Marketing is an important aspect of any successful company. In simple words, marketing refers to the business’s activities related to buying and selling a product or service.

營銷是任何成功公司的重要方面。 簡而言之，營銷是指與購買或出售產品或服務有關的企業活動。

以客戶為中心 (Customer Centricity)

Customer centricity is putting your customer first and at the core of your business to provide the best possible experience and build long-term relationships.

以客戶為中心將客戶放在第一位，並將業務放在核心位置，以提供最佳的體驗並建立長期的合作關係。

An organization with a customer-centric mindset has customer experience as a core value that helps in the decision-making process for leadership, reflects the beliefs that employees support, and provides clarity to current and potential customers.

一個以客戶為中心

以客戶為中心的營銷 (Customer-Centric Marketing)

Customer-centric marketing is an approach that prioritizes customers’ needs and interests in all decisions related to advertising, selling, and promoting products and services.

以客戶為中心的營銷是一種在與廣告，銷售以及促銷產品和服務有關的所有決策中將客戶的需求和興趣放在優先位置的方法。

For successful customer-centric marketing, you need a deep understanding of the question: why do your customers need your product or service? The goal is not business growth alone; it is growth driven by convincing customers to believe that whatever your company provides can improve some aspect of their work or life.

為了成功進行以客戶為中心的營銷，您需要對以下問題有深刻的理解：為什麼客戶需要您的產品或服務？ 我們的目標不只是業務增長。 通過說服客戶相信公司提供的任何產品都可以改善他們工作或生活的某些方面來驅動增長。

機器學習-人工智慧的子集 (Machine Learning — A Subset of Artificial Intelligence)

Artificial Intelligence (AI) — is a computer science field focused on making machines seem like they possess human intelligence. It is called “artificial” because humans create it, and it does not exist naturally.

人工智慧(AI) —是一門電腦科學領域，致力於使機器看起來好像具有人類智慧。 之所以稱為“人工”，是因為人類創造了它，並且它不是自然存在的。

Machine Learning (ML) is a subset of AI. ML algorithms are computer-implementable instructions that take a dataset as input. They find out patterns within the dataset that were previously undiscovered.

機器學習(ML)是AI的子集。 ML演算法是計算機可執行的指令，將資料集作為輸入。 他們在資料集中找出以前未被發現的模式。

Supervised ML is when these patterns lead to some labels on the data, and your ML model learns how to map each unique pattern to the most appropriate label. This type of ML is used for predicting labels for unlabeled data.

監督式ML是這些模式在資料上導致一些標籤的時候，您的ML模型學習如何將每個唯一的模式對映到最合適的標籤。 這種型別的ML用於預測未標記資料的標籤。

Unsupervised ML is when your model presents the discovered patterns to the users to provide insights.

無監督的ML是指您的模型將發現的模式呈現給使用者以提供見解。

ML models improve their performance over time as they encounter more and more data. They improve by experience, just like a human would when indulging in a new activity through careful observation and self-correction.

隨著ML模型遇到越來越多的資料，它們的效能會隨著時間的推移而提高。 它們會隨著經驗的增長而提高，就像人類通過仔細觀察和自我糾正沉迷於新活動時一樣。

以客戶為中心的營銷的機器學習 (Machine Learning For Customer-Centric Marketing)

As competition gets tougher, consumers now have more choices of businesses to engage with, making ML evaluative to efficiently reaching and engaging your customers.

隨著競爭的加劇，消費者現在可以選擇更多的業務選擇，從而使ML能夠有效地吸引和吸引客戶。

All business owners have noticed the growing hype around AI in marketing. AI applications in marketing include chatbots, content creation, programmatic advertising and a lot more.

所有企業主都注意到，在市場上圍繞AI的炒作越來越多。 營銷中的AI應用程式包括聊天機器人，內容建立，程式化廣告等等。

With so many AI applications in marketing, you should never lose sight of what is essential in implementing effective and optimized marketing strategy i.e. deep and clear understanding of your customers.

在市場營銷中有如此多的AI應用程式，您永遠都不應忽視實施有效和優化的營銷策略的基本要素，即對客戶的深刻而清晰的瞭解。

Once your ML model is well trained, it can quickly categorize any new data inputs and predict likely outcomes. You can generate deeper customer insights and make better behavioral predictions.

一旦您的ML模型經過了良好的訓練，就可以快速對所有新資料輸入進行分類並預測可能的結果。 您可以產生更深刻的客戶見解，並做出更好的行為預測。

These can be related to your prospects’ and customers’ probability of converting on certain campaigns, increasing their frequency of purchase, churn or lapse, or something more specific.

這些可能與您的潛在客戶和客戶轉換某些廣告系列，增加其購買，流失或失誤的頻率或更具體的內容有關的可能性有關。

Leveraging ML in your marketing strategy is no longer a luxury. Rather, it has become a necessity.

在您的營銷策略中使用ML不再是奢侈。 相反，它已成為必需品。

Let’s take a look at ML-driven insights marketers are using to come up with best customer-centric marketing strategies.

讓我們看看營銷人員正在使用ML驅動的見解來提出最佳的以客戶為中心的營銷策略。

行為洞察力和預測 (Behavioral Insights and Predictions)

Which behavioral patterns of your customers led them to take a particular action in the past?

過去，您的客戶的哪種行為方式使他們採取了特定措施？

Did the customers who ended their relationship with you developed behavioral patterns significantly different from those who remained loyal to you?

終止與您的關係的客戶的行為模式是否與仍然忠於您的客戶形成了明顯的差異？

It is not easy for a human mind to figure out and study all these patterns. The mathematics behind all this is very complex.

人的頭腦很難弄清楚並研究所有這些模式。 所有這些背後的數學非常複雜。

Therefore, your ML model takes care of this task. It can detect which piece of information about a customer has put how much weight on their decision to take a particular action, such as churning.

因此，您的ML模型可以完成此任務。 它可以檢測到有關客戶的哪些資訊對他們採取特定行動(例如攪動)的決定有多大的影響。

It is important to anticipate your customer’s actions before they take them, especially when the action is irreversible. A lost customer is an irreversible loss. You should never have to begin facing the loss and then start thinking about reducing and preventing it.

重要的是在客戶採取行動之前就對其進行預測，尤其是當這些行動不可逆轉時。 失去客戶是不可挽回的損失。 您永遠不必開始面對損失，然後開始考慮減少和防止損失。

Supervised ML algorithms can discover predictive patterns hidden deep in your customer data.

監督的ML演算法可以發現隱藏在客戶資料深處的預測模式。

You can use them to find out which prospects are most likely to become your customers by training your model on the data of previous prospects who were successfully converted into customers and the prospects who were lost.

您可以通過使用模型將成功轉換為客戶的先前潛在客戶和丟失的潛在客戶的資料訓練模型，從而使用它們找出最有可能成為您的客戶的潛在客戶。

Once your model is trained, your model can take potential customers’ data as input and predict the extent to which a new lead best “looks like” leads who were successfully converted into customers in the past.

一旦對模型進行了訓練，您的模型就可以將潛在客戶的資料作為輸入，並預測在過去成功轉換為客戶的最佳“看起來”新潛在客戶的程度。

Similarly, you can use supervised ML to predict if a customer is likely to churn. You can train your model on data of those customers who are already lost and those who are still active.

同樣，您可以使用監督式ML來預測客戶是否可能流失。 您可以根據已經丟失的客戶和仍然活躍的客戶的資料訓練模型。

Your model can then take data of your current customers as input and figure out if any customer’s data shows patterns similar to those in data of lost customers.

然後，您的模型可以將當前客戶的資料作為輸入，並確定是否有任何客戶的資料顯示出與丟失客戶的資料相似的模式。

The anticipation of your customers’ actions would allow you to come up with the most effective marketing campaigns according to the point they are at in their customer journey.

預期客戶的行為將使您能夠根據客戶在客戶旅程中所處的點來提出最有效的營銷活動。

基於角色的見解和預測 (Persona-Based Insights and Predictions)

Although predicting your customer’s next move is very helpful in reaching the right people at the right time, it is not where the road to truly optimized marketing ends.

儘管預測客戶的下一步行動對在正確的時間接觸合適的人非常有幫助，但這並不是真正優化營銷之路的終點。

To interact effectively with your customers, you need to know them as real people. You would then be able to provide hyper-personalized experiences that evoke emotional responses.

為了與客戶進行有效互動，您需要了解他們是真實的人。 然後，您將能夠提供喚起情感React的超個性化體驗。

If you can show your audience that you understand well their reasons to interact with you, whether they are an early prospect or a loyal customer, the relationship and trust between you and your customers grow stronger.

如果您可以向受眾群體表明您很好地理解了與您互動的原因，無論他們是早期潛在客戶還是忠實的客戶，您與客戶之間的關係和信任就會增強。

市場細分 (Market Segmentation)

Customer segmentation is to divide your customers into segments. A customer from one segment is significantly different from a customer of another segment, based on the data used to carry out the segmentation process.

客戶細分是將客戶劃分為多個細分。 根據用於執行細分過程的資料，一個細分市場的客戶與另一細分市場的客戶存在顯著差異。

Unsupervised ML comes into play here. Clustering algorithms, such as K-Means, are part of unsupervised ML, used to discover hidden patterns within an unlabeled dataset and group data-points that are significantly similar.

無監督的ML在這裡起作用。 聚類演算法(例如K-Means)是無監督ML的一部分，用於發現未標記的資料集中的隱藏模式以及與資料點顯著相似的組。

The figure above visualizes a very simple example of cluster formation using ML. The dataset included two pieces of information (attributes) about each customer.

上圖顯示了使用ML形成簇的非常簡單的示例。 資料集包含有關每個客戶的兩條資訊(屬性)。

Based on the values of these attributes, each customer occupies their unique position within the two-dimensional representation above. It is two dimensional because there are two attributes.

基於這些屬性的值，每個客戶在上述二維表示中佔據其唯一位置。 這是二維的，因為有兩個屬性。

The algorithm’s task is to figure out how to group these data-points into a number explicitly provided by the users of the algorithm.

該演算法的任務是弄清楚如何將這些資料點分組為演算法使用者明確提供的數字。

The resulting groups, or clusters, could form the foundation of unbiased, truly data-driven personas. As you move forward and collect more data, rerunning the clustering algorithms may reveal new groups amongst your customers that emerge.

由此產生的組或群集可以構成無偏見，真正由資料驅動的角色的基礎。 隨著您前進並收集更多資料，重新執行群集演算法可能會在出現的客戶中揭示新的群體。

Therefore, you would be able to update your knowledge and refresh your messaging, creative, and other personalization efforts to always stay relevant as your customer base evolves.

因此，您將能夠更新您的知識並重新整理您的訊息傳遞，創意和其他個性化工作，從而始終隨著客戶群的發展而保持相關性。

Let us tell you an interesting example that would clear how clustering can allow customer-centricity in marketing. Burrow, a disruptive direct-to-consumer furniture brand, employs ML-driven personas to identify what color couches their audience segments see in targeted ads.

讓我們告訴您一個有趣的示例，該示例將闡明群集如何使營銷以客戶為中心。 Burrow是一傢俱有破壞性的直接面向消費者的傢俱品牌，它採用ML驅動的角色來識別其受眾群體在目標廣告中看到的顏色。

They discovered that older customers, living in single-family homes, with children were more inclined to buy dark-colored couches.

他們發現，住在獨戶住宅中且有孩子的年長顧客更傾向於購買深色沙發。

The younger customers, living in apartments, having few or no children were likely to buy light-colored couches.

居住在公寓中，幾乎沒有孩子或沒有孩子的年輕顧客很可能會購買淺色的沙發。

These insights allowed Burrow to push creative that reflected these attributes to the audiences that possessed them.

這些見解使Burrow能夠將反映這些屬性的創意推向擁有它們的受眾。

結論—使用機器學習可為公司節省數百萬美元 (Conclusion — Use Of Machine Learning is Saving Millions For Companies)

Although many complex AI applications would need more years or even decades to develop fully, the democratization of ML is allowing marketing teams to generate predictive customer insights with no need of spending millions on expensive consultants or hire large data science teams.

儘管許多複雜的AI應用程式需要花費甚至數年甚至數十年的時間才能完全開發，但ML的民主化使營銷團隊無需花費數百萬美元聘請昂貴的顧問或僱用大型資料科學團隊即可生成預測性客戶見解。

It is time for you to explore this option.

現在是您探索此選項的時候了。