Retention Marketing and Maximizing Customer Potential With Aaron Schwartz
🎯 Summary
Technology Professional Summary: Digital Deep Dive on Data-Driven Customer Retention
This episode of the Digital Deep Dive podcast, hosted by Aaron Cohnet, features Aaron Schwartz, co-founder of Oreda.ai, for an in-depth discussion on maximizing customer retention marketing amidst rising acquisition costs. The core narrative centers on the industry’s pivot from brute-force communication tactics to sophisticated, data-driven personalization, particularly in email marketing.
Key Takeaways for Technology Professionals:
1. The Pitfall of Over-Communicating (The “Burn Down” Effect): Brands facing high Customer Acquisition Costs (CAC) often overcompensate by aggressively increasing communication frequency (email/SMS) to their existing audience. This short-term boost leads to audience burnout, resulting in higher unsubscribe rates, reduced click-through rates, and subsequent Gmail down-ranking (deliverability issues), causing emails to land in spam folders rather than inboxes.
2. The Data Gap: Moving Beyond Time-Bound Segmentation: The primary challenge is that existing marketing platforms (like Klaviyo) force brands to rely on simplistic, time-bound segmentation rules (e.g., “clicked in the last 90 days”). This fails to capture true customer intent, especially for high-value customers with longer purchase cycles (e.g., buying every 108 days). The consensus is that the data exists (often hundreds of millions of engagement points), but the tools to process it dynamically are lacking.
3. The Solution: AI/ML for Predictive Engagement Modeling: The recommended solution, exemplified by Oreda.ai’s approach, is to leverage Machine Learning (ML) models that analyze the vast dataset to create a real-time, forced ranking of which customers want to hear from the brand and when.
- Actionable Insight: Stop segmenting based on arbitrary time windows; instead, build models that predict engagement likelihood daily.
- Benefit: This improves inbox placement, reduces spam reports (by avoiding communication with uninterested users), and ultimately optimizes ROI by ensuring communications are sent only when the customer is receptive.
4. Channel Optimization and the Role of Direct Mail: While email is the primary focus, the conversation highlights that retention requires a multi-channel approach. Direct mail is cited as a highly profitable retention channel if deeply segmented to target only the specific customers who will generate incremental profit from a postcard. This reinforces the theme: expensive channels must be reserved for the highest-value, most receptive segments.
5. Strategic Mindset Shift: From Marketer to Consumer: Schwartz urges founders and CMOs to adopt a consumer mindset—questioning their own marketing habits (“Don’t be the brand that emails when you want to, be the brand that emails when they want to hear from you”). This requires challenging internal assumptions and existing playbooks.
6. Organizational and Budgetary Friction: A significant challenge noted is organizational inertia:
- Leadership Conflict: CMOs often face mandates from CEOs to maintain high sending volumes (e.g., 95% of the list) even when data suggests a smaller, highly engaged segment (30%) drives the majority of revenue.
- Budget Silos: Retention SaaS budgets are often treated separately from performance marketing budgets, leading to underinvestment in retention technology, even when the ROI is demonstrably high (e.g., 200x ROI on email).
7. Context and Industry Relevance: This conversation is critical because the current digital marketing environment is defined by high CAC and stagnant revenue, forcing brands to maximize existing customer value. The episode argues that the necessary ML capabilities to solve the retention data problem have only recently become accessible and affordable at scale, making the adoption of these advanced modeling techniques a competitive necessity, not an option. Brands relying on “intuitive” or outdated segmentation risk significant revenue decline post-peak seasons like BFCM.
🏢 Companies Mentioned
đź’¬ Key Insights
"Just focus on what is the job to be done first before you go try to find a solution, right? Like, make sure you understand what needs improvement, and then, you know, especially for even you talk to massive companies, but a person within that company still has [a specific job]."
"It's like anything else. You have a restaurant, and you know three out of ten dishes are bad. Right? Everybody would go fix those three dishes before you started doing advertisements about how great it is and getting more people in the door."
"And they spent six months—and I'm just denying months doing it—that integration, and they advertise it, they show it, they walk through the demo, and a new brand says, 'I want that.' They plug it in, and where that AI goes to harvest all the data is in a different PIM, DAM, CRM, ERP, or MS, WMS, whatever it might be, and the AI breaks down."
"There's a lot of fantastic AI-driven tools that are out there that aren't ready to be rolled out to multiple brands because every brand tries to roll it out to—so the AI stuff really, really works. They've got their trial brand, they've got the use case, their case study, and it really works."
"But we're at a point now where I think people, executive teams, leadership is saying, 'Hey, by the way, you need to drive more revenue. What can we do without spending more money?'"
"I can give you the best timing in the world, but if your content is bad, that's a problem. You get the best content in the world, and I'm going to unsubscribe if you message me at the wrong time. So, it's really the magic happens when the two come together."