Right-size your direct mail budget — model your Customer Acquisition Cost and ROI before you…
Staying Ahead of the DM Data Curve
Since its inception in the 1980s, data modeling has profoundly impacted direct mail marketing to accomplish various marketing objectives across diﬀerent channels. The goal is always the same — generate incremental business by ﬁnding new people who are most like those currently exhibiting proﬁtable behavior. When it comes to prospecting, that is ﬁnding lookalike prospects who most closely resemble existing customers.
At its start, modeling focused on creating customer data proﬁles and segmentation analyses based on demographic information.
The combination of growing modeling expertise increased computing capacity, and the availability of enhanced data sets improved modeling eﬀectiveness incrementally over the course of its history. But in the last ten years, we have witnessed exponential changes in data and analytics to enable increasingly eﬀective model building.
e-Commerce, Social Media, and the Deluge of Data
The advent of digital commerce led to the development of web analytics tools designed to gather all data related to website visits and online consumer behaviors. This enabled modeling to focus on prospects’ actions, such as the customer journey. In addition, social media started generating massive amounts of data, and the challenges of dealing with “big data” are legendary. Issues like storage capacity, processing speed, and data analysis led analytics into a new era of more advanced techniques, such as machine learning and text analytics.
Starting in the mid-2010s, marketers began combining online and oﬄine data to complete a 360-degree view of the consumer. Data models focused on collecting all data coming through from multiple systems like point of sale, CRM, social media, and website interactions. These data challenges have eased with Artiﬁcial Intelligence (AI) and Machine Learning (ML) breakthroughs to enable data analytics automation to uncover patterns and help marketers make real-time predictions.
Modeling exercises once took weeks and cost thousands of dollars. With the implementation of AI and ML, the turnaround time has dramatically reduced — in certain cases, data providers will waive costs associated with building a model in lieu of data usage commitments.
“Instead of taking weeks to generate a model, now we can generate multiple models for testing in about ten working days.”
So, how does this beneﬁt marketers? These days, we can generate multiple models based on various input assumptions and back-test them against a customer database to predict success — all in about a couple of weeks. Whereas in the past we would be excited to evaluate a single model in a mailing, we will often evaluate multiple models from diﬀerent data sources simultaneously. This helps us speed up years of learnings down to just months.
Gunderson Direct has long-lasting relationships with some of the country’s largest corporations, helping them to lower their customer acquisition costs and increase proﬁts using address-based integrated direct marketing programs.
Drop us a line for more information on how our direct marketing expertise can help your business.
If you found this information valuable, sign up for our newsletter.