Transactional data is better for direct mail than compiled modeled data because it provides a more accurate, timely, and specific reflection of a person's actual purchasing behavior and intent. Compiled modeled data, by contrast, is an approximation based on public records and statistical assumptions, which can be broad, less reliable, and prone to inaccuracies. What is the difference between the two data types? Transactional data: This is a record of your customers' actual purchases and interactions. It includes information such as what they bought, how much they spent, when they bought it, and how often they buy. Compiled modeled data: This is a list built by data compilers who gather information from public records, surveys, and other sources to create profiles of potential customers. The data is then modeled to predict future behavior. Why transactional data is more effective Reflects actual behavior, not inferred intent: Transactional data is based on real actions. While compiled data uses algorithms to predict who might buy a product, transactional data shows who has actually purchased specific items. Enables superior personalization: By knowing a customer's purchase history, average order value, and buying frequency, you can craft personalized direct mail offers. For example, a retailer can send a coupon for a product a customer bought previously, or a special offer for a related item. Provides actionable, real-time insights: Transactional data, particularly when gathered through modern e-commerce and POS systems, is timely. You can send relevant direct mail communications to customers within days of their online engagement, capturing their interest at its peak. Increases customer lifetime value (CLV): Using transactional data to power personalized lifecycle marketing campaigns, from win-back campaigns to loyalty rewards, is more effective at driving repeat business and boosting a customer's total value
This list was curated by our group of data experts with various data backgrounds spanning over 35 years. Our experts begin by analyzing consumers who self-reported their intent to purchase a specific product, their purchasing history regarding specific products, and their preferred channel as compared to those that did not. The resulting algorithm is applied to a dataset of only known buyers and responders. These filters will always generate a higher-than-average response rate in any channel. The study data sets were augmented with demographic, lifestyle, interest, behavioral, financial, recency, frequency, monetary, and other transactional data, then modeled to create this responsive specific audience of intenders. There are over 3,000 data sources that contribute information to this unique solution.
