This article contains wording that
promotes the subject in a subjective manner without imparting real information. (February 2023) |
This section may contain information not
important or relevant to the article's subject. (February 2023) |
Data-driven marketing is a process used by marketers to gain insights and identify trends about consumers and how they behave — what they buy, the effectiveness of ads, and how they browse. Modern solutions rely on big data strategies and collect information about consumer interactions and engagements to generate predictions about future behaviors. This kind of analysis involves understanding the data that is already present, the data that can be acquired, and how to organize, analyze, and apply that data to better marketing efforts. The intended goal is generally to enhance and personalize the customer experience. The market research allows for a comprehensive study of preferences. [1]
Some marketing decisions have always been made on the basis of data, defined in the general sense as information. Audience targeting and segmentation strategies provide many examples. Since 1950, the Nielsen [2] ratings have provided information to media buyers about television program audiences. Business-to-business marketers often target advertising to specialized trade publications and their digital channels.
Data driven marketing in the contemporary sense can be traced back to the 1980s and the emergence of database marketing, which increased the ease of personalizing customer communications. [3] In 1993, WebTrends released one of the first web analytics products when only a few hundred websites existed. [4] In the twenty-first century, social media and mobile technology have contributed to an explosion in the amount of data and its availability. Today, marketers use tools such as:
The universe of data driven marketing is vast, but there are essentially two types of data used in marketing: contact information and performance metrics. [6] Capturing contact information allows marketers to track potential customers and target them through emails, paid social, other digital tactics, phone calls, or direct mail like catalogs. Tracking of performance metrics – such as engagement, clicks, and page views – enables marketers to improve and refine marketing activities to more effectively reach high-value prospects.
Analysis techniques for marketing can include:
Advanced marketing analytics uses complex models to provide intelligence such as:
E-commerce retailers use data driven marketing extensively to ensure the best customer experience and increase sales. One example cited in the Harvard Business Review is Vineyard Vines, a fashion brand with brick-and-mortar stores and an online product catalog. The company has used an artificial intelligence (AI) platform to gain insights about its customers from actions taken or not taken on the e-commerce site. Email or social media communications are automatically triggered at certain points, such as cart abandonment. Insights are also used to refine search engine marketing. [10]
In business-to-business marketing, where inbound leads must be captured and nurtured, tactics are more likely to be aimed at long-term retention of the prospect rather than urging them to buy. Content marketing is frequently used. Prospects may be offered a white paper or other high-value information resources in exchange for their email address. Marketing automation tools support continuing activity along the customer journey. [11]
This article contains wording that
promotes the subject in a subjective manner without imparting real information. (February 2023) |
This section may contain information not
important or relevant to the article's subject. (February 2023) |
Data-driven marketing is a process used by marketers to gain insights and identify trends about consumers and how they behave — what they buy, the effectiveness of ads, and how they browse. Modern solutions rely on big data strategies and collect information about consumer interactions and engagements to generate predictions about future behaviors. This kind of analysis involves understanding the data that is already present, the data that can be acquired, and how to organize, analyze, and apply that data to better marketing efforts. The intended goal is generally to enhance and personalize the customer experience. The market research allows for a comprehensive study of preferences. [1]
Some marketing decisions have always been made on the basis of data, defined in the general sense as information. Audience targeting and segmentation strategies provide many examples. Since 1950, the Nielsen [2] ratings have provided information to media buyers about television program audiences. Business-to-business marketers often target advertising to specialized trade publications and their digital channels.
Data driven marketing in the contemporary sense can be traced back to the 1980s and the emergence of database marketing, which increased the ease of personalizing customer communications. [3] In 1993, WebTrends released one of the first web analytics products when only a few hundred websites existed. [4] In the twenty-first century, social media and mobile technology have contributed to an explosion in the amount of data and its availability. Today, marketers use tools such as:
The universe of data driven marketing is vast, but there are essentially two types of data used in marketing: contact information and performance metrics. [6] Capturing contact information allows marketers to track potential customers and target them through emails, paid social, other digital tactics, phone calls, or direct mail like catalogs. Tracking of performance metrics – such as engagement, clicks, and page views – enables marketers to improve and refine marketing activities to more effectively reach high-value prospects.
Analysis techniques for marketing can include:
Advanced marketing analytics uses complex models to provide intelligence such as:
E-commerce retailers use data driven marketing extensively to ensure the best customer experience and increase sales. One example cited in the Harvard Business Review is Vineyard Vines, a fashion brand with brick-and-mortar stores and an online product catalog. The company has used an artificial intelligence (AI) platform to gain insights about its customers from actions taken or not taken on the e-commerce site. Email or social media communications are automatically triggered at certain points, such as cart abandonment. Insights are also used to refine search engine marketing. [10]
In business-to-business marketing, where inbound leads must be captured and nurtured, tactics are more likely to be aimed at long-term retention of the prospect rather than urging them to buy. Content marketing is frequently used. Prospects may be offered a white paper or other high-value information resources in exchange for their email address. Marketing automation tools support continuing activity along the customer journey. [11]