Executing a strategic incentive program modeling initiative allows organizations to simulate participant behavior, project operational costs and validate ROI before a program ever goes live. Following a structural shift in its travel partner portfolio, a leading financial services provider used predictive analytics to stress-test a new tier incentive before market launch.
The Situation: Testing a New Tier Structure Before Launch
A new tier incentive needed to work before it went live
What challenge did the financial services provider face? The client needed to understand what a proposed half-year tier incentive would produce, what it would cost and how participants might respond before committing budget to the launch.
A leading financial services provider was preparing a new tier incentive for a travel partner program. Before launch, the client needed to understand what the new structure could produce, what it could cost and how participants might respond.
The proposed incentive used a half-year structure. Because activity was not evenly distributed across the year, the client needed to know whether a half-year program would produce meaningful account volume, how it would compare to an annual structure and how cost would change under different scenarios.
The client also needed to understand how participants might behave as they approach tier thresholds. If participants were close to the next level, would they stretch to get there? And if they did, what would that mean for account volume and program cost?
This was a planning problem, but it was also a confidence problem. The client needed enough data to make decisions before the incentive went live.
The Solution: Data-Driven Simulation and Scenario Analysis
Modeling the tier incentive with historical program data
How did Maritz forecast the impact of the incentive? Maritz used historical program data to simulate participant daily account rates, evaluate multi-product variables and model “stretching” behavior near key tier thresholds.
To answer those questions, Maritz helped the client estimate the likely impact of the proposed tier incentive using historical program data. The analysis simulated the half-year structure, calculated each participant’s daily account rate and modeled stretching behavior when participants were within 15 days of reaching the next tier, a threshold informed by similar programs where participants tend to increase activity gradually before settling back near, and often slightly above, their prior average rate.
The model compared results with and without simulated stretching behavior. It also evaluated scenarios with and without an additional product variable so the client could understand how different program considerations might affect account volume and cost.
The analysis focused on practical questions:
- How many accounts could the half-year incentive generate?
- How would the result compare to an annual structure?
- What would the program cost?
- How would accounts be distributed across tiers?
- What would the cost per incremental account look like?
- How could participant stretching behavior affect the final numbers?
The model projected roughly 18-19% account growth for the half-year incentive without the additional product scenario, equal to about 3,300 accounts. With the additional product scenario included, the projection increased to just over 4,400 accounts. The projected cost per incremental account was just over $120 without the additional product scenario and just over $100 when it was included.
The analysis also showed that the half-year structure could generate more than half of the annual structure’s account volume, even though it ran for only half the year. Because account activity was not evenly distributed across the year, lower thresholds put more participants within reach of the next tier and gave them a clearer reason to stretch.
The Results: Validated Growth and Commercial Confidence
A clearer view of projected cost, modeled account growth and actual performance
What results did the incentive program modeling deliver? The predictive model projected an 18-19% baseline growth rate, which the actual program launch ultimately outperformed by delivering 25% account growth (roughly 4,600 accounts).
The planning work gave the client a clearer view of how the proposed incentive could perform before it went live. Maritz modeled expected account volume, program cost, cost per incremental account and distribution of accounts by tier.
The analysis showed that the half-year incentive could produce more than half of the annual structure’s account volume, with projected results varying by scenario. It also showed how the additional product scenario could affect both account volume and cost.
After launch, the client evaluated the incentive’s annual performance and found that it drove roughly 4,600 additional accounts, or about 25% growth. That result slightly exceeded the original model, giving the client confidence that pre-launch projections were directionally accurate and commercially useful.
The model gave the client a way to evaluate the incentive before making launch decisions. Instead of treating the new structure as a guess, the client could see the likely range of outcomes, understand the tradeoffs behind the design and compare those expectations against actual performance after launch.
What This Means for Incentive Programs: Moving Beyond Guesswork
New incentives should not be built on hope
Why does incentive program modeling matter? Predictive modeling removes the guesswork from program design, allowing companies to evaluate stakeholder tradeoffs, map behavior changes and secure budget approval using data rather than hope.
Before a program launches, clients need to understand how the structure is likely to work: who it reaches, what it costs and where participants may change their behavior. Modeling cannot guarantee the future, but it can help teams make better decisions before the money is spent and the program is in market.
This work shows the value of testing a tier structure before launch. By looking at historic activity, tier thresholds, timing and participant behavior, program teams can make decisions with fewer unknowns.
For organizations planning a new incentive, the question is not whether the idea sounds good. It is whether the structure gives people a clear reason to act, whether the cost hold up under different scenarios and whether the projected impact can be compared against real performance once the program is in market.
