How to Calculate the ROI of a Luxury Wealth Screening  

The global luxury market continues to expand, driven disproportionately by high-net-worth (HNW) and ultra-high-net-worth (UHNW) individuals. Overall luxury spending, including both goods and experiences, is expected to grow beyond $2 trillion by 2030, intensifying competition for high-value clients and raising expectations for personalization, relevance, and return on investment. 

Luxury brands often have unrealized opportunities within their existing customer, prospect, and engagement data. While client acquisition and marketing teams invest heavily in premium outreach, events, and experiences, many lack a reliable way to identify which individuals truly warrant high-touch engagement—and how much incremental value that prioritization could unlock. 

Building a case for a wealth screening

A wealth screening identifies high-net-worth (HNW) and ultra-high-net-worth (UHNW) individuals within existing datasets, providing verified insight into spending capacity rather than transaction history alone. In practice, for luxury brands, this distinction matters: relying solely on transaction data means operating without visibility into true client potential. The result is often misprioritized outreach and unrealized revenue opportunities. 

Wealth screening addresses this gap with a quantifiable, scalable approach. Client acquisition and marketing teams can use it to generate incremental revenue that would otherwise have been left on the table, and the returns can be modeled, measured, and connected directly to business outcomes. 

Yet, for luxury professionals, the decision to invest in a wealth screening is not always clear. This guide shows luxury teams how to maximize and demonstrate the value of a wealth screening across industries. Using a practical ROI formula, luxury leaders can build a clear business case and demonstrate that the return significantly exceeds the cost.  

Let’s explore the repeatable ROI framework below, suitable for different luxury categories. 

The ROI framework for a wealth screening for luxury brands 

The return on a wealth screening can be modeled using a small number of inputs that are familiar to client acquisition and marketing teams. The framework below is intentionally straightforward, enabling teams to build and review a business case quickly. 

Incremental revenue calculation

• Input Volume is the number of records screened 
• Match Rate is the percentage of records that match to verified wealth profiles 
• Engagement Rate is the percentage of matched individuals who convert 
• Revenue per Customer is the average revenue generated per conversion

ROI calculation

• Screening cost represents the total cost of running the screening on the selected dataset. 

Notably, this framework can be applied consistently across luxury categories. Differences in ROI are driven by the assumptions used for each input, not by changes to the formula itself. 

Defining “conversion” in a luxury context 

In this context, what constitutes a meaningful conversion? In luxury environments, conversion often extends beyond a traditional transaction. 

For example, depending on the business model, conversion may be defined as: 

• A completed consultation or appointment that leads to a sale 
• A purchase above a defined value threshold 
• A deposit placed for a future asset or experience 
• Revenue attributed to an invite-only event or private activation 

Aligning on a definition of conversion ensures that engagement rates and revenue assumptions are credible and aligned with internal reporting standards. 

Step-by-step guide to calculating ROI 

Step 1: Determine input volume

To begin, select a dataset that is already tied to active engagement or outreach. Common examples include CRM records, clienteling lists, loyalty members, event invitees, or qualified inbound inquiries. 

Input volume should reflect a dataset where improved prioritization would realistically change how resources are allocated. 

Step 2: Estimate match rate

Notably, match rate represents the share of records that can be confidently linked to individual wealth profiles. Match rates vary based on data quality, geography, and the nature of the dataset. 

For internal modeling, many teams begin with conservative assumptions or evaluate a range (low, base, high) to understand sensitivity. 

Step 3: Define engagement rate

Engagement rate reflects the percentage of matched individuals who convert, based on the definition established earlier. 

In luxury contexts, engagement rates often improve when teams reserve outreach, events, and advisor time for individuals with verified capacity and relevance. For ROI modeling, teams should ground engagement assumptions in existing high-touch motions rather than speculative uplift.

Step 4: Establish revenue per customer

Revenue per customer should be drawn from metrics already recognized by finance or commercial leadership. This may include average transaction value, average deal size, or average first-year revenue. 

Where outcomes vary significantly, using a conservative average for premium engagements helps ensure the model remains credible. 

Step 5: Account for screening cost

Screening cost represents the investment required to run a wealth screening against the selected dataset. As a result, this cost forms the basis for the ROI calculation, and teams should treat it as a discrete line item. 

Some teams also model internal activation costs separately to present multiple scenarios. 

Example ROI calculation 

To illustrate how the framework works in practice, consider the following scenario:

• Input Volume: 100,000 records 
• Match Rate: 5% 
• Engagement Rate: 1% 
• Revenue per Customer: $7,500 
• Screening Cost: $31,000 

Incremental revenue:

100,000 × 0.05 × 0.01 × 7,500 = $375,000

ROI:

($375,000 − $31,000) ÷ $31,000 × 100 = 1,110%

This example demonstrates how relatively small improvements in targeting and engagement can translate into meaningful financial impact. 

Industry benchmarks for ROI calculations 

In practice, teams can apply the same ROI framework across luxury categories by adjusting assumptions for match rate, engagement, and revenue per customer.

Assumptions held constant:

• Records screened: 100,000 
• Screening cost: $31,000

These scenarios illustrate how different operating models affect ROI.

Considerations for internal review 

When presenting ROI internally, teams can strengthen credibility by doing the following:

• Using conservative base-case assumptions 
• Anchoring engagement rates to known programs or motions 
• Aligning revenue inputs with existing reporting standards 
• Including a simple sensitivity analysis to show how results change with modest input variation 

Ultimately, this approach helps position wealth screening as a measured investment rather than an experimental initiative.

Strategic implications beyond the model 

While this guide focuses on financial evaluation, it also shows how wealth screening supports more disciplined decision-making. Verified insight into client capacity enables more consistent personalization, more efficient allocation of high-cost resources, and reduced risk of misaligned outreach. Over time, regular screening contributes to a more robust foundation for luxury customer intelligence. 

Successful wealth screening with Altrata 

While the framework above helps luxury teams understand the opportunity wealth screening creates, implementing it requires accurate, continuously refreshed wealth intelligence you can trust. That’s where Altrata comes in. Get in touch with our team for a personalized ROI simulation.