A familiar challenge arises: You send out a direct mail campaign, relying on zip code-based targeting, hoping the campaign will hit the right customers. But as the results roll in and you’re not seeing the numbers you hoped for, you wonder if there’s a way to reach the customers who actually care about the offer. While a broad approach to sending out direct mail may occasionally work, it often leads to wasted marketing dollars by targeting people who have little to no interest in the product.
You’re not alone. Many marketers face the same frustration, using broad tactics that treat every potential customer the same. Tactics like narrowing the focus to specific carrier routes won’t fix this problem. Although you might cut costs, you’re still targeting people who don’t align with your brand’s offerings. What you really need is a way to find high-potential customers—those who don’t just buy once but become long-term customers.
Let’s explore how advanced targeting techniques, driven by data and machine learning, help you focus on high-potential customers who are more likely to engage, maximizing your marketing investment.
Targeting Prospects More Effectively with Direct Mail
A more effective way to reach high-potential customers through direct mail is by leveraging lookalike audiences. Unlike traditional methods that rely on basic demographic data, lookalike audiences allow for more precise targeting. They identify potential customers who share characteristics with your most valuable existing buyers.
Lookalike audience features can include demographics, geographic location, behavior, psychographics, and financial data. While many lookalike models use a limited data set, modern machine learning allows for much more sophisticated targeting by incorporating a wide array of features to create highly relevant prospect groups.
Enhancing Lookalike Audiences for Superior Results
Modern data capabilities and advanced algorithms now allow direct mail marketers to rank prospects by their likelihood to make a purchase, from most to least likely. Instead of hoping for the best, you can now focus on the prospects that really matter. Postie’s lookalike audience models are built specifically for this purpose, delivering precise targeting to boost your campaign effectiveness.
Postie’s data scientists use feature engineering and machine learning to identify prospects who mirror your most valuable customers. By combining first-party and third-party data from providers like Epsilon, Experian, and Acxiom into the Postie DMP, Postie creates detailed prospect lists that are fine-tuned using proprietary algorithms.
Building an effective lookalike audience relies on three essential components:
- First-party data from your existing customers
- Third-party data from trusted sources such as Epsilon, Experian, and Acxiom
- Postie’s DMP and advanced machine learning models, managed by expert data scientists to refine and rank prospects
Together, these components ensure your campaigns target the right people at the right time. Postie is one of the only direct mail marketing services that takes this approach.
Leveraging Your First-Party Data
First-party data is the cornerstone of creating highly effective lookalike audiences. This data, collected directly from your customers, offers the most accurate insight into their behaviors, preferences, and demographics. By analyzing this data—such as purchase history and engagement patterns—you can identify common traits among your most valuable customers, helping you better target similar prospects in future campaigns.
To fully optimize your first-party data, make sure your database is clean and regularly updated, integrates data from multiple touchpoints, and clearly defines audience segments with distinct characteristics.
Expanding Customer Insights with Third-Party Data
Third-party data allows you to expand beyond your current customer base, offering valuable insights into potential prospects. Postie accesses data from top data providers to gain a comprehensive view of U.S. households, leading to more personalized and effective direct mail campaigns.
- Acxiom: Uses binary feature data (yes/no questions) to categorize households, creating a foundational profile of potential customers.
- Epsilon: Offers more nuanced insights, considering complex features like income brackets and lifestyle categories, allowing for more tailored customer profiles.
- Experian: Focuses on geo-related data, providing location-based insights crucial for industries where proximity to services matters.
Postie combines these data sources with our DMP to create a holistic view of your ideal customer, helping your campaigns reach the right audience with greater accuracy.
Using Machine Learning to Uncover What Others Miss
Traditional filtering methods, which sort customers based on simple criteria like age or pet ownership, can be too rigid. They often fail to capture the full complexity of customer behavior and preferences, leaving out potential high-value customers who don’t fit neatly into predefined categories.
At Postie, we don’t just stop at basic filtering—we go deeper with machine learning to uncover hidden patterns and improve your targeting. Our approach includes:
- Feature identification: We use machine learning to dig up hidden connections in the data, revealing valuable insights—like predicting purchases before specific behaviors even occur.
- Data removal: We remove biased or insufficient data to ensure the results are accurate and trustworthy, so you’re not basing decisions on flawed information.
- Normalizing data: We standardize data across various sources, making comparisons reliable and consistent, which leads to more precise audience targeting.
Once the feature engineering process is complete, we apply customized modeling techniques tailored to meet your specific needs.
Harnessing the Power of Random Forest Algorithms
Random Forest algorithms are like a team of decision-makers, each using a different angle to figure out customer behavior. Think of it this way: each tree in the forest looks at different feature combinations—one might check out income and age, while another dives into credit score and recent search history.
Instead of relying on just one tree’s perspective, the Random Forest model gathers “votes” from all the trees to make a final prediction. This method detects complex patterns that a single decision path might miss, resulting in more accurate outcomes.
Random Forest algorithms are a trusted, simple approach for analysts, yet as data complexity grows, deep learning models often outperform them. Postie has embraced this change and is now using deep learning to improve our data-driven strategies and achieve superior results.
How Deep Learning Tackles Complex Data
Deciphering more complex datasets, especially those involving unstructured data like images or PDFs, requires turning to deep learning models. Deep learning uses layered neural networks to spot hidden patterns and relationships traditional methods might miss. Think of it as a Random Forest on steroids. Deep learning models have a different architecture capable of identifying subtle patterns that Random Forests often struggle to detect.
Traditional data methods might focus on basic demographics and purchase history. But with deep learning, we can dig deeper, finding insights that go beyond the obvious, revealing trends and behaviors that help you connect with your audience in a smarter way.
We analyze social media images to detect customer preferences, process chat logs to gauge sentiment, and examine browsing patterns to uncover subtle interests. Combining visual, textual, and behavioral data allows us to create hyper-personalized campaigns that resonate with each prospect’s unique preferences.
Model Calibration: Fine-Tuning for Optimal Performance
At Postie, we don’t settle for off-the-shelf solutions. Our in-house data science team builds custom algorithms specifically for direct mail marketing. This capability lets us work with raw data from different sources, giving us deeper insights into how each data provider affects prospect scoring.
Here’s how it works: one model might give a prospect a high score using Epsilon data, while another model using Acxiom data gives that same prospect a low score. If that prospect converts, we know the model that gave the high score spotted something important. We constantly analyze real-world conversion data to refine these models, making them even better at identifying top prospects for future campaigns.
Model calibration is key—it helps prevent what’s known as “model decay,” where performance drops off as you move down a ranked list of prospects. Instead of relying on just one model, we use a variety of them to reduce this drop-off. Each model brings its own unique perspective, helping top prospects stand out from different sources and keeping performance strong as you scale.
In short, we’re always optimizing, using different models to increase your chances of acquiring new customers and boosting conversions. Our lookalike audience models are dynamic, constantly adjusting to make sure your prospect lists stay sharp and effective.
How One Brand Achieved 408% Return on Ad Spend with Postie
A national apparel brand sought to scale customer acquisition, and Postie’s data science team stepped in with machine learning to make it happen. We built and tested multiple lookalike models based on the brand’s top customers, focusing on key attributes like lifetime value and average order value to create a hyper-targeted strategy.
Personalized mailings were sent to these lookalike audiences, and the results were impressive—return on ad spend (ROAS) peaked at 408%. The mailings also enabled the brand to achieve a final CPA of $198, beating its target CPA of $235—a testament to direct mail’s precision in acquiring high-value customers efficiently.
Reach the Right Prospects with Lookalike Audiences from Postie
Now that you’ve seen how Postie uses cutting-edge data science to create highly targeted lookalike audiences for direct mail, it’s time to put these insights to work for you.
Partnering with Postie means tapping into the full potential of your first-party data to create direct mail campaigns that are precise and cost-effective. With our advanced targeting and machine learning models, you’ll reach the right prospects and convert them into loyal, high-value customers—setting the stage for long-term growth and success.
If you’re ready to level up your lookalike audiences, start by integrating Postie’s deep-learning insights into your campaigns. Take the next step today and see how our expert strategies can elevate your targeting and deliver impactful results throughout the year.
