預測性模型並影響客戶行為Predictive modeling and influencing customer behavior

數位經濟中有兩種類別的應用程式:歷程 記錄預測 性。There are two classes of applications in the digital economy: historical and predictive. 您可以使用歷程記錄資料(包括近乎即時的資料),來單獨滿足許多客戶需求。Many customer needs can be met solely by using historical data, including nearly real-time data. 大部分的解決方案主要著重于匯總資料。Most solutions focus primarily on aggregating data in the moment. 然後,他們會以數位或環境體驗的形式處理資料,並將其與客戶共用。They then process and share that data back to the customer in the form of a digital or ambient experience.

相較于歷程模型,則是預測模型。In contrast to historical modeling is predictive modeling. 但是,什麼是預測模型?But, what is predictive modeling? 預測模型會使用統計資料和已知結果來處理和建立模型,以用來預測未來的結果(在原因內)。Predictive modeling uses statistics and known results to process and create models that can be used to predict future outcomes, within reason. 當預測性模型變得更符合成本效益且可供使用時,客戶會要求向前思考體驗,以提供更好的決策和動作。As predictive modeling becomes more cost-effective and readily available, customers demand forward-thinking experiences that lead to better decisions and actions. 不過,該需求不一定會建議預測性解決方案。However, that demand doesn't always suggest a predictive solution. 在大部分的情況下,歷程記錄視圖可以提供足夠的資料,讓客戶自行決定。In most cases, a historical view can provide enough data to empower the customer to make a decision on their own.

可惜的是,客戶通常會採用 myopic 的觀點,根據其立即的環境和影響因素,來導向決策。Unfortunately, customers often take a myopic view that leads to decisions based on their immediate surroundings and sphere of influence. 當選項和決策隨著數量和影響而成長時,myopic view 可能無法滿足客戶的需求。As options and decisions grow in number and impact, that myopic view may not serve the customer's needs. 同樣地,假設是以大規模的方式證明,提供解決方案的公司可以跨數千或數百萬個客戶決策來查看。At the same time, as a hypothesis is proven at scale, the company providing the solution can see across thousands or millions of customer decisions. 這個大圖片方法可以看到廣泛的模式,以及這些模式的影響。This big-picture approach makes it possible to see broad patterns and the impacts of those patterns. 預測性模型化功能是一種明智的投資,因為必須瞭解這些模式,才能做出最適合客戶的決策。Predictive modeling capability is a wise investment when an understanding of those patterns is necessary to make decisions that best serve the customer.

預測模型的範例,以及它如何影響客戶行為Examples of predictive modeling and how it influences customer behavior

不同類型的應用程式和環境體驗都會使用資料來進行預測:Different kinds of applications and ambient experiences use data to make predictions:

  • 電子商務: 電子商務網站會根據其他類似的取用者購買的產品,建議可能會新增至購物車的產品。E-commerce: Based on what other similar consumers have purchased, an e-commerce website suggests products that may be worth adding to your cart.
  • 調整的現實: IoT 提供更先進的預測功能實例。Adjusted reality: IoT offers more advanced instances of predictive functionality. 例如,假設某個裝置上的裝置會偵測到機器的溫度上升。For example, suppose a device on an assembly line detects a rise in a machine's temperature. 以雲端為基礎的預測模型會決定如何回應。A cloud-based predictive model determines how to respond. 根據該預測,其他裝置會使元件行變慢,直到電腦很酷。Based on that prediction, another device slows down the assembly line until the machine can cool.
  • 消費者產品: 行動電話、智慧型家裡、甚至是您的汽車,全都使用預測性功能,它們會根據類似地點或時間的因素,進行分析以建議使用者行為。Consumer products: Cell phones, smart homes, even your car, all use predictive capabilities, which they analyze to suggest user behavior based on factors like location or time of day. 當預測和初始假設對齊時,預測會導致動作。When a prediction and the initial hypothesis are aligned, the prediction leads to action. 在非常成熟的階段中,這種調整可能會使像是自我駕駛汽車的產品成為現實。At a very mature stage, this alignment can make products like a self-driving car a reality.

開發預測功能Develop predictive capabilities

持續提供精確預測功能的解決方案通常包含五個核心特性。Solutions that consistently provide accurate predictive capabilities commonly include five core characteristics. 五個核心預測模型特性為:The five core predictive modeling characteristics are:

  • 資料Data
  • 深入解析Insights
  • 模式Patterns
  • 預測Predictions
  • 互動Interactions

開發預測功能需要每個層面。Each aspect is required to develop predictive capabilities. 就像所有絕佳的創新一樣,預測功能的開發也需要反復專案的 承諾Like all great innovations, the development of predictive capabilities requires a commitment to iteration. 在每個反復專案中,有一或多個下列特性經過成熟,以驗證日益複雜的客戶假設。In each iteration, one or more of the following characteristics is matured to validate increasingly complex customer hypotheses.

預測功能的步驟

警告

如果客戶假設是在 組建中以客戶理解的方式 納入預測功能,則可能會有同樣適用的原則。If the customer hypothesis developed in Build with customer empathy includes predictive capabilities, the principles described there might well apply. 不過,預測功能需要大量的時間和能源投資。However, predictive capabilities require significant investment of time and energy. 當預測功能是 技術尖峰時(相對於真實客戶價值的來源),建議您延遲預測,直到客戶假設經過大規模驗證為止。When predictive capabilities are technical spikes, as opposed to a source of real customer value, we suggest that you delay predictions until the customer hypotheses have been validated at scale.

資料Data

資料是先前所述特性的最 elemental。Data is the most elemental of the characteristics mentioned earlier. 開發數位發明的每個專業領域都會產生資料。Each of the disciplines for developing digital inventions generates data. 當然,這項資料會參與預測的開發。That data, of course, contributes to the development of predictions. 如需如何將資料帶入預測性解決方案的詳細資訊,請參閱 使用數位發明將大眾化資料與裝置互動For more information on ways to get data into a predictive solution, see Democratize data with digital invention and Interact with devices.

您可以使用各種資料來源來提供預測性模型化功能。A variety of data sources can be used to deliver predictive modeling capabilities.

深入解析Insights

主題專家會使用客戶需求和行為的相關資料,從原始資料研究中開發基本的商業見解。Subject matter experts use data about customer needs and behaviors to develop basic business insights from a study of raw data. 這些深入解析可以找出所需的客戶行為 (或不想要的結果) 。Those insights can pinpoint occurrences of the desired customer behaviors (or, alternatively, undesirable results). 在預測的反復專案期間,這些見解有助於找出可能最終產生正面結果的潛在相互關聯。During iterations on the predictions, these insights can aid in identifying potential correlations that could ultimately generate positive outcomes. 如需啟用主題專家以開發見解的指導方針,請參閱 使用數位發明將大眾化資料For guidance on enabling subject matter experts to develop insights, see Democratize data with digital invention.

模式Patterns

人們一律嘗試偵測大量資料中的模式。People have always tried to detect patterns in large volumes of data. 電腦是針對該目的所設計。Computers were designed for that purpose. 機器學習藉由偵測這類模式(包含機器學習模型的技能)來加速該進行。Machine learning accelerates that quest by detecting precisely such patterns, a skill that comprises the machine learning model. 這些模式接著會透過機器學習演算法套用,以在演算法中輸入新的資料集時預測結果。Those patterns are then applied through machine learning algorithms to predict outcomes when a new set of data is entered into the algorithms.

使用深入解析作為起點,機器學習會開發並套用預測模型,以將資料中的模式變成大寫。Using insights as a starting point, machine learning develops and applies predictive models to capitalize on the patterns in data. 透過多個定型、測試和採用反復專案,這些模型和演算法可以精確地預測未來的結果。Through multiple iterations of training, testing, and adoption, those models and algorithms can accurately predict future outcomes.

Azure Machine Learning 是 Azure 中的雲端原生服務,可根據您的資料來建立和定型模型。Azure Machine Learning is the cloud-native service in Azure for building and training models based on your data. 這項工具也包含 加速開發機器學習演算法的工作流程This tool also includes a workflow for accelerating the development of machine learning algorithms. 此工作流程可透過視覺化介面或 Python 來開發演算法。This workflow can be used to develop algorithms through a visual interface or Python.

如需更健全的機器學習模型, Azure HDInsight 中的 ML 服務 提供建置於 Apache Hadoop 叢集上的機器學習平臺。For more robust machine learning models, ML Services in Azure HDInsight provides a machine learning platform built on Apache Hadoop clusters. 這種方法可讓您更精細地控制基礎叢集、儲存體和計算節點。This approach enables more granular control of the underlying clusters, storage, and compute nodes. Azure HDInsight 也透過 ScaleR 和 SparkR 等工具提供更先進的整合,以根據整合和內嵌資料來建立預測,甚至是使用資料流程中的資料。Azure HDInsight also offers more advanced integration through tools like ScaleR and SparkR to create predictions based on integrated and ingested data, even working with data from a stream. 飛行延遲預測解決方案會在用來根據天氣狀況預測航班延遲時,示範每一個先進的功能。The flight delay prediction solution demonstrates each of these advanced capabilities when used to predict flight delays based on weather conditions. HDInsight 解決方案也允許企業控制項(例如資料安全性、網路存取和效能監視)讓模式。The HDInsight solution also allows for enterprise controls, such as data security, network access, and performance monitoring to operationalize patterns.

預測Predictions

建立並定型模式之後,您可以透過 Api 加以套用,這可以在傳遞數位體驗期間進行預測。After a pattern is built and trained, you can apply it through APIs, which can make predictions during the delivery of a digital experience. 這些 Api 大多是根據您資料中的模式,從經過妥善定型的模型來建立。Most of these APIs are built from a well-trained model based on a pattern in your data. 當更多客戶將日常工作負載部署到雲端時,雲端提供者所使用的預測 Api 會導致更快速的採用。As more customers deploy everyday workloads to the cloud, the prediction APIs used by cloud providers lead to ever-faster adoption.

Azure 認知服務 是雲端廠商所建立的預測 API 範例。Azure Cognitive Services is an example of a predictive API built by a cloud vendor. 此服務包含內容仲裁的預測性 Api、異常偵測,以及個人化內容的建議。This service includes predictive APIs for content moderation, anomaly detection, and suggestions to personalize content. 這些 Api 已備妥可供使用,並以 Microsoft 用來定型模型的知名內容模式為基礎。These APIs are ready to use and are based on well-known content patterns, which Microsoft has used to train models. 這些 Api 都會根據您送入 API 的資料進行預測。Each of those APIs makes predictions based on the data you feed into the API.

Azure Machine Learning 可讓您部署自訂建立的演算法,而您只需根據自己的資料來建立和定型。Azure Machine Learning lets you deploy custom-built algorithms, which you can create and train based solely on your own data. 如需有關使用 Azure Machine Learning 部署預測的詳細資訊,請參閱 將機器學習模型部署到 AzureFor information about deploying predictions with Azure Machine Learning, see Deploy machine learning models to Azure.

如需公開針對 Azure HDInsight 上的 ML 服務所開發之預測之程式的相關資訊,請參閱 設定 HDInsight叢集。For information about the processes for exposing predictions developed for ML Services on Azure HDInsight, see Set up HDInsight clusters.

互動Interactions

透過 API 提供預測之後,您就可以使用它來影響客戶行為。After a prediction is made available through an API, you can use it to influence customer behavior. 這項影響會採用互動的形式。That influence takes the form of interactions. 與機器學習演算法的互動會發生在您的其他數位或環境體驗內。An interaction with a machine learning algorithm happens within your other digital or ambient experiences. 透過應用程式或體驗收集資料時,它會透過機器學習演算法來執行。As data is collected through the application or experience, it's run through the machine learning algorithms. 當演算法預測結果時,該預測可透過現有的體驗與客戶共用。When the algorithm predicts an outcome, that prediction can be shared back with the customer through the existing experience.

深入瞭解如何透過 調整的現實解決方案來建立環境體驗。Learn more about how to create an ambient experience through an adjusted reality solution.

下一步Next steps

熟悉發明和創新方法專業領域,您現在已準備好瞭解如何以客戶理解的方式打造。Having acquainted yourself with disciplines of invention and the Innovate methodology, you're now ready to learn how to build with customer empathy.