Leveraging Customer Data
Do you know who your customers are, what they want, when they’re likely to buy, and why they make their purchasing decisions? Many companies invest heavily in data-driven campaigns to answer these questions, but achieving real results can often be elusive.
Customer data is a highly valued asset. However, many databases are outdated or incomplete, leading to error-prone targeting that misses the mark with the wrong audiences at the wrong times.
To better understand and predict customer behavior, many marketers use pre-developed audience propensities. This targeting technology has been available for decades but is now gaining new importance due to data privacy regulations restricting personally identifiable tracking.
Opt-In Compliant Data for Accurate Audience Building
Our research firm collects "opt-in compliant" data from brand and product buyers who voluntarily share their information. With sufficient data, such as from Southwest Airlines frequent flyers, we can create propensity scores across the US population.
AI-Driven Opt-In Propensity Modeling Explained
Propensity modeling correlates customer characteristics with anticipated behaviors or propensities. These models start at the product category level and go down to brand usage details. Modeled prospects are look-alikes of recent buyers, likely to make future purchases with high probability. These data points can be overlaid to create multiple filtered audiences, such as frequent Southwest Airlines flyers who also stay at Hilton hotels.
Testing and Optimizing Campaigns with Propensity Audiences
By applying propensity audiences as new test segments, you can evaluate current campaign performance with likely responders without using customer CRM data. After establishing a test benchmark, we measure response rates from digital or direct campaigns. This helps refine predictions and incrementally increase ROI with our automated AI-driven scoring.
Any objective can be optimized, including response, conversion, revenue, cross-sell opportunities, or reducing churn and cancellations. This strategic data-driven process builds better customer relationships through effective selling techniques and personalization.
Segmenting Your Customers for Better Results
Using high-value customer data to build segmentation models offers advantages over propensity models by incorporating recent behaviors. Not all data is valuable; therefore, we test and purge unprofitable data sources to optimize the best sources and modeling techniques.
The Role of Technology and AI in Marketing
Technology, especially AI, is proving its value in strategic data-driven marketing. By leveraging audience intelligence, you can deliver highly targeted and personalized messages, enhancing customer retention and acquisition strategies. This approach saves time and money, ultimately driving faster and greater returns on investment.
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