LTV, churn risk, lookalikes, segment definitions. Sage runs the math; the senior strategist decides what to override. The segments ship back to your ad platforms and lifecycle tools — actionable, not theoretical.
Each pillar is operated by a named member of the AAAT or your senior strategist. None are optional — skipping any one creates a hole you will pay for later.
We start with your data: Shopify, CRM, Klaviyo. Sage joins what is joinable and surfaces what is missing. Modeling on platform-only signals is a path to silent over-spend.
Sage models LTV by acquisition month, by source, by product. The output is a CAC payback curve a CFO can read.
Built off your best-LTV cohort, not your top spenders. Refreshed monthly. Pushed live to Meta and Google Customer Match.
A churn model that pages Lookout when a cohort starts drifting. Klaviyo winback flows fire against the model output, not against arbitrary day counts.
We do not deliver insights as a PDF. Segments ship into Google Ads, Meta Custom Audiences, and Klaviyo as named lists with refresh cadence.
What you get from this engagement — concrete, named, owned by a senior strategist or a member of the AAAT.
The same questions buyers ask in a sales call. Same answers we give.
At least six months of orders or events tied to identifiable customers. Below that, we can still rebuild segmentation but LTV modeling is shaky.
Inside your ad platforms and Klaviyo, as named lists with refresh cadence. We do not gate-keep your audience data behind our dashboard.
No. Sage models against your data within the engagement, but we do not contribute customer data to model training. Engagement-level confidentiality is the default.
AdMax's Audience + Data Science services begin with a deep dive into your existing customer data. We meticulously audit what information you collect, how it can be joined across different sources, and identify any critical gaps that might be hindering comprehensive analysis. This foundational step ensures that our subsequent modeling and segmentation efforts are built on the most robust and accurate data available.
This initial audit provides a clear roadmap for data enhancement and integration. We'll outline specific recommendations for data collection improvements and identify the most valuable data points to focus on, ensuring that your first-party data becomes a powerful asset for understanding customer behavior and driving marketing decisions.
Gain a clear understanding of your customer's long-term value and the cost associated with acquiring them. AdMax's Sage platform models LTV by acquisition month, source, and product, providing granular insights into which customer cohorts are most profitable. This allows for precise calculation of CAC payback periods, enabling you to optimize marketing spend for maximum return.
Our LTV and CAC payback models are not just theoretical reports; they are actionable insights. By segmenting LTV based on key acquisition drivers, you can identify high-value customer groups and tailor acquisition strategies to attract more of them, while also understanding the efficiency of your current marketing channels.
Move beyond generic lookalike audiences and build segments that truly reflect your most valuable customers. AdMax creates lookalike audiences seeded from your best LTV cohorts, ensuring that the new audiences share the characteristics of your most profitable customers, not just those who spend the most. This targeted approach significantly improves the effectiveness of your advertising campaigns.
These sophisticated lookalike audiences are designed for direct integration into your advertising platforms. By leveraging the insights derived from your highest LTV customers, you can reach new prospects who are more likely to convert and contribute to long-term business growth, driving down acquisition costs and increasing overall marketing ROI.
Proactively identify customers at risk of churning and implement targeted retention strategies. AdMax's data science services assess churn risk by analyzing various customer behaviors and signals. This allows for the development of data-driven winback flows and personalized engagement tactics designed to re-engage at-risk customers before they leave.
Our churn risk models are designed to trigger specific actions within your lifecycle marketing tools. For instance, winback flows can be automated to fire based on the model's output, rather than arbitrary timeframes, ensuring that interventions are timely and relevant, thereby increasing customer retention rates and protecting your customer lifetime value.
Ready to see this in your account? Book a thirty-minute call with a senior strategist. We talk shop, not slides.