Smart Targeting machine learning

Limitations of Traditional Statistical Methods in Direct Mail Marketing

4 Min Read
by Amanda Boughey

Smart marketers have been utilizing data and statistical methods to help with targeted marketing campaigns for decades. In the realm of direct mail marketing campaigns, traditional statistical methods have long held sway, often relying on techniques like regression analysis. Yet, it’s important to recognize that these techniques were conceived in an era of limited data and have their limitations when it comes to tackling the complexities of modern data sets.

Unlike their modern counterparts, traditional statistical methods were born in an era before computers, where data was scarce and datasets were small, often comprising only a few hundred to a thousand data samples. The primary objective of these methods was to estimate the relevance of individual data attributes, rather than identifying patterns among them for predictive targeting. The focus was more on A/B testing rather than precise targeting.

However, when we delve into the output of these methods, their shortcomings become evident. They operate on a point-based system, where various attributes are assigned specific point values that are summed up to rank prospective households. While this approach might seem intuitive at first, it struggles when faced with datasets that involve multiple attributes and complex relationships. These traditional methods operate within a singular equation and are non-robust systems, meaning that a single miscalculation can lead to a cascade of errors and compromises.

The limitations of traditional statistical methods become even more glaring when compared to contemporary machine learning techniques. Modern machine learning is tailor-made for predictive tasks, taking full advantage of the vast volumes of data available today. Unlike traditional methods, which struggle with multiple attributes, modern machine learning algorithms can handle thousands of data attributes, and their intricate pattern recognition capabilities are far superior.

Our Head of Data Science, Dr. Nicholas Tyris, often puts this into perspective for me by having me consider a targeting algorithm like the random forest, which can identify over 800,000 patterns among data attributes. This is a level of complexity that traditional methods can’t even dream of achieving. However, embracing this advanced technology isn’t just about the numbers; it’s about leveraging the power of these patterns to enhance targeting precision and campaign success.

Machine Learning in Action: A Use Case

Let’s take a real-world example to illustrate the potential of contemporary machine learning in direct mail marketing. A prominent outdoor brand seeking to refine their targeting efforts aimed to pinpoint strong prospects for specific products while focusing on e-commerce and incremental performance. This level of complexity was beyond the capabilities of traditional methods.

Utilizing lookalike modeling powered by contemporary machine learning, the brand executed a series of campaigns leading up to their busy season. The strategy was tested and refined iteratively, culminating in a net new customer campaign. The results were astounding—up to 600% Return on Ad Spend (ROAS) and a 30% lift in incremental performance. These impressive numbers are a testament to the power of modern machine learning in delivering exceptional campaign outcomes.

However, achieving this level of success wasn’t solely attributed to advanced algorithms. The brand’s collaboration with Postie’s data scientists (who, might I add – are simply amazing 🤩) and their willingness to integrate domain knowledge were crucial. This partnership facilitated the fine-tuning of strategies and allowed for data-driven decision-making during critical campaign periods.

In a field where success is measured by constant improvement, the key takeaway from such a remarkable campaign is the importance of continuous learning and optimization. Us marketers must view each campaign as an opportunity to gather insights, refine strategies, and enhance targeting precision. By embracing the capabilities of contemporary machine learning, direct mail marketing campaigns can tap into a new realm of possibilities, unlocking patterns and insights that were once unimaginable.

The Era of Relying Solely on Traditional Statistical Methods in Direct Mail Marketing is Fading

The advent of contemporary machine learning techniques has ushered in a new era of predictive targeting, enabling marketers to unlock the power of intricate patterns within their data. As the digital landscape evolves and data availability continues to surge, the marriage of domain knowledge and machine learning holds the key to crafting campaigns that not only meet but exceed expectations.

What kind of statistical methods are you utilizing? Do you know? Hop on a call with an expert from Postie to make sure you’re using modern machine learning today!

See What Postie Can Do for You!
Book a Demo