{"id":866571,"date":"2022-08-04T08:57:12","date_gmt":"2022-08-04T15:57:12","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-blog-post&p=866571"},"modified":"2022-08-04T08:57:58","modified_gmt":"2022-08-04T15:57:58","slug":"customerlifetimevaluepredictivemodel","status":"publish","type":"msr-blog-post","link":"https:\/\/www.microsoft.com\/en-us\/research\/articles\/customerlifetimevaluepredictivemodel\/","title":{"rendered":"Customer Lifetime Value Predictive Model now uses Customer Profile Attributes"},"content":{"rendered":"\n
Authors: <\/strong> Kidus Asfaw, Sally Kellaway,<\/a> and Radha Sayyaparaju<\/p>\n\n\n\n When crafting your marketing and sales strategies, personalizing your marketing campaigns, journeys and content to the special traits of your customer segments is an important way to drive engagement and purchases. One method for segmenting your audiences is to determine the potential value of those customers to isolate your high and low predicted value customers and personalize campaigns to reward high value customers and drive up the value of lower value customers. Artificial intelligence (AI) can be used to predict the value of customers to create segments like these.<\/p>\n\n\n\n As with all AI, the more data an AI model has, the more accurate its predictions will be. The type of data that\u2019s provided to an AI model can influence the accuracy of those predictions \u2013 e.g., transaction data can help predict transaction value in the future, which can be made more accurate by adding customer data. The more customer data you provide to a model, the more accurate and targeted the segments of \u201chigh\u201d and \u201clow\u201d value customers will be. As a result, the personalization of marketing campaigns driven with those segments can be more targeted and \u2018speak\u2019 more directly to the individual traits of your customers \u2013 driving engagement and sales!<\/p>\n\n\n\n Dynamics 365 Customer Insights is introducing a new feature for the Customer Lifetime Value (CLV) out-of-box predictive model that allows you to optionally select customer profile attributes to include in the prediction. You can select from 18 commonly used attributes (in any combination) to include as an input to the model. These attributes will drive more personalized, relevant, and actionable model results for your business use cases. This blog post will share what customer profile attributes are, how they\u2019re used in the CLV model, and what\u2019s new in the overall experience of using the model.<\/p>\n\n\n\n The customer lifetime value model predicts the potential value (revenue) that individual active customers will bring to your business in a future period of time that you define. This model can help you achieve various goals:<\/p>\n\n\n\n The fields listed in a customer profile (or any CDM -defined entity) are called attributes<\/em> (read more about the CDM schema here: Common Data Model – Common Data Model | Microsoft Docs (opens in new tab)<\/span><\/a>). Customer Insights utilizes both standard entity definitions and its own defined entities (opens in new tab)<\/span><\/a>, including the CustomerProfile entity (opens in new tab)<\/span><\/a>. This entity includes dozens of attributes that define the qualities of that customer. An example customer attribute is a customer\u2019s birth date, first name, last name or gamertag.<\/p>\n\n\n\n These customer attributes can be used as extra sources of information for personalizing customer lifetime value predictions. Personalization of these predictions helps to \u201ccustomize\u201d to your use case without requiring the development of fully custom AI models.<\/p>\n\n\n\n The profile attributes that you select during configuration are added to the model and considered alongside factors added (and featurized) from the Required Data that you added. The model develops its predictions of lifetime value and will show all factors that influenced those predictions on the results page.<\/p>\n\n\n\n As illustrated in the figure below, the CLV model takes transaction activities data and featurizes it into a transactions table with one row per customer. Each row will have features such as average transaction value for the customer. Similarly, CLV uses other activities (in the figure below, retail product web reviews are used as an example) to augment the transactions table with additional features. We call the resulting table a featurized activities table. The new profile attributes feature allows the user to add customer attributes as an additional feature to the featurized activities table.<\/p>\n\n\n\nWhat is the CLV model?<\/h4>\n\n\n\n
What are customer profile attributes?<\/h4>\n\n\n\n
What is the model doing with these attributes?<\/h4>\n\n\n\n