Cut Mailing Costs by 30%
Without Losing Performance
A regional performing arts organization replaced broad saturation mailings with a machine learning-driven targeting strategy, eliminating 30% of their mailing volume while matching or exceeding prior campaign results.
Mailing Volume
and Cost
or Better
Prior Full-Saturation
Mailing Efforts
High-Propensity
Households Only
Broad Reach.
Wasted Budget.
A regional performing arts organization had long relied on saturation direct mail to fill seats. While the reach was wide, the efficiency was not. Rising costs and limited audience insight forced a rethink of their entire acquisition strategy.
The Client
A regional performing arts organization running ongoing direct mail campaigns to drive ticket sales. Their audience is local and geographically defined, making targeted direct mail a core acquisition channel.
The Challenge
Rising direct mail costs with limited visibility into which households were most likely to respond. Traditional saturation mailings were broad and expensive, and the organization feared that reducing volume would lead to a drop in ticket sales.
The Solution
DS Graphics Universal Wilde partnered with the organization to apply machine learning to their customer data. Rather than mailing everyone, the team built a lookalike model to score every household by likelihood to convert, then removed low-propensity addresses from the list entirely.
The Approach
Historical ticket-buyer data was used to train a model that identified behavioral and demographic patterns among existing customers. Each household in the target region received a likelihood-to-convert score. Only high-propensity households made the final mailing list.
A Three-Step
Machine Learning Strategy
The approach replaced guesswork with a repeatable, data-driven system for identifying which households deserved a spot on the mailing list.
Build the Lookalike Model
Using historical ticket-buyer data, a machine learning algorithm identified patterns and behaviors common among existing customers. The model learned what a high-value audience member actually looks like at a household level.
Score and Suppress
The model evaluated every household in the target region, assigning a likelihood-to-convert score based on how closely each matched known customer profiles. Households below the conversion threshold were removed from the list entirely.
- 2Score every householdPropensity score assigned based on fit with the customer profile model.
- 3Eliminate low-propensity addressesBudget focused only on households with real conversion potential.
Data Over Assumptions
No guesswork. No blanket coverage. Every decision was backed by real customer data, ensuring that every dollar in the mailing budget was directed at a household that had demonstrated meaningful similarity to an existing ticket buyer.
Smarter Segments, Better Results
Resources previously wasted on low-propensity households were reinvested into stronger audience segments. The result was a leaner, more efficient campaign that proved volume and performance are not the same thing.
Less Volume,
Same Impact
30% of the mailing list was removed after being identified as low-propensity, cutting print and postage costs by 30% without touching the high-value segment.
After suppression, every dollar went to households most likely to engage, increasing overall marketing efficiency with the same creative and offer.
Despite mailing 30% fewer pieces, campaign results matched or exceeded prior saturation efforts, confirming that the removed households were genuinely low-value.
Freed-up budget can be reinvested into additional high-propensity outreach, creative upgrades, or complementary digital channels to further amplify results.
The Impact,
By the Numbers
Mailing Volume and Cost
High-Propensity Households
or Better
Prior Saturation Efforts
Trained on Historical Buyers
Remaining on Final List
vs. Saturation Baseline
Why Machine Learning
Changes the Math
Volume Is Not Value
Saturation mailing assumes more reach equals more response. The shift this organization made was fundamental: by treating every household as a data point, they discovered that 30% of their list was simply not worth reaching. Not bad addresses — just households whose behavioral and demographic signals indicated low conversion potential.
How the Lookalike Model Works
The model was trained on historical ticket-buyer data, identifying signals common among households that had actually converted. Those patterns were applied to every household in the target region. Rather than guessing who might be interested, the model identified who looks like someone who already was.
The Performance Paradox
Mailing fewer people did not reduce results. Campaign performance matched or exceeded prior saturation efforts, validating the core hypothesis: the removed households were not contributing to outcomes. When you eliminate the noise, the signal gets stronger — and the remaining households are more likely to respond.
A Self-Improving System
Unlike a one-time list cleanse, this model improves with each campaign. New response data retrains the model, making future scoring more accurate. Historical data becomes a compounding competitive advantage rather than a static reference point.
Performance Snapshot
What Was Eliminated
Smarter Segments.
Better Outcomes.
A 30% reduction in mailing volume with no loss in performance. Machine learning did not shrink this campaign. It sharpened it.
The households this organization stopped mailing were never going to convert. The ones they kept were exactly who they needed. Machine learning did not shrink this campaign — it sharpened it, delivering equal results at 30% lower cost with every dollar focused on buyers who matched the profile of someone who had already said yes.
For any organization relying on direct mail, this is the playbook: stop mailing everyone. Start mailing the right people.