Marketing Technologies in Computer Science | 计算机科学中的市场营销技术

📚 Marketing Technologies in Computer Science | 计算机科学中的市场营销技术

Marketing has moved from guesswork to measurement. Modern campaigns rely on computer systems that collect data, run algorithms and automate decisions. This article links A-Level Computer Science topics such as databases, machine learning, web technologies and ethics to real marketing systems.

市场营销已经从猜测转向了可衡量的体系。现代营销活动依赖计算机系统来收集数据、运行算法并自动作出决策。本文将A-Level计算机科学中数据库、机器学习、网络技术和伦理等主题与现实中的营销系统联系起来。

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1. Marketing and Computer Science: The Data-Driven Shift | 市场营销与计算机科学：数据驱动转型

Traditional marketing used mass media such as television and newspapers, where success was difficult to measure. Today a customer journey can be represented as a pipeline: impression → click → sign-up → purchase. Each stage is an event that a computer system can record and analyse.

传统营销使用电视和报纸等大众媒体，成功与否很难衡量。今天，客户旅程可以表示为一条流水线：展示→点击→注册→购买。每个阶段都是计算机系统可以记录和分析的事件。

In Cambridge A-Level Computer Science, this shift is studied through data representation, relational databases, algorithms and the internet. Marketing systems are therefore a useful context for understanding how these topics interact in industry.

在剑桥A-Level计算机科学中，这一转变通过数据表示、关系数据库、算法和互联网来学习。因此，营销系统是理解这些主题在行业中如何相互作用的一个有用背景。

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2. Data Collection: Cookies, Tracking and Privacy | 数据收集：Cookie、追踪与隐私

Websites use cookies, tracking pixels and scripts to record user behaviour. A typical log entry stores fields such as timestamp, user ID, page URL, device type and referrer. These records form the input data for marketing analysis.

网站使用Cookie、追踪像素和脚本来记录用户行为。一条典型的日志条目会存储时间戳、用户ID、页面URL、设备类型和来源页面等字段。这些记录构成了营销分析的输入数据。

First-party cookies are set by the site the user is visiting, while third-party cookies are set by advertising networks and can track users across multiple websites. Under the UK Data Protection Act 2018 and the General Data Protection Regulation, personal data requires a lawful basis such as consent.

第一方Cookie由用户正在访问的网站设置，而第三方Cookie由广告网络设置，可以跨多个网站追踪用户。根据英国《2018年数据保护法》和《通用数据保护条例》，处理个人数据需要合法依据，例如用户同意。

Marketers must also follow the principle of data minimisation: only collect what is necessary, store it securely and delete it when it is no longer needed.

营销人员还必须遵循数据最小化原则：只收集必要数据，安全存储，并在不再需要时删除。

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3. Customer Segmentation Using Clustering | 使用聚类进行客户细分

Customer segmentation divides a market into groups with similar characteristics. In computer science, this is often achieved by clustering, an unsupervised learning method that does not require labelled data. The k-means algorithm is a common choice.

客户细分将市场划分为具有相似特征的群体。在计算机科学中，这通常通过聚类实现，聚类是一种不需要标签数据的无监督学习方法。k均值算法是常见的选择。

K-means works by selecting k initial centroids, assigning each data point to the nearest centroid and then recalculating each centroid as the mean of its assigned points. This process repeats until the assignments stop changing. Distance is usually calculated with Euclidean distance:

K均值算法首先选择k个初始质心，将每个数据点分配到最近的质心，然后将每个质心重新计算为其分配点的均值。这个过程不断重复，直到分配不再变化。距离通常使用欧几里得距离计算：

d = √((x₂ − x₁)² + (y₂ − y₁)²)

The value of k can be chosen using the elbow method or silhouette score. Once segments are found, a business can target high-value customers with different messages and offers.

k值可以通过肘部法则或轮廓系数来选择。一旦找到细分群体，企业就可以向高价值客户推送不同的信息和优惠。

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4. Recommendation Systems and Collaborative Filtering | 推荐系统与协同过滤

Recommendation systems predict how much a user might like an item, such as a product or film. Collaborative filtering builds a user-item matrix from ratings or purchase histories. If two users have rated items similarly in the past, their preferences are assumed to be similar.

推荐系统预测用户对某个商品或电影等项目的喜欢程度。协同过滤根据评分或购买历史构建用户-商品矩阵。如果两个用户过去对商品的评分相似，就认为他们的偏好也相似。

A basic prediction can be made by taking a user’s average rating and adjusting it with the weighted deviations of similar users. This can be written as:

一个基本的预测方法是先取用户的平均评分，再用相似用户的加权偏差进行调整。这可以写成：

predicted rating = average rating + (sum of weighted deviations ÷ sum of weights)

The cold start problem occurs when a new user or item has no history, so the system cannot make a reliable prediction. Hybrid recommenders combine collaborative filtering with content-based filtering, which uses item attributes such as genre or keywords.

冷启动问题出现在新用户或新商品没有历史记录时，系统无法作出可靠预测。混合推荐系统将协同过滤与基于内容的过滤相结合，后者使用商品类型或关键词等属性。

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5. A/B Testing and Controlled Experiments | A/B测试与对照实验

A/B testing is a controlled experiment that randomly splits website visitors into two groups. Group A sees the original version and group B sees a modified version. The aim is to compare a metric such as conversion rate:

A/B测试是一种对照实验，将网站访问者随机分为两组。A组看到原始版本，B组看到修改版本。目的是比较转化率等指标：

Conversion rate = conversions ÷ visitors × 100%

Analysts set a null hypothesis that there is no difference between the two versions. If the observed difference is very unlikely under the null hypothesis, typically p < 0.05, it is declared statistically significant.

分析人员会设定零假设，即两个版本之间没有差异。如果在零假设下观察到的差异非常不可能出现，通常p < 0.05，就认为它具有统计显著性。

Engineers must also avoid common pitfalls, such as ending the test too early, ignoring the novelty effect or using too small a sample. A larger sample reduces random error and increases confidence in the result.

工程师还必须避免常见错误，例如过早结束测试、忽略新奇效应或样本量过小。较大的样本可以减少随机误差并提高结果的可信度。

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6. Predictive Analytics: Churn and Lifetime Value | 预测分析：流失与生命周期价值

Supervised learning can predict whether a customer will churn, meaning stop using a service. Features might include login frequency, days since last purchase, number of support tickets and subscription length. Algorithms include logistic regression, decision trees and random forests.

监督学习可以预测客户是否会流失，也就是停止使用某项服务。特征可能包括登录频率、距离上次购买的天数、支持工单数量和订阅时长。算法包括逻辑回归、决策树和随机森林。

Logistic regression outputs a probability between 0 and 1 using a sigmoid function. Its decision boundary is learned from the training data.

逻辑回归使用S型函数输出0到1之间的概率。它的决策边界从训练数据中学习得到。

Customer lifetime value is the total profit a business expects from a customer over the whole relationship. A simple model is:

客户生命周期价值是企业在整个客户关系中预期获得的总利润。一个简单模型是：

CLV = average order value × purchase frequency × customer lifespan

Predictive models help a company decide how much to spend on retaining each customer and which customers need early intervention.

预测模型帮助企业决定在每位客户身上投入多少留存成本，以及哪些客户需要早期干预。

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7. Search Engine Marketing and Ranking Algorithms | 搜索引擎营销与排名算法

Search engines rank web pages using algorithms. PageRank, developed by Google, models links between pages as votes of importance. The score of a page depends on the scores of pages that link to it.

搜索引擎使用算法对网页进行排名。谷歌开发的PageRank将页面之间的链接视为重要性的投票。一个页面的得分取决于链接到它的页面的得分。

Search engine marketing includes search engine optimisation, which improves organic rankings, and paid search, where advertisers bid on keywords. Ad rank is usually calculated from the bid and a quality score that measures relevance.

搜索引擎营销包括搜索引擎优化，用于提高自然排名；以及付费搜索，广告主对关键词出价。广告排名通常由出价和衡量相关性的质量得分共同计算。

A useful metric in search campaigns is the click-through rate:

搜索广告中一个有用的指标是点击率：

CTR = clicks ÷ impressions × 100%

Computer systems store keyword data in databases and use algorithms to match queries with advertisements. Ranking must be fast, so indexing and caching are important.

计算机系统将关键词数据存储在数据库中，并使用算法将查询与广告匹配。排名必须快速，因此索引和缓存非常重要。

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8. Programmatic Advertising and Real-Time Bidding | 程序化广告与实时竞价

Programmatic advertising automates the buying and selling of online ad space. When a page loads, an auction takes place in real time. A supply-side platform offers ad inventory, while a demand-side platform bids on behalf of advertisers.

程序化广告自动化在线广告位的买卖。当页面加载时，会实时进行拍卖。供应方平台提供广告库存，需求方平台代表广告主出价。

The winning ad is chosen within milliseconds. This requires distributed systems, low-latency data stores and machine learning models that estimate the probability of a click or conversion.

获胜的广告在毫秒内被选出。这需要分布式系统、低延迟数据存储以及估计点击或转化概率的机器学习模型。

Pricing model	Meaning
CPM	Cost per thousand impressions
CPC	Cost per click
CPA	Cost per acquisition or action

These models determine how an advertiser pays. Choosing the right model depends on the campaign goal: awareness, traffic or sales.

这些模式决定了广告主的付费方式。选择哪种模式取决于营销目标：曝光、流量还是销售。

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9. Sentiment Analysis and Social Media Monitoring | 情感分析与社交媒体监测

Sentiment analysis is a natural language processing task that classifies text as positive, neutral or negative. Marketers monitor social media posts, reviews and emails to understand public reactions to a brand or campaign.

情感分析是一项自然语言处理任务，将文本分类为正面、中性或负面。营销人员监测社交媒体帖子、评论和邮件，以了解公众对品牌或营销活动的反应。

Rule-based systems use sentiment lexicons containing words with polarity scores. For example, ‘excellent’ adds positive score while ‘terrible’ adds negative score. Supervised models such as naive Bayes treat sentiment classification as a text classification problem.

基于规则的系统使用包含词语极性得分的情感词典。例如，’excellent’增加正面得分，而’terrible’增加负面得分。朴素贝叶斯等监督模型将情感分类视为文本分类问题。

Common challenges include sarcasm, emojis, negation and multilingual content. A sentence such as ‘Great, another delay!’ may be positive by vocabulary but negative in meaning.

常见挑战包括讽刺、表情

Published by TutorHao | A-Level Computer Science Revision Series | aleveler.com

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