Unveiling the Power of Customer Analytics for Business Success
Martech Outlook | Thursday, July 18, 2024
Customer analytics involves analyzing data to understand consumer needs and preferences and guiding targeted marketing and sales efforts.
FREMONT, CA: Although customer analytics and analysis are frequently used interchangeably, the former has a broader definition. Customer analysis involves analyzing insights and choosing the best action. In contrast, customer analytics involves gathering data and applying analytics techniques.
Customer analytics software involves data collection, segmentation, behavior analytics, and relationship management. It includes CRM, e-commerce platforms, POS systems, social networking, and marketing tools for effective connections.
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Different types of customer data can be analyzed.
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Insights into customers' spending habits, payment preferences, and share of wallet are obtained through analyzing product/service purchases, returns, and reservations using data analysis.
Information on service/product use
Analyzing product/service utilization patterns helps businesses improve customer experience and enhance their product/service portfolio by analyzing log data, IoT data, and customer support requests.
Web usage information
Analyzing customer pages, scrolling, and clicks helps identify critical points in a customer journey, assess page performance, and tailor content accordingly. This helps identify stuck issues, leave/convert points, and optimize content for a better customer experience.
Data from texts sent by customers
Analyzing customer reviews and social media posts helps identify preferences, complaints, and problems, enabling businesses to understand customer attitudes toward products and brands.
Tips for Analyzing Customer Data
Define the objective of customer data analysis to segment customers, understand preferences, and predict behavior. This helps determine the data to collect and analytics techniques to apply. Collect relevant data from internal and external sources, ensuring smooth integration and relevance. Establish data quality management to deal with incomplete or duplicated data, laying the foundation for accurate insights. Analyze data using appropriate analytics techniques and easy-to-navigate dashboards, drawing conclusions on dependencies and patterns. Monitor and refine customer behavior trends to spot changes and adjust marketing and sales strategies, as well as the applied analytics techniques.
Different Kinds of Analytics for Customer Data
Descriptive customer analytics
It involves analyzing historical and real-time customer-related data to identify business trends and issues. For example, trends like a decrease in sales or an increase in customer attrition rates can be revealed.
Diagnostic customer analytics
It identifies the root causes of business problems using data discovery and mining techniques.
Predictive customer analytics
This technology utilizes data mining and machine learning to generate predictions and forecasts by analyzing both historical and real-time data.
Prescriptive customer analytics
Suggests the best action based on predictive analytics estimates of future events, such as offering a 5% coupon to a specific customer segment for maximum revenue increase.
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