Direct Mail Marketing first-party data

Data Science Insights: Blending First- and Third-Party Data

9 Min Read
by Amanda Boughey

As third-party cookies start to crumble, digital marketers must rethink their data collection game plan. You and your colleagues might be panicking, but at Postie, we’re feeling goooood. We’re a marketing platform built on the power of first-party data. And when we do go beyond first-party data, we take a smart approach and blend first-party data with the vast, untapped potential of intelligent third-party data companies. This lets us unlock insights that enable more targeted marketing strategies. It’s our way of staying ahead as reliance on cookies diminishes, making sure our client’s campaigns keep up with changing consumer behaviors.

We want you to feel goooood, too, so we’re going to dive into some of our non-cookie data tactics. By the end of this article, you’ll feel like you’re at the beach on a sunny day in perfect weather. 🤞

The Look-a-Like Model: Leveraging First-Party Data for Targeting

The first thing that helps us sleep at night is the look-a-like Model, which pinpoints potential customers that resemble your company’s current audience. At its core, a look-a-like Model aims to find new prospects that look like a group of existing customers. The process works by taking a sample of a client’s first-party customer data gleaned from direct interactions, purchases, website visits, and more. 

Look-a-like models have become common in digital targeting. At Postie, look-a-like model magic happens when we feed all this data into a sophisticated machine learning model, merging the science of prediction and the art of marketing. Our model meticulously analyzes the characteristics, behaviors, and patterns present in the existing customer data. It doesn’t just look at surface-level similarities; it delves into the nuanced traits that define your loyal customers.

Once we complete this analysis, we use the model to cast a wider net across a broader population. The aim is to identify people who share these traits but haven’t interacted with your brand yet. Essentially, this process pinpoints valuable potential customers who are likely to connect with your brand or a client’s brand.

Unlike digital channels, our look-a-like model approach never needed cookies. We use your data and complement it with machine learning, so you’re not just shooting in the dark. Instead, you’re making informed, data-driven decisions to target individuals who are more likely to convert. This way, your marketing efforts are efficient, effective, and relevant to those you’re reaching out to.

Feature Engineering and Model Optimization Techniques

At Postie, we use feature engineering to shape your data to achieve more effective machine learning models. In data science, “features” refer to individual measurable attributes or characteristics within a dataset. Think of feature engineering as carefully picking out the essential components for a complex machine, where every part plays a crucial role in the system’s performance. Our aim is to boost the model’s precision by thoughtfully choosing and transforming the right data features. This detailed approach involves:

  • Identifying relevant features: We employ advanced algorithms, complemented by expert analysis, to pinpoint the features in your data that are most impactful. Our process focuses on attributes that significantly influence the predictions of our machine learning models. 
  • Removing biased or sparse features: Not all of these features are useful. Some may be biased or have insufficient data (sparse), which can negatively affect our model’s accuracy. By eliminating such features, we prevent our model’s output from being skewed, thereby ensuring the reliability of our predictions.
  • Transforming and normalizing data: We format the data to ensure it’s easily processed by machine learning models. Formatting involves normalizing numerical values to a standard range and encoding categorical data, which means converting categories or labels into a numerical format. Implementing this process allows the algorithms to interpret and learn from the data effectively.

Once we’ve fine-tuned your data through feature engineering, we apply various modeling techniques, each chosen to align perfectly with the specific needs of your data. The process starts with selecting the right algorithm to unlock the insights within your data.

One such method we use is the Random Forest algorithm. It’s a robust and versatile method particularly well-suited for handling large datasets with numerous features. The technique works by creating a “forest” of decision trees, each contributing its findings. By merging these outcomes, we achieve more accurate and stable predictions, vital for understanding complex customer behaviors.

For datasets that are more complex, especially those involving unstructured data like images or PDFs, we turn to Deep Learning models. Deep learning relies on layered neural networks, enabling them to uncover deep patterns and relationships hidden within the data.

We apply these techniques to make your marketing campaigns more personalized and effective, achieving a deeper connection with your audience. By deeply connecting with your audience, we ensure that your brand’s message resonates more strongly, leading to increased engagement and better overall results.

Understanding Model Decay and Continuous Optimization

Direct mail marketing isn’t just about launching campaigns; it’s about continually refining them. We proactively adjust our models and strategies to ensure optimal performance. This process is built around the concept of model decay.

Model Decay in Marketing

Model decay is an inherent challenge in predictive modeling. Over time, even the most successful models can start to lose their effectiveness. This happens when the original conditions for the model shift or the target audience changes. It’s a bit like a navigation system that doesn’t account for new roads or changes in traffic patterns – gradually, the directions become less reliable.

Postie recognizes and addresses model decay to maintain the effectiveness of a direct mail campaign. The point is to constantly monitor models for signs of decay. Once decay is identified, we proactively update and refine our models. This means your targeting remains sharp and your messages continue to resonate. 

Continuous Optimization

At Postie, we view models as evolving tools that require regular refinement. Additionally, we integrate a feedback loop, using insights from each campaign to enhance model accuracy, particularly in targeting responsive audience segments. 

Using first-party data, we’re able to anticipate model performance and adjust strategies based on transaction data. Our models constantly evolve to align with market shifts, whether in consumer behavior, trends, or competitive dynamics. 

Employing different models allows us to capture a wider market spectrum and adapt more fluidly. We address over-saturation by tracking and managing who receives our messages, avoiding repetitive targeting. The core of our optimization process lies in our machine learning capabilities, which analyze extensive data to identify and leverage patterns so our strategies remain relevant and effective.

Our continuous optimization and model decay capabilities help us maintain high performance standards and keep us at the forefront of direct mail marketing technology.

Postie’s Unique Approach to Data Partnerships

Postie’s innovative approach in the realm of direct mail marketing is significantly enhanced by its strategic partnerships with leading data providers: Acxiom, Epsilon, and Experian. Each of these collaborations brings a unique dimension to understanding customer demographics and behaviors for more personalized and effective campaigns.

  • Acxiom’s binary feature data: Acxiom specializes in providing binary feature data, a type of data that categorizes households based on whether they fit certain characteristics, essentially answering ‘yes’ or ‘no’ to specific features. By taking a binary approach, they’re able to form a foundational understanding of a household’s traits, aiding Postie in creating a basic profile of potential customers.
  • Epsilon’s varied feature data: Epsilon goes a step further by offering a more nuanced view of households. Their data includes varied features such as income brackets and lifestyle categories. The depth of this data allows for the creation of more complex customer profiles and supports the development of highly tailored marketing models.  
  • Experian’s geo-related features: Experian brings a geographical perspective to the table. Their strong geo-related features are indispensable for service industry clients, providing insights into location-based consumer behaviors and preferences. Their geographical data supports campaigns where location plays a key role in customer targeting and segmentation.

The synergy of these diverse data sources allows Postie to construct a comprehensive picture of the households in the U.S. With over a million different households and billions of data points to analyze, our ability to harness and interpret this data is unmatched. 

Case Study: 700% ROAS with Look-a-Like Audiences in Apparel Marketing

An apparel brand, renowned for its high-quality products and strong brand loyalty, faced challenges in acquiring new customers, especially at a national scale. Historically, their marketing efforts were concentrated on digital channels for retention, but they struggled to expand their customer base effectively.

Postie’s approach involved a two-fold strategy:

  • Audience identification: The brand provided Postie with a seed audience of their best customers, characterized by high lifetime value and average order value. This seed audience was crucial in identifying the qualities of their ideal customer.
  • Model development and testing: Using this seed audience, Postie built and tested multiple look-a-like models through our robust Data Management Platform (DMP) and machine learning capabilities. This process involved analyzing various customer attributes and behaviors to create a finely-tuned targeting strategy.

We executed this direct mail campaign by sending personalized materials to the identified look-a-like audiences. The selection of these audiences was based on the predictive scoring from the look-a-like models, ensuring that the mail reached individuals most likely to resonate with the brand.

The campaign results were nothing short of remarkable:

  • Peak Return on Ad Spend (ROAS): The campaign achieved a peak ROAS of 700%, indicating that for every dollar spent, the brand gained seven dollars in return.
  • Average ROAS: The average return on ad spend throughout the campaign was an impressive 400%, a testament to the campaign’s effectiveness and efficiency.
  • Scalability and optimization: The success of the initial campaign laid the groundwork for continued optimization and scalability, allowing the brand to reach a broader audience while maintaining high engagement and conversion rates.

The success story of the apparel company demonstrates the power of our data-driven direct mail campaigns. By tapping into look-a-like audiences, the brand significantly enhanced its marketing investments. The impressive ROI and scalability of the campaign underscore the effectiveness of blending traditional direct mail with contemporary data analytics and machine learning to achieve remarkable marketing results.

If your target marketing requires merging first-party data and third-party data, take a close look at what we have to offer. Discover why combining these data sources will give you the upper hand in a cookie-less environment. Start this essential journey with us and elevate your marketing strategies to new heights of precision and effectiveness.

Want to dive even deeper into first-party data? Listen to our on-demand webinar.

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