京公网安备 11010802034615号
经营许可证编号:京B2-20210330
大数据与数字化营销
【大数据与数字化营销】据对美公司首席信息官(CIO)的调查发现:仅23%的公司在收集顾客的人口信息和消费习惯之类的数据,并且利用这些数据进行战略决策。但其中却仅有46%的公司拥有数据分析的资源或系统。他们面对的主要挑战在于数据处理、信息管理和数据分析难题。数据管理平台(DMP)发展空间巨大,将是未来数字营销的理想工具。
文章全文:
To Handle Big Data, Advertisers Turn to DMPs
There’s a big to-do about Big Data and data management platforms (DMPs) in the digital advertising space. According to a new eMarketer report, “Data Management Platforms: Using Big Data to Power Marketing Performance,” DMPs enable marketers to use their Big Data to make smarter and more efficient marketing decisions.
Still even as brands use Big Data to build a holistic picture of their potential and real customers, many still find it challenging to extract cross-channel insight from that data.
Ziff Davis found 49% of companies polled worldwide had enacted a data management strategy as of fall 2012. And according to a survey from IT staffing service Robert Half Technology, just 23% of US chief information officers (CIOs) said they were collecting customer data such as demographic information or buying habits. Of that small percentage, less than half (46%) reported having the resources or systems to analyze the information they gathered.
A very general term, Big Data can refer to first-party customer information, third-party audience data, offline purchase data, online advertising behavioral data, campaign analytics and much more.
It can prove challenging to integrate disparate sets of data coming from social media, campaign analytics, offline sources or third parties. In fact, Big Data solution provider Infochimps surveyed IT professionals in North America and found that 83% of respondents said processing such information was a leading Big Data challenge, followed by managing the information (42%) and analyzing the data (41%).
If data is digital marketing’s currency, then the DMP is its bank. Big Data is stored and standardized here so that each data asset can be tied to a particular customer or audience segment. Once standardized, marketers can use that information to power multiple functions, both within digital and across a company’s broader marketing program.
DMPs can house both structured data, typically quantitative in nature, as well as unstructured data, often qualitative in nature—for example, social network data. Once all of these disparate sources are entered, DMPs can standardize them to build a larger, more descriptive picture of a customer or audience base that marketers can act on.
The DMP’s ability to take all of that Big Data from first-, second- and third-party sources and then organize it into meaningful audience segments makes it an ideal tool for audience targeting. This function—particularly for first- and third-party data—was also the top-reported competency of DMPs by US marketing professionals in a September 2012 surveyed by Winterberry Group.
Other than their role in organizing data on customers, DMPs are also a prime tool for campaign measurement, both within digital and across platforms.
“There’s real value in being able to address the audience first to determine what to buy,” said Mark Zagorski, CEO of data provider eXelate. “By looking at your audience and how they’re interacting with a particular ad or promotion, you can take those learnings and feed them into your current efforts and your next campaign.”
The full report, “Data Management Platforms: Using Big Data to Power Marketing Performance” also answers these key questions:
数据分析咨询请扫描二维码
若不方便扫码,搜微信号:CDAshujufenxi
在机器学习与数据分析领域,特征是连接数据与模型的核心载体,而特征重要性分析则是挖掘数据价值、优化模型性能、赋能业务决策的 ...
2026-01-27关联分析是数据挖掘领域中挖掘数据间潜在关联关系的经典方法,广泛应用于零售购物篮分析、电商推荐、用户行为路径挖掘等场景。而 ...
2026-01-27数据分析的基础范式,是支撑数据工作从“零散操作”走向“标准化落地”的核心方法论框架,它定义了数据分析的核心逻辑、流程与目 ...
2026-01-27在数据分析、后端开发、业务运维等工作中,SQL语句是操作数据库的核心工具。面对复杂的表结构、多表关联逻辑及灵活的查询需求, ...
2026-01-26支持向量机(SVM)作为机器学习中经典的分类算法,凭借其在小样本、高维数据场景下的优异泛化能力,被广泛应用于图像识别、文本 ...
2026-01-26在数字化浪潮下,数据分析已成为企业决策的核心支撑,而CDA数据分析师作为标准化、专业化的数据人才代表,正逐步成为连接数据资 ...
2026-01-26数据分析的核心价值在于用数据驱动决策,而指标作为数据的“载体”,其选取的合理性直接决定分析结果的有效性。选对指标能精准定 ...
2026-01-23在MySQL查询编写中,我们习惯按“SELECT → FROM → WHERE → ORDER BY”的语法顺序组织语句,直觉上认为代码顺序即执行顺序。但 ...
2026-01-23数字化转型已从企业“可选项”升级为“必答题”,其核心本质是通过数据驱动业务重构、流程优化与模式创新,实现从传统运营向智能 ...
2026-01-23CDA持证人已遍布在世界范围各行各业,包括世界500强企业、顶尖科技独角兽、大型金融机构、国企事业单位、国家行政机关等等,“CDA数据分析师”人才队伍遵守着CDA职业道德准则,发挥着专业技能,已成为支撑科技发展的核心力量。 ...
2026-01-22在数字化时代,企业积累的海量数据如同散落的珍珠,而数据模型就是串联这些珍珠的线——它并非简单的数据集合,而是对现实业务场 ...
2026-01-22在数字化运营场景中,用户每一次点击、浏览、交互都构成了行为轨迹,这些轨迹交织成海量的用户行为路径。但并非所有路径都具备业 ...
2026-01-22在数字化时代,企业数据资产的价值持续攀升,数据安全已从“合规底线”升级为“生存红线”。企业数据安全管理方法论以“战略引领 ...
2026-01-22在SQL数据分析与业务查询中,日期数据是高频处理对象——订单创建时间、用户注册日期、数据统计周期等场景,都需对日期进行格式 ...
2026-01-21在实际业务数据分析中,单一数据表往往无法满足需求——用户信息存储在用户表、消费记录在订单表、商品详情在商品表,想要挖掘“ ...
2026-01-21在数字化转型浪潮中,企业数据已从“辅助资源”升级为“核心资产”,而高效的数据管理则是释放数据价值的前提。企业数据管理方法 ...
2026-01-21在数字化商业环境中,数据已成为企业优化运营、抢占市场、规避风险的核心资产。但商业数据分析绝非“堆砌数据、生成报表”的简单 ...
2026-01-20定量报告的核心价值是传递数据洞察,但密密麻麻的表格、复杂的计算公式、晦涩的数值罗列,往往让读者望而却步,导致核心信息被淹 ...
2026-01-20在CDA(Certified Data Analyst)数据分析师的工作场景中,“精准分类与回归预测”是高频核心需求——比如预测用户是否流失、判 ...
2026-01-20在建筑工程造价工作中,清单汇总分类是核心环节之一,尤其是针对楼梯、楼梯间这类包含多个分项工程(如混凝土浇筑、钢筋制作、扶 ...
2026-01-19