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AI: Driving Customer Segmentation and ROI
Artificial intelligence can play a crucial role in the segmentation of customer’s data due to its ability to access and analyze massive data sets.
FREMONT, CA: Precision plays a vital role in a company when it comes to leveraging their databases for the targeted audience. Businesses adopt various means to get an edge on this aspect. They understand that targeting the right audience for a campaign can not only enhance the chance of conversions but also save their valuable time. Thus segmentation processes are highly focused upon by the teams.
Segmentation is often based on location, age, first time, or repeat customers and other such criteria. However, the level of segmentation is limited by the available customer insights as well as the tools required to derive value from them. Artificial intelligence can play a crucial role in the segmentation of customer’s data based on several conditions as per the requirement.
AI assists in the collection and analysis of massive data sets to generate detailed, targeted segments while automating the process of personalizing campaigns for various sections. AI deployment will also yield better results as compared to the conventional ones because AI-based solutions offer numerous advantages that humans can’t match, such as elimination of stereotyping and human bias. AI algorithms can also identify hidden trends and patterns that are often inaccessible via conventional techniques. Personalization is a highly efficient practice used by companies that allow customers with augmented personal experience and services. With AI solutions, personalization can be applied on a grander scale, thereby paving the way for a larger audience base. Thus AI-driven segmentation yield better returns on investment (ROI) and enhance the possibility the conversions in general.
Segmentation results in better conversions as it allows targeting smaller groups with specific contents. However, with an increase in the number of segments, the task of marketing them also gets more complicated. Such complexities can be managed with the help of machine learning (ML) technology that tweaks and adjusts the variables specific according to the targeted audience. The variables can have factors like colors, images, delivery time, and the subject line. Marketers are required to select some of the variables, and ML can automate the process ahead, trying various combinations of these variables.