抽象化的定义与通俗的描述,尽量说明清楚其含义与应用场景 示例:如果项目中有使用过该模式,则会给出项目中的代码,否则会给出尽可能简单好理解的java代码 Android:该设计模式在Android源码框架中哪些地方有使用到 重构:项目中是否存在可以用该模式进行重构的地方,如果有会给出重构前与重构后的代码或者思路 用这种方式进行介绍设计模式,旨在结合每天都在接触的Android实际项目开发更好地理解设计模式,拉近与设计模式的距离,同时在

Salesforce Einstein承诺提供“开箱即用”的人工智能应用程序

Salesforce在Einstein人工智能上采用了一种应用程序优先的方法.这和公共云人工智能产品形成了鲜明的对比,采用这种做法的部分原因是该公司汲取了从Salesforce Wave学到的教训. 这个世界上没有足够多的数据科学家,所以Salesforce依靠自动化和以应用程序为中心的方法来将其Einstein人工智能功能带给大众. 在上周于旧金山举行的Salesforce分析师峰会上,公司高管们分享了该公司在两年多的时间里工作的详细情况,目标是建立高度自动化的数据管理和机器学习通道,以大规模

麦肯锡 | 消费者决策流程: 演变、重塑和争论

2007年,麦肯锡咨询公司提出了消费者决策流程(Consumer Decision Journey,CDJ)理念,替代传统营销漏斗(The Marketing Funnel)理论作为企业营销的方法论.基于这个理念设计的环状CDJ模型,比传统漏斗模型更能反应互联网时代的消费者新变化,也比谷歌的ZMOT(Zero Moment Of Truth)更好懂(这个词至今都没有一个靠谱的翻译).从此,营销狗们的PPT更有说服力了,麦肯锡的PPT更值钱了. 时隔8年之后,麦肯锡的这套理论要更新了. 消费者决策

[20150515]关于块转储问题.txt

[20150515]关于块转储问题.txt --我自己在学习oracle有时候使用块转储时,发现转储的内容跟我自己的想象不一样. --正好前一阵子ITPUB有人也遇到类似的问题,自己做一个简单探究: 1.建立测试环境: SCOTT@test> @ver1 PORT_STRING                    VERSION        BANNER ------------------------------ -------------- -----------------------

[20150520]使用gdb查看等待事件.txt

[20150520]使用gdb查看等待事件.txt -- 昨天开始重看vage-- 使用gdb 看等待事件这部分内容跳过了,今天测试看看.如何操作. -- 实际上设置断点在gdb下,11g等待事件的起始函数是kslwtbctx函数.还是通过演示来说明: 1.测试环境: SCOTT@test> @ver1 PORT_STRING                    VERSION        BANNER ------------------------------ -------------

[20150518]关于块转储问题2.txt

[20150518]关于块转储问题2.txt --我自己在学习oracle有时候使用块转储时,发现转储的内容跟我自己的想象不一样. --正好前一阵子ITPUB有人也遇到类似的问题,自己做一个简单探究,参考链接如下: http://blog.itpub.net/267265/viewspace-1655497/ -- 我前面提到块转储alter system dump datafile 4 block 1523;,仅仅从数据文件读取.无论在何种情况下. -- 昨天看了相关文档,可以使用如下: AL

[20150612]使用bvi查看数据块.txt

[20150612]使用bvi查看数据块.txt --编写一个简单的脚本实现bvi查看数据块,主要我现在喜欢使用bbed查看,而修改选择bvi. --通过例子来说明: SCOTT@test> select rowid,dept.* from dept ; ROWID                    DEPTNO DNAME          LOC ------------------ ------------ -------------- ------------- AABJVUAAEA

[20150513]人为破坏数据块.txt

[20150513]人为破坏数据块.txt --演示的目的,参考链接: http://www.askmaclean.com/archives/oracle-make-block-physical-corruption.html --不要在生产系统测试!!!!! 1.建立测试环境: SCOTT@test> @ &r/ver1 PORT_STRING                    VERSION        BANNER ------------------------------ -