パーソナライズのための機械学習 |Sitecore

人工知能(AI)そしてMLは、今日の私たちの日常生活に織り込まれています。多くの場合、大きなメリットがあります。

アルゴリズムは、飛行機での移動中に安全を確保するために、私たちが依存する飛行パターンを指示します。自然言語処理(NLP)は、SiriやAlexaとの対話を強化します。機械学習は、Netflixの提案のキュレーションそして、その接触者追跡Covid-19との闘いに役立っています.

一方、ソーシャルメディアフィードやGoogle検索で集団思考を推進するアルゴリズムの背後にある決定であろうと、採用における格差を助長する偏ったデータ, AIとMLaren’t always beneficial.

As consumers, citizens, and professionals, we should all have an understanding of the ways AIとMLare being put to use, how they affect us, and what benefits they provide.In this blog, we consider the role of AIとMLin personalization.By grasping how machine learning is being put to use to drive personalization, specifically within digital customer experiences, you’ll be better equipped to take advantage of this exciting technology for massive benefits in your organization.

パーソナライズの必須事項は変わりません

But first, let’s note this — personalization isn’t going anywhere.Once a luxury, personalization has become a baseline service in today's digital economy, one the vast majority of consumers appreciate:

90%が見つかりましたマーケティングのパーソナライズは、やや魅力的または非常に魅力的です
80%がパーソナライズされたエクスペリエンスを提供するブランドから購入する可能性が高くなります
72%が回答彼らはパーソナライズされたメッセージングにのみ関与します

Consumers’ love for personalization makes sense.We all embrace experiences that offer us value, and this means being treated like the individuals we are.All businesses today must look for opportunities to show customers they understand their interests, preferences, and intent by delivering relevant content and products to ensure they’re not wasting their customer’s time.

Unfortunately, getting a personalization program up and running is no simple task.Enter machine learning, whose algorithms can support, automate, and accelerate the process.

AIとML

Second, let’s clarify some terms.

人工知能(AI) refers to the broad arena of techniques used to get machines to perform tasks that appear intelligent.Machine learning is a subset of AI.

Over the past couple of decades, machine learning has become a central focus of AI research due to its success in completing cognitive tasks that, less than a century ago, seemed impossible — beating humans at complex games such as Chess, Go, and Jeopardy, driving cars, translating languages, etc.

But neither machine learning nor the other AI methods can currently begin to compete with humans when it comes to improvising, formulating strategies, communicating empathetically, imagining novel situations, inventing new products, and the list goes on.

Machine learning and AI can support some tasks and completely automate others, but machines won’t be displaying creative intelligence, let alone consciousness, any time soon. They definitely don’t mean the end of marketing.No one knows their customers like a brand does, and every brand relies on their employees when it comes to empathetic listening, messaging, and service.AI can detect trends, but completing the circle of powerful customer experiences requires a human element.

(ちなみに、AIの代わりに知能拡張(IA)のような用語を使うことを好む人がいるのはそのためです。

パーソナライズのための機械学習技術

While machine learning can feel like magic, the truth is it’s simply statistical and probabilistic models put to work toward a (usually) defined end.Machine learning analyzes large datasets to identify trends.From this it can extrapolate what’s most probable to happen or what type of experience is most likely to lead to a certain result.

Of course, while it’s not magic, it’s not exactly simple either.

ここでは、パーソナライズに使用される最も一般的なML手法とその使用目的について説明します。

回帰分析

Linear regression could help discover which pages are most likely to lead to a conversion.Logistical regression could be used to discover the best follow-up actions for an abandoned cart.

協会

From Netflix to Amazon, this method is a critical tool for building out recommendation engines.Based on your purchase of Dan and Chip Heath’s 瞬間の力たとえば、AmazonのMLはSeth Godinのパーミッションマーケティング.

クラスタリング

クラスタリング algorithms are a great tool for grouping customers into segments.

マルコフ鎖

ユーザーのリアルタイムのWebサイトの行動を分析し、それに基づいてナビゲーション予測を行い、エクスペリエンスをパーソナライズするために使用できます。

ディープラーニング

SiriやAlexaを支える自然言語処理(NLP)から、可能なダイレクトマーケティング戦術の価値の判断、モバイル広告のオーディエンスのセグメント化まで、ディープラーニングは、過去数十年でMLの最もエキサイティングな研究の多くが行われてきた場所です。

Most machine-learning engines use a combination of these methods to analyze data and offer insight.

パーソナライズにおけるMLの概要

It’s important to have a working knowledge of what’s under the hood, but you want to get the machine learning engine started for your personalization program.The following are not linear steps to take.Your program will be unique depending on your market, size, and in-the-moment goals.But keeping these suggestions in mind as you begin imagining, designing, and creating your program will streamline the process significantly.

ユーザー中心に保つ

The user is always the place to start.You know your business goals, and, hopefully, you’ve aligned them with your web goals.(If not, check out エンゲージメントバリューに関するこの記事.) With these goals in mind, you can start looking for various ways to improve the user experience.What are the critical points of interaction? How can you remove friction or better direct a user toward a specific action?

Keeping your user’s needs front and center and letting empathy drive your use of machine learning and AI is a great way to ensure you’re offering value, versus just using the shiny new thing.

自分のルールを知る

You can (and probably should) use personalization across the entire web journey.This can take many forms, personalized search being one great example.There are, however, four broad categories of personalization rule types.

コンテキスト

コンテキスト rules personalize experiences based on known facts about a user, such as Geo IP address or the channel of entry into a site.

暁

Some visitors to your site will self-identify by filling in a form for a discount, giving you an email, etc.暁 rules personalize experiences for these known visitors by, for example, using data from previous page views and conversions to assess the most likely content the visitor is looking for.

暗黙の

ユーザーが誰であるかがわからない場合は、暗黙的なルールでパターンマッチングとペルソナマッチングを使用して、匿名ユーザーがサイトで行うアクションに基づいてエクスペリエンスをパーソナライズすることができます。

習慣

さらなる開発が必要ですが、カスタムルールは必要なものを使用してパーソナライズすることができます。

AIとMLcan support all of these rules, but some solutions will require you to determine which rules you want to implement where and when.

小さく始める

Chances are my Netflix queue and your Netflix queue have at least one overlapping movie or TV show suggestion.But this movie or TV show probably looks different in each of our queues.While I love comedy, you’re (let’s pretend) a huge action fan, and Netflix uses this knowledge to tailor the image it places on the recommended movie or TV show.

画像を変えるのは比較的簡単なことですが、提案を見てエンゲージメントを維持する可能性を高めるための微妙な方法です。

Likewise, you can start small with your machine-learning personalization program.

For example, try offering 5 different homepage banners, each tailored to a different persona, and let a machine learning algorithm determine who sees what.Or make variants by switching up any of the elements typically found in marketing assets: headlines, subheads, images, formatting, color, copy, call to action, etc.The point is you can and should start small and build on quick wins.

まず最初にすべきこと - 解決策を決定する

私たちは、パーソナライズの旅を始めた、または始めようとしているすべての人を支援するためにeブックを作成しました。パーソナライゼーションへの道:より強固な関係を築くための9つの鍵.The nine keys include making personalization a business priority, establishing your team, outlining your audience and their journeys, and more.

But even with all nine keys in place, implementing machine learning in your personalization strategy from scratch can be a huge lift.This is why it’s critical to choose the right solution.The right one will do the machine learning heavy lifting for you, helping you keep your team as lean and nimble as possible while getting all the benefits of machine learning and AI.

We recently introduced Sitecore AI Auto-Personalization Standard.Simply toggle a switch to turn it on, and it identifies visitor trends, creates customer segments, and modifies page elements to deliver a personalized customer experience.Create one of the above rules and Sitecore AI will tell you which variation of content is most likely to drive engagement for each customer.Sitecore AI Auto-Personalization automates 1:1 customer experiences.Its sophistication, ease-of-use, and effectiveness helped Sitecore win the 2020年コンテンツマーケティング賞人工知能を含むコンテンツのベストユース部門私たちの仕事のためにマイクロソフトのパートナーネットワーク.

Sitecore AI Auto-Personalization Premium is available for customers who want unlimited personalization.It also provides an AI insights dashboard, which includes audiences identified from historical and daily data.This dashboard is a great way for marketers to see what’s connecting and what’s not, so they can make changes to the assumptions driving the AI that’s driving customer experiences.

について詳細AIとそのすべての利点.

Personalization is no longer optional

Personalization has moved from a nice-to-have to a must-have.

71%

of consumers

expect companies to deliver personalized interactions.

76%

of people

get frustrated when personalization doesn’t happen.

trillion dollar

opportunity for brands using AI to personalize customer experiences.

People want experiences that feel relevant and respectful of their time. Businesses that deliver this win loyalty. The challenge? Delivering personalized content at scale is hard. That is where machine learning comes in.

AI and machine learning: what’s the difference?

Artificial intelligence (AI) is the science of creating systems that perform tasks we usually associate with human intelligence, like reasoning, problem-solving, understanding language, and making decisions. AI can follow explicit rules, learn from data, or combine both approaches.

Machine learning (ML) is a subset of AI. Instead of relying only on pre-set rules, machine learning learns from data. It identifies patterns, adapts to new information, and improves over time without being reprogrammed for every scenario. In short: all machine learning is AI, but not all AI uses machine learning.

Recent breakthroughs in machine learning power many of the AI applications we see today: recommendation engines, fraud detection, self-driving cars, and real-time language translation. These systems process massive amounts of real-time data, spot patterns in browsing history and purchase history, and use predictive models to make predictions at a speed no human can match.

Still, machine learning is not a substitute for human creativity, judgment, or empathy. AI models can optimize processes and surface insights, but they don’t understand context or values the way people do. Building trust and creating hyper-personalized user experiences still require human perspective. The best results come when AI and people work together, leveraging machine learning and machine learning algorithms to combine scalable ai-powered efficiency with human insight to make smarter decisions.

How machine learning drives personalization

Machine learning analyzes large datasets to identify patterns in customer behavior, predict what customers are likely to want next, and deliver more relevant, timely experiences. Rather than relying on static rules, these models continuously learn from customer data and adjust as customer preferences and user behaviors change. Some of the most common machine learning techniques used in personalization include:

Regression analysis

Regression models estimate relationships between variables to predict outcomes. In personalization, they’re often used to understand which pages, messages, or actions are most likely to lead to conversions, helping teams optimize content, offers, and journeys based on probability rather than guesswork.

Association

Association techniques uncover relationships between items or behaviors that frequently occur together. This is the foundation of many recommendation engines—such as those used by Netflix or Amazon—where past user interactions or purchasing patterns are used to suggest relevant content or make product recommendations.

Clustering

Clustering algorithms group customers based on shared characteristics or behaviors without requiring predefined segments. This allows organizations to move beyond broad personas and create dynamic customer segments that evolve over time, enabling more targeted and personalized experiences.

Markov chains

Markov models analyze sequences of behavior to predict what a customer is likely to do next. By focusing on real-time interactions and transition probabilities, these models are especially useful for guiding next-best actions and adapting experiences as customers move through a journey.

Deep learning

Deep learning uses multi-layered neural networks to model complex patterns in large, unstructured datasets. In personalization, it powers advanced capabilities such as natural language processing, image recognition, and highly granular audience segmentation—making it possible to tailor experiences across channels and content types.

Most modern personalization engines combine several of these techniques, using each where it performs best. Together, they enable experiences that are more accurate and responsive while still leaving room for human strategy, creativity, and oversight.

Steps to take now

1. Make personalization a business priority

Personalization is no longer optional. Set clear goals for how it supports growth, customer loyalty, and competitive advantage.

2. Invest in the right technology

Choose platforms that simplify AI-driven personalization instead of adding complexity. Look for solutions that automate the heavy lifting while giving your team control and visibility.

3. Start small, scale fast

Begin with quick wins—like testing homepage variations or targeted content—and use machine learning to optimize. Build on what works and expand gradually.

4. Build a data foundation

Ensure your organization has clean, high-quality data. AI depends on it. Align teams on data collection governance and privacy standards to maintain trust.

5. Empower your people

AI is a tool, not a replacement. Equip your teams with training and workflows to combine machine intelligence with human creativity and empathy.

6. Keep the customer at the center

Every decision should start with the customer experience and focus on customer satisfaction. Use AI to remove friction, anticipate needs, and deliver value at every touchpoint.