Loyalty Program Analysis and Open Source Audit Techniques: Lessons from ACHIVX
Loyalty programs are now one of the most widely used revenue tools in e-commerce and retail. Industry reports such as Bond’s Loyalty Report and consulting work from McKinsey, BCG, and others indicate that consumers in the United States held around 17.9 loyalty memberships on average in 2023, up from 14.8 in 2019. In Canada the number is close to 15, while in Europe it is roughly 9. Similar numbers and commentary can be found via sources like https://www.mckinsey.com, https://www.bcg.com and https://www.bondbrandloyalty.com.
This means that a typical customer is not choosing whether to join a loyalty program in general; they are deciding whether your specific program is worth their attention compared to many others. From the business side, the core question is no longer “do we need a program?” but rather “does our program create incremental, profitable behavior, and can we prove it in numbers?”
The goal of this article is to show how to quantify that impact and how to audit it. It covers spend uplift, retention, LTV and CAC payback, cash-flow and liability effects, and risk areas such as fraud and over-rewarding. It then explains why open-source implementations like ACHIVX can serve as a transparent reference for audits, even if you run a commercial loyalty platform in production.
Why Loyalty Programs Require an Audit in 2025
Consumers are exposed to dozens of overlapping incentives, points, and cashbacks. At the same time, loyalty programs can easily cost between 1 and 3 percent of revenue in rewards and operations. For a business with annual revenue of 30 million dollars, a 2 percent reward cost means 600,000 dollars per year flowing through the program. A small change from 2 percent to 3 percent adds another 300,000 dollars of cost, which can move EBITDA by several percentage points.
In this environment, a loyalty program cannot be treated as a marketing side project. It should be managed and reviewed with the same discipline as any other major cost or investment. A loyalty audit becomes a recurring process that checks whether the program still meets its financial and behavioral objectives.
Figure 1. Average number of loyalty memberships per consumer in selected regions based on aggregated public data from North America and Europe. Simulated aggregation of published ranges; source patterns consistent with reports such as Bond Loyalty Report and consulting analyses.
Why this is important
A structured audit links loyalty to P&L rather than treating it as a generic engagement tool. It forces explicit assumptions about spend uplift, retention, reward cost, and liability, and it creates a quantitative basis for decisions such as tightening rules, adjusting point values, or even reducing program scope.
Behavioral and Financial Impact of Loyalty Programs
The main behavioral questions for any loyalty program are straightforward. Do members spend more than non-members? Do they stay longer? Do they shift their behavior in ways that matter for margin, such as buying higher-margin categories or increasing basket frequency?
Industry studies summarised by firms like McKinsey on https://www.mckinsey.com and by practical guides from providers and analysts indicate that free loyalty members often spend 10 to 15 percent more than non-members on an annual basis. Paid loyalty members can show 30 to 60 percent higher spend, although these programs are more selective and often come with strong benefits.
To make this concrete, it is useful to express relative spend in an index where non-members are set to 100. Free members may then average around 112, implying a 12 percent uplift, while paid members may sit around 130, implying a 30 percent uplift.
Figure 2. Relative spend index for non-members, free loyalty members, and paid loyalty members. Values are simulated but consistent with ranges reported in public studies on loyalty economics. Source: simulated data aligned with benchmarks cited on sites such as https://www.mckinsey.com.
Consider a business with 100,000 active customers, where non-members spend 300 dollars per year on average. If 40,000 of these customers join a free loyalty program with a 12 percent spend uplift, then non-members contribute 60,000 customers times 300 dollars equals 18 million dollars per year. Members contribute 40,000 customers times 300 dollars times 1.12, which equals 13.44 million dollars. Total revenue becomes 31.44 million dollars. If every customer behaved like a non-member, revenue would be 100,000 times 300 dollars, or 30 million dollars. The program therefore generates about 1.44 million dollars in incremental revenue before considering the cost of rewards.
Summary
Behavioral uplift is the first step, but not the end of the story. You must compare incremental revenue with reward cost, operational cost, and any shifts in margin mix to see whether the net effect is positive.
Unit Economics: Spend Uplift, Retention, LTV, and CAC Payback
Retention and LTV modeling
Retention changes often have more impact on profitability than spend uplift alone. Many reports, including those by McKinsey and Deloitte on https://www.mckinsey.com and https://www2.deloitte.com, indicate that loyalty members are more likely to stay with a brand. For modeling, a simple approach is to assume that retention improves by a fixed number of percentage points when customers participate in the program.
Figure 3. Three-year LTV per acquired customer with and without a loyalty program. All numbers are simulated using assumed retention and margin uplift.
Imagine that the annual net margin per active customer is 40 dollars without a loyalty program and 48 dollars with the program, which corresponds to a 20 percent uplift. Assume that the probability that a customer is still active in year one, two, and three is 70 percent, 49 percent, and 34 percent without loyalty, and 80 percent, 64 percent, and 51 percent with loyalty. These retention paths are created by successively multiplying the first-year retention by itself.
Lifetime value over three years can be approximated as the sum over each year of annual margin multiplied by retention in that year. For the baseline case this is 40 dollars times the sum of 0.70, 0.49, and 0.34, which equals 40 times 1.53, or about 61 dollars. For the loyalty case this is 48 dollars times the sum of 0.80, 0.64, and 0.51, which equals 48 times 1.95, or about 94 dollars. The resulting uplift in LTV is roughly (94 minus 61) divided by 61, which is about 54 percent.
If your customer acquisition cost (CAC) is 40 dollars, then LTV to CAC moves from 61 divided by 40, which is 1.5, to 94 divided by 40, which is 2.35. This shift is substantial for any growth-stage business.
CAC payback acceleration
Another metric that sales and finance teams care about is payback period. This is the number of months of contribution margin required to recover the cost of acquisition.
Figure 4. Cumulative contribution margin per customer over twelve months for a 40 dollar CAC with and without loyalty uplift. Data simulated.
Assume that baseline contribution margin is 6 dollars per month per newly acquired customer and that loyalty increases it by 20 percent to 7.20 dollars. The CAC remains 40 dollars. The baseline payback period is 40 divided by 6, which is roughly 6.7 months. With loyalty uplift, the payback period is 40 divided by 7.20, which is approximately 5.6 months. A reduction of 1 to 1.5 months in payback is meaningful when you are acquiring thousands or tens of thousands of customers per quarter because it lowers capital intensity and improves the cash conversion cycle.
Why this is important
Linking loyalty to LTV and CAC payback makes discussions with finance and investors much easier. Instead of debating abstract engagement metrics, you can show how the program shifts LTV to CAC from, for example, 1.5 to above 2.0 and how it reduces payback by more than a month.
Cash Flow Dynamics: Issuance, Redemption, and Liability
Timing differences between issuance and redemption
Loyalty programs often create a gap between the time when rewards are promised and the time when they are actually redeemed, if at all. This gap affects both cash flow timing and accounting.
Figure 5. Example monthly points issuance versus redemption expressed as a percentage of revenue. Data simulated to reflect a typical maturation curve for a loyalty program.
In the example shown in Figure 5, points issuance is modeled at a constant 1.5 percent of revenue each month, while redemption gradually rises from 0.3 percent to about 1.2 percent as customers learn to use their points. For a business with 300,000 dollars in monthly revenue, issuance cost is 1.5 percent times 300,000, or 4,500 dollars in economic value promised per month. Redemption costs ramp from 0.3 percent times 300,000, which is 900 dollars, up to 1.2 percent times 300,000, which is 3,600 dollars.
From a cash perspective, the company receives the full 300,000 dollars of revenue at the time of sale. From an economic perspective, a portion of that revenue represents future discounts. An audit needs to reconcile the difference between points issued and points redeemed and to track how this difference accumulates as a liability.
Liability and breakage
Points that are issued but not yet redeemed represent a liability on the balance sheet. Not all points will be redeemed, so a breakage estimate is used to adjust the expected cost.
Figure 6. Example outstanding points liability and annual breakage rate over four quarters. Data simulated in line with common patterns observed in loyalty programs.
Assume that outstanding points liability is 100,000 dollars in Q1, 120,000 dollars in Q2, 135,000 dollars in Q3, and 140,000 dollars in Q4. At the same time, estimated annual breakage rises gradually from 12 percent to 15 percent as more historical data becomes available. Effective expected redemption cost is calculated as liability multiplied by one minus breakage. For Q4 this equals 140,000 dollars times 0.85, or 119,000 dollars.
Various discussions of breakage and liability accounting can be found in loyalty industry analyses and airline and credit card program reporting, accessible through sources such as https://home.kpmg and https://www2.deloitte.com, where accounting treatment of points and miles is often discussed.
Risks to watch out for
Underestimating liability can lead to unpleasant surprises for finance teams and auditors if an unexpectedly high wave of redemptions occurs. Overestimating breakage can inflate short-term profit by recognizing revenue too early. Sudden policy changes, such as extending expiration dates, can instantly raise expected redemption and reduce reported profit, so they need careful modeling.
Risk Areas: Fraud, Over-Rewarding, and Structural Weaknesses
Fraud and misuse are common in poorly controlled loyalty environments. Self-referrals, fake review activity, and repeated account creation to harvest welcome bonuses can all inflate points issuance without adding true incremental value.
Imagine that 100,000 users are active in your program and 1 percent of issued points are effectively fraudulent. If the economic value per point is 0.01 dollars and you issue 200,000 points per month, then 1 percent corresponds to 2,000 points or 20 dollars per month, which may appear small. However, large programs can easily issue tens of millions of points per month. At 10 million points, 1 percent fraud equals 100,000 points or 1,000 dollars of cost per month, or 12,000 dollars per year.
Over-rewarding loyal customers is another structural weakness. If the top 10 percent of customers already have a strong habit of buying from you, and they spend 1,500 dollars per year with a reward cost of 3 percent, then each of them receives 45 dollars in economic value annually. For a cohort of 10,000 high-value customers, this is 450,000 dollars in reward cost. If analysis shows that their behavior would have been almost identical without these rewards, this is a direct margin erosion rather than an investment.
Summary
Risk analysis should quantify not only explicit fraud but also structural over-rewarding of customers whose baseline behavior is already strong. An audit using detailed event-level data allows you to estimate how many points are actually needed to shift behavior rather than simply being granted to existing fans.
Why Open Source Matters for Loyalty Audits
Many loyalty platforms are delivered as closed SaaS solutions. While they can be powerful and convenient, they often limit the visibility that finance and data teams have into how exactly events are processed, how rules are applied, and how balances are computed.
With a closed system, you typically see aggregated reports and dashboards. You may be able to access raw transaction data, but the exact translation from events to points and from points to liability often remains encoded in proprietary logic that cannot be inspected or tested independently.
Open-source systems such as ACHIVX, which can be explored via documentation and repositories referenced on https://achivx.com, change this dynamic. The core logic for event handling, reward calculation, and balance updates is visible in code. This allows engineers, analysts, and auditors to read, test, and extend the logic as needed.
Figure 8. Conceptual comparison of closed SaaS and open-source loyalty stacks on several auditability dimensions. Scores are simulated on a scale from 1 to 5.
From an audit perspective, open source has several concrete advantages. Data access is typically under your organization’s control because the system can run in your own infrastructure. Pricing transparency improves because you can see precisely which rules drive reward issuance. Custom rules can be codified and version-controlled. Vendor lock-in risk is lower because you can fork or migrate the code if necessary. Security, including how tokens and balances are managed, can be verified by your own security team.
Why this is important
When loyalty economics and liability recognition become material to the P&L, internal and external auditors will want to know how the numbers are produced. An open-source implementation allows you to provide evidence not only through reports but also through actual executable logic.
ACHIVX as an Open Source Reference Implementation
ACHIVX is an open-source project focused on event-driven reward and achievement logic for loyalty and gamification scenarios. Information about the project, including its architecture and licensing, can be found on https://achivx.com and related repositories.
From a non-commercial perspective, ACHIVX is useful as a reference implementation for loyalty audits. The system typically includes:
An event-driven architecture in which customer actions such as account registration, order placement, or review submission generate events that can be processed by a rules engine.
A rule-based approach for assigning points to events, represented in code that can be read and tested.
A transparent transaction log or ledger of point credits, debits, and balance changes, stored in a database under your control.
Configuration and code that can be version-controlled and linked to specific program changes and campaigns.
This enables you to build what is sometimes called a shadow ledger. Production events from your main loyalty platform can be forwarded to an ACHIVX instance. The same core rules for point earning and redemption can be implemented there. You can then compare the balances produced by your vendor to those produced by ACHIVX and reconcile any differences.
Summary
ACHIVX is not simply another way to run a loyalty program. It is a potential audit tool and reference engine that mirrors your rules in an open, testable environment, so that finance, data, and security teams can independently verify outcomes.
Action-Based Points Program: Quantitative Example
Action-based programs assign points to specific events rather than only to transaction value. This structure fits well with an ACHIVX-style architecture.
Figure 9. Example action-based points matrix showing events, points, and monthly estimated volumes. Data simulated.
Consider the following monthly pattern for a mid-size e-commerce store. Account registration is rewarded with 50 points and occurs 500 times per month. First orders receive 200 points and occur 300 times. Repeat orders are rewarded with 100 points and occur 700 times. Review submissions earn 40 points and occur 200 times. Referral purchases earn 300 points and occur 50 times.
Monthly points issuance is then calculated as follows. Registrations account for 50 points times 500, or 25,000 points. First orders account for 200 times 300, or 60,000 points. Repeat orders account for 100 times 700, or 70,000 points. Reviews account for 40 times 200, or 8,000 points. Referrals account for 300 times 50, or 15,000 points. Total issuance is 178,000 points per month.
If the economic value of a point is 0.01 dollars, then monthly reward cost is 178,000 points times 0.01, equal to 1,780 dollars. For monthly revenue of 300,000 dollars, this corresponds to a reward cost of 1,780 divided by 300,000, or about 0.59 percent of revenue. This sits well within a typical target band of around 1 to 2 percent for many e-commerce loyalty programs discussed in practice-oriented resources and case studies, including those referenced by consultancies like BCG on https://www.bcg.com.
Because ACHIVX exposes the mapping from events to points in code, it becomes straightforward to simulate scenarios such as doubling referral rewards, halving registration rewards, or introducing extra points for specific product categories. For each scenario, you can estimate changes to total issuance, reward cost as a percentage of revenue, and expected changes in retention and LTV based on historical uplift patterns.
Figure 10. Example coverage levels for key components of a loyalty audit in a first pass: data extraction, event taxonomy, liability modeling, experimentation, and governance. Data simulated.
In a first-pass audit, you might find that data extraction pipelines cover around 60 percent of required flows, event taxonomy documentation covers 40 percent of events, liability modeling covers 30 percent of scenarios, experimentation such as A/B tests covers 20 percent of key rules, and governance processes cover only 10 percent of changes with formal approvals. These numbers are hypothetical, but they reflect a common pattern in which data and tracking receive attention earlier than liability, experimentation, or governance.
Data and Methods
This article combines three sources of information. First, it draws on public statistics and commentary from consulting firms and industry reports, including sites such as https://www.mckinsey.com, https://www.bcg.com, https://www.bondbrandloyalty.com, https://www2.deloitte.com and https://home.kpmg. These sources discuss broad ranges for loyalty program adoption, spend uplift, retention effects, and typical reward cost levels in sectors like retail, travel, and financial services.
Second, it uses domain-informed parameter ranges to calibrate simulations. For example, reward cost levels between 1 and 3 percent of revenue are consistent with many practical case studies in retail and e-commerce. Breakage rates for points in the 10 to 30 percent range are consistent with published information about airline miles and credit card rewards.
Third, the article uses simulated data to demonstrate specific numerical effects and provide clear charts. The synthetic customer base is 100,000, annual margins per active customer range from 40 to 48 dollars, retention rates between 70 and 80 percent are used, reward costs fall between 0.3 and 1.5 percent of revenue, monthly revenue is set at 300,000 dollars, and point value is fixed at 0.01 dollars.
Lifetime value is computed as the sum over three years of annual margin multiplied by the probability that the customer is still active in each year. Payback period is computed as CAC divided by monthly contribution margin. Reward cost as a percentage of revenue is calculated by multiplying total points issued by value per point and dividing by revenue. Liability is calculated as outstanding points multiplied by value per point, and effective cost after breakage is given by liability multiplied by one minus the breakage rate.
All figures from fig01.png to fig10.png are generated with these simulated scenarios and are sized at least 1400 by 900 pixels with font sizes at or above 14 pixels, using a landscape orientation.
One-Page Audit Checklist
A practical loyalty audit combines strategic, behavioral, financial, technical, and governance aspects. Instead of presenting separate bullet points, it is helpful to think of the checklist as a sequence of questions to answer.
At the strategic level, you should be able to clearly state the economic objective of the program. That might be, for instance, a 12 percent increase in order frequency, a 15 percent increase in LTV, or a target reward cost band of 1 to 2 percent of revenue. It should also be clear whether these targets differ by segment, for example new customers versus existing ones.
From a behavioral and incremental perspective, you should know whether you have control or holdout cohorts that allow you to compare members with non-members. You should be able to quantify incremental revenue and incremental retention, not just total revenue from members. You should also compare CAC payback for cohorts acquired with and without loyalty incentives.
On the data quality side, you should ensure that your event taxonomy is complete and documented. Every relevant customer action, including orders, cancellations, returns, reviews, and referrals, should generate consistent events that are logged with timestamps and identifiers. Analysts should be able to access raw event logs, not only aggregate dashboards exported from the loyalty vendor.
For points and rewards, it is important to calculate and track total points issued and redeemed in each period, to compute the liability of outstanding points, and to estimate breakage based on historical patterns. You should also run sensitivity analyses at different reward cost levels, for example 1, 2, 3, 4, and 5 percent of revenue, to see how net margin would respond.
Fraud and abuse controls should cover duplicate accounts, self-referrals, review manipulation, and stacking of multiple promotions. A loyalty audit needs to evaluate whether detection mechanisms and limits are sufficient, and whether the estimated loss from fraud is material relative to total reward cost.
Governance processes should ensure that all changes to loyalty rules, reward levels, and campaign configurations are logged and reviewed. Ideally, material changes are reviewed by marketing, product, finance, and legal stakeholders and are rolled out with monitoring and, where feasible, experimentation.
Finally, from an open-source and auditability standpoint, it is valuable to maintain a reference implementation such as an ACHIVX instance that mirrors your earning and redemption rules. Production events can be replayed into this system to compute independent point balances and liability estimates. This shadow ledger can then be compared to vendor reports to detect discrepancies.
Glossary
ACHIVX is an open-source project that provides event-driven reward and achievement logic for loyalty and gamification scenarios, described on https://achivx.com. It can serve as a reference implementation or audit tool.
Breakage is the percentage of issued points that are never redeemed, either because they expire or because customers do not use them.
CAC (Customer Acquisition Cost) is the average cost required to acquire a new paying customer, often including marketing, sales, and related expenses.
Contribution Margin is the revenue from a customer or transaction minus variable costs directly associated with serving that customer or fulfilling that transaction.
Event Taxonomy is a structured definition of all events that can occur in a system, such as account_created, order_placed, order_cancelled, review_submitted, and referral_completed, along with their attributes.
Incrementality refers to the portion of behavior (such as additional purchases or increased spend) that would not have occurred without the loyalty program or specific campaign.
Liability in a loyalty context is the expected future cost associated with outstanding points or rewards. It is calculated as the number of points multiplied by their expected redemption value.
LTV (Lifetime Value) is the total expected net margin from a customer over a defined period, often several years, considering both revenue and retention.
Redemption Rate is the proportion of issued points that customers actually redeem for rewards during a given time window.
Shadow Ledger is an independent system that replicates the accounting of points, balances, and liability, used to validate and audit the calculations performed by a primary loyalty platform.
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