# Loyalty Program Benchmark Report 2027

State of loyalty program success, measurement and business impact.

Published by Open Loyalty. Based on an annual loyalty survey, a market pulse across 150+ companies, and 500+ industry conversations (1,500+ hours of conversations).

> **How to use this file:** this is the full text of the Loyalty Program Benchmark Report 2027 in Markdown, prepared for use with AI assistants such as Claude or ChatGPT. Charts are converted into tables. Percentages in each section refer only to the research source named in that section. For the designed version with all visuals, see the PDF.

## Contents

1. Introduction
2. Executive summary
3. Self-assessment
4. Survey results: defining success, success metrics, expected results, key challenges
5. 2026 vs 2027 survey results
6. Market pulse
7. Loyalty in practice
8. Strategic recommendations from Open Loyalty experts

---

## 1. Introduction

### Let's discover the loyalty benchmark together!

**About the report**

The Loyalty Program Benchmark Report 2027 shows how loyalty programs define success, what they measure, what they want to improve next, and what gets in the way. Then, it adds broader market context and real implementation patterns to show where ambition and execution diverge.

Rather than forcing every input into one dataset, the report uses three separate research streams, each for the questions it's best suited to answer. Together, they give a more complete view of loyalty priorities, maturity, and execution.

**How to read the report**

The findings should be read as complementary evidence, not one combined dataset. The survey is the core benchmark, market research adds context on current priorities, and industry conversations show how those priorities play out in real projects.

We compare with the 2026 benchmark only where questions and categories align, so read those changes as directional signals.

**Who's behind the report?**

The Loyalty Program Benchmark Report 2027 was developed by Open Loyalty, a loyalty technology company working with brands across industries and markets.

Our position gives us a view of both what is expected from loyalty programs and the technology, data, and organizational decisions needed to deliver it.

The report turns those recurring questions and market signals into a structured benchmark loyalty teams can use beyond a single campaign, implementation, or vendor decision.

**Why have we created the report?**

Loyalty teams are asked to improve retention, grow customer value, personalize experiences, and prove commercial impact at the same time, yet there is no universal model of a successful program.

This report shows what other loyalty teams are trying to achieve, how they measure progress, and where they struggle. The result is a practical benchmark for testing your assumptions about strategy, measurement, technology, and program development.

### What will you learn from the report?

This report brings together the strongest signals from the 2027 benchmark and puts them into context with the previous edition. You'll see how loyalty teams define success right now, which metrics they rely on, what they want to improve next, and where execution still gets stuck.

- How loyalty leaders define success
- Which metrics are actually tracked
- What loyalty teams want to improve
- What challenges stand in the way
- How your loyalty program compares to others in your industry

### Research methodology

The Loyalty Program Benchmark Report 2027 combines three separate research streams to look at loyalty from different angles. The annual survey provides the core benchmark, anonymized market research adds a broader view of current priorities and investment areas, and industry conversations show how those themes appear in real implementation decisions.

| Research stream | What it covers |
| --- | --- |
| Annual loyalty survey | Structured and open-ended responses from 56 loyalty professionals, covering four core areas: how success is defined, which metrics are tracked, what teams want to improve next, and what gets in the way. |
| Market benchmark analysis (market pulse) | Anonymized responses from 210 loyalty practitioners, analyzed to understand near-term areas of focus, current execution friction, and the capabilities teams are actively exploring. |
| Industry conversations | Recurring themes from 504 loyalty conversations with organizations launching, replacing, or expanding loyalty initiatives. The analysis shows which implementation issues come up most often in real-world decision-making. |

### Foreword

> "Expectations for loyalty programs are rising. Member activity and reward redemption tell us how people interact with a program, while retention, customer lifetime value, incremental revenue, and ROI show what that activity delivers for the business.
>
> The 2027 benchmark looks at both sides of that equation: how loyalty teams define and measure success, and what they are prioritizing next as their programs develop.
>
> We also bring those findings together with patterns from hundreds of loyalty conversations. The result is a practical view of where loyalty teams are focusing their attention, where execution tends to get difficult, and how the market is approaching the next stage of loyalty development."
>
> — Karl Bzik, Co-founder at Open Loyalty

---

## 2. Executive summary

*What the 2027 benchmark says about loyalty success, execution and next-stage readiness.*

### Key report takeaways

| Finding | What it means |
| --- | --- |
| **53.6%** of respondents define loyalty success through Retention/LTV | Loyalty is strongly judged on whether it keeps customers for longer and increases their value over time. |
| **55.4%** of respondents track Engagement as a success metric | Engagement remains the most commonly tracked operational signal, closely followed by financial impact (53.6%). |
| **41.1%** of respondents want to improve Engagement/Participation next | The most common near-term priority is getting members more active, with the expectation that stronger participation will translate into revenue, retention and CLV. |
| **45.7%** of respondents are evaluating AI for loyalty, CRM or marketing automation | AI is now a mainstream evaluation area. In the same market pulse, identity and measurement capabilities appear considerably lower on the evaluation list. |
| **51.8%** of respondents set an explicit numeric improvement target | More than half still describe improvement directionally, leaving a gap between strategic ambition and measurable accountability. |
| **28.6%** mention Integration as a challenge | The strongest survey signal is that execution friction sits in the stack around loyalty. |

*Source: annual loyalty survey (53.6%, 55.4%, 41.1%, 51.8%, 28.6%) and market pulse data (45.7%).*

### Execution is where loyalty teams hit the most friction

Across sources, loyalty teams struggle less with deciding what loyalty should do and more with whether the stack, data and operating model can support it.

Replacing a loyalty setup is rarely just a search for more features. For many teams, it's a move away from architectural and operational constraints that limit how quickly the program can evolve.

**Top implementation challenges** (source: annual loyalty survey)

| Challenge | Share of respondents |
| --- | --- |
| Integration | 28.6% |
| Strategy | 26.8% |
| Data | 25% |
| Tech | 23.2% |
| Org alignment | 23.2% |

**Recurring implementation friction** (source: 504 industry conversations)

- **51.1%** of companies in the analysis raised integration complexity.
- **49.8%** of companies raised current-platform or legacy limitations.
- **81.2%** of replacement-stage companies raised current-platform or legacy limitations.

### AI and personalization are moving faster than data and measurement capabilities

Teams are prioritizing capabilities that improve relevance at scale. Identity and measurement tools appear much lower on the evaluation list.

**What loyalty teams are actively evaluating next** (source: market pulse)

| Capability | Share of respondents |
| --- | --- |
| Personalization and decisioning | 46.7% |
| AI for loyalty, CRM and marketing automation | 45.7% |
| Gamification and engagement mechanics | 42.9% |
| Predictive analytics and measurement | 21.4% |
| Customer data platforms and identity resolution | 12.9% |

Interest in AI and personalization is running ahead of interest in data and measurement capabilities. AI is being evaluated by 45.7% of respondents, compared with 12.9% evaluating customer data platforms and identity resolution, around 3.5× more often.

### What loyalty leaders should take from the benchmark

The next advantage comes from measurement discipline, connected architecture and the ability to change fast.

1. **Define success beyond activity.** Tie loyalty to retention, customer value, revenue impact and ROI, not just member usage.
2. **Use engagement as the operating lever.** Participation is the first growth layer, but it needs to convert into customer value and business impact.
3. **Put numbers on improvement.** Directional ambition is useful. Numeric targets make loyalty performance measurable and accountable.
4. **Fix the execution layer.** Integration, data, ownership and delivery capacity are where loyalty growth usually slows down.
5. **Treat AI as a scale layer.** Start with identity, event data and measurement before automating member-level decisions.

### Report created with experts' input

Experts contributing to the report have experience at companies including Qatar Airways, Douglas, LEGO, Leroy Merlin, Decathlon, Samsung, Kaufland, Comarch, Carrefour, Sephora, Shell, BAT, adidas, Revolut, TUI, Virgin Active, Uber, Orange, Crocs, ING, New Balance, IKEA, Flying Tiger Copenhagen, ALDI and MediaMarkt.

---

## 3. Self-assessment

*How ready is your loyalty program to move forward?*

### Self-assessment rules

Use this quick self-assessment to identify where your loyalty setup is strongest, and where the benchmark suggests you should look more closely.

Score each statement: **0 = not yet, 1 = partly, 2 = yes.**

### Self-assessment questions

| Area | Question |
| --- | --- |
| Business impact | Can you show how loyalty contributes to revenue, retention, CLV or ROI, beyond member activity alone? |
| Measurement | Do you track behavioral metrics such as engagement together with financial or customer-value outcomes? |
| Goal setting | Do you have explicit numeric improvement targets rather than only directional goals such as "increase engagement"? |
| Data and integration | Can customer identity, transactions and loyalty events move reliably across your loyalty, CRM, POS, ecommerce and other core systems? |
| Ownership and capacity | Is there clear ownership of loyalty, with enough cross-functional capacity to continuously develop and optimize the program? |
| Ability to change | Can you introduce new mechanics, channels, markets or integrations without major redevelopment of the loyalty core? |
| AI readiness | Before introducing AI-driven personalization or decisioning, do you already have reliable customer identity, event data and a way to measure incremental lift? |

### Score

| Score | Where you stand | What it means | Next move |
| --- | --- | --- | --- |
| 0–5 | Fix the foundations first | Your setup is still limited by the fundamentals, usually measurement, data, ownership, or architecture. | Go to "Loyalty in practice" |
| 6–9 | The basics are there, but execution may be the constraint | You already have key building blocks in place, but gaps between systems, teams, or measurement may be slowing progress. | Go to "Key challenges" |
| 10–12 | Ready to scale more systematically | You have a relatively strong foundation. Your next step is to increase impact and expand capabilities without adding unnecessary complexity. | Go to "Market pulse" |
| 13–14 | Optimize, experiment, and automate | You have the core setup in place. Your next opportunity is to improve decisioning, personalization, experimentation, and automation. | Go to "Strategic recommendations" |

This is a directional self-assessment based on the 2027 benchmark. For a tailored view of your setup, talk to Open Loyalty's loyalty experts.

---

## 4. Survey results

*How loyalty leaders define success, measure it, set goals, and describe challenges.*

We asked loyalty professionals how they think about four parts of program performance: what success looks like, which metrics prove it, what they want to improve next, and what gets in the way.

Together, these four areas show both what loyalty programs are expected to achieve and how teams assess whether they're getting there.

- 56 loyalty professionals surveyed
- Responses analyzed and coded into themes
- 4 connected research areas
- Directional comparison with the 2026 benchmark

*All percentages in this section come from the annual loyalty survey. Open-ended responses were coded into one or more themes. Percentages show the share of 56 respondents whose answer included each theme, so totals don't sum to 100%.*

### Defining success

*Survey question: When would your main business owner consider your loyalty program a success?*

#### Business moments defined as a loyalty program success

| Theme | Share of respondents |
| --- | --- |
| Retention/LTV | 53.6% |
| Revenue Impact | 51.8% |
| Engagement/Activity | 30.4% |
| ROI/Financial | 26.8% |
| Acquisition/Membership | 19.6% |
| Satisfaction/Sentiment | 17.9% |
| Redemption | 7.1% |
| Strategic Alignment | 5.4% |
| Advocacy | 5.4% |
| Operational | 3.6% |
| Data/Segmentation | 3.6% |
| Tier/Progression | 1.8% |

**Prove the business impact.** 51.8% of respondents mention Revenue Impact, while 26.8% point to ROI/Financial return when defining loyalty success. Loyalty is expected to show a clear sales contribution and a clear return. Programs with a stronger revenue link have a stronger case for budget.

**Build a relationship that lasts.** 53.6% of respondents mention Retention/LTV, and 17.9% also point to Satisfaction/Sentiment. The strongest success signal is customer behavior over time: customers stay, buy again, and deepen their relationship with the brand.

**Keep members genuinely active.** 30.4% of respondents mention Engagement/Activity as a sign that the program is working. Activity still carries weight, though it sits behind retention and revenue. Teams look for signs that members participate, earn, redeem, and return regularly.

**Keep growing the program.** 19.6% of respondents mention Acquisition/Membership as part of their success definition. Program growth still matters, though it appears less often than retention or revenue. New member growth carries more weight when it leads to repeat activity and commercial impact.

#### When does the business call loyalty a success?

How loyalty professionals describe the moment their main business owner considers the program successful.

**The common thread:** a loyalty program is a success when it drives measurable business impact and creates value for both the company and the customer.

- **Financial impact & ROI:** clear positive ROI; drives incremental revenue; increase in turnover/sales; increase in direct ROI; increased revenue over inflation; commercial ROI covers operational costs; drives additional visits, a higher basket size, and revenue above costs; improved margins and reduced marketing costs.
- **Business & operational outcomes:** supports the company's profitability; self-sustaining growth cycle; faster marketing time-to-market; reduced reliance on third-party channels; better customer data and insights; cross-brand engagement; fuels continued business growth (via rewards); direct impact on organizational KPIs.
- **Overall program performance:** measurable business impact; success is defined by specific KPIs and usage rate; significant positive outcomes and user ratings; consistent positive trend over time; reported regularly to senior leadership.
- **Customer behavior & engagement:** higher AOV (basket size); high retention/less churn; high program engagement; active members/active reward redemption; positive behavioral change; higher purchase frequency; tier progression and engagement depth; higher CLV/CLV uplift.
- **Adoption & penetration:** high adoption rate; penetration rate; 30% penetration rate; every second customer wants to join; increase in the number of members; all/most users engage with the program.
- **Customer experience & perception:** higher NPS; gratitude; positive customer feedback; customer satisfaction with benefits; improved brand perception; customers are excited about the program and take pride in it; customer awareness of the business behind the program.

#### Expert view: when would your main business owner consider your loyalty program a success?

> "Success means achieving revenue growth that exceeds inflation and delivers real business growth."
>
> — Teodor Gurgui, Mobile & Loyalty Product Manager. Industry experience: Carrefour, Centrade Cheil.

> "When the loyalty program supports the profitable growth of the overall business and contributes to broader business performance."
>
> — Jakub Krzakala, Europe & North America Loyalty & CRM Lead. Industry experience: Shell, Foot Locker Europe, PAYBACK.

> "For our hotel booking platform, success is defined by driving repeat bookings and customer retention. Key criteria include a measurable increase in Customer Lifetime Value (CLV) and achieving a frequency of at least two bookings per year per active member."
>
> — Agnieszka Obstarczyk, Head of CRM & Marketing Automation. Industry experience: Triverna, Total Experience, Positive User, Cavatina Holding.

### Success metrics

*Survey question: Improvement of which business metric reflects the success of your loyalty program?*

#### Success metrics tracked

| Metric | Share of respondents |
| --- | --- |
| Engagement | 55.4% |
| Financial Impact | 53.6% |
| Retention | 44.6% |
| Customer Value | 30.4% |
| Acquisition | 10.7% |
| Growth | 10.7% |
| Redemption | 10.7% |
| Share of Wallet | 8.9% |
| Satisfaction | 7.1% |
| Advocacy | 5.4% |
| Rewards Utilization | 5.4% |
| Operations | 3.6% |
| Referrals | 3.6% |
| Rewards | 3.6% |
| Loyalty Penetration | 1.8% |

**Track engagement and business impact together.** Engagement and Financial Impact are the two most common metric groups, mentioned by 55.4% and 53.6% of respondents respectively. Teams want to see both whether members are active and whether that activity translates into commercial results.

**Look beyond activity to retention and value.** Retention is tracked by 44.6% of respondents, while 30.4% track Customer Value. Programs are looking beyond immediate activity toward repeat behavior, longer customer relationships, and stronger customer economics.

**Watch whether the program keeps growing.** Growth and Acquisition are each mentioned by 10.7% of respondents. Program expansion is tracked less often than engagement, financial impact, or retention, putting more emphasis on the performance of the existing member base.

**Don't lose sight of the member experience.** Redemption appears in 10.7% of responses, Satisfaction in 7.1%, and Advocacy and Rewards Utilization in 5.4% each. These metrics add more detail on how members interact with the program and respond to the experience, though they rarely sit at the center of the measurement framework.

#### Success metrics: differences by industry

The most frequently mentioned success metrics within each industry group.

| Industry | Most frequently mentioned success metrics |
| --- | --- |
| Retail | Financial impact, Retention, Engagement, Customer value |
| Technology | Financial impact, Engagement |
| Hospitality, Travel and Airlines | Financial impact, Retention, Engagement |
| Financial Services and Insurance | Retention, Engagement, Advocacy/referral |
| Restaurants/QSR | Financial impact |
| Consumer Goods/FMCG/CPG | Financial impact, Retention, Engagement, Customer value, Redemption |
| Energy/Telecom/Utilities | Financial impact, Engagement |
| Services | Retention |
| Health, Beauty and Pharma | Financial impact, Customer value |
| Gaming/Entertainment* | Financial impact, Retention, Engagement |
| Sports and Fitness* | Financial impact, Engagement, Growth |

*Industry sample sizes vary significantly, so results should be read as directional patterns rather than representative industry benchmarks.*

#### Expert view: which business metric reflects the success of your loyalty program?

> "Incremental margin and the number of active customers in the program at any given time are the key measures of success."
>
> — Jakub Krzakala, Europe & North America Loyalty & CRM Lead. Industry experience: Shell, Foot Locker Europe, PAYBACK.

> "Our primary metric is Customer Lifetime Value (CLV). Secondary metrics include loyalty channel revenue share, Average Order Value (AOV), loyalty database growth rate, reduced database churn, and loyalty channel traffic."
>
> — Agnieszka Obstarczyk, Head of CRM & Marketing Automation. Industry experience: Triverna, Total Experience, Positive User, Cavatina Holding.

### Expected results for the near future

*Survey question: What level of improvement of the metrics would you like to see in the next year?*

#### Expected improvements

| Improvement area | Share of respondents |
| --- | --- |
| Engagement/Participation | 41.1% |
| Revenue/Sales Growth | 25% |
| General Improvement | 21.4% |
| Membership/Acquisition Growth | 19.6% |
| Retention/Churn | 19.6% |
| CLV/LTV | 14.3% |
| Strategic Goals/Maturity | 10.7% |
| NPS/CSAT/Satisfaction | 7.1% |
| No Target/Not Specified | 7.1% |
| Personalization/Experience | 5.4% |
| Redemption/Burn Rate | 5.4% |

**Participation becomes the growth engine.** Engagement/Participation is the most common improvement area, mentioned by 41.1% of respondents. Teams want members to participate more often through higher activity, purchase frequency, and stronger program usage.

**Revenue comes next.** Revenue/Sales Growth is mentioned by 25% of respondents, making it the second most common priority. The expectation is that stronger engagement should translate into higher revenue, spend, or sales contribution.

**Many goals are still loosely defined.** General Improvement appears in 21.4% of responses, while 7.1% give no specific target. That makes progress harder to benchmark and leaves more room for interpretation around what "improvement" actually means.

**Retention and growth form the next layer.** Membership/Acquisition Growth and Retention/Churn are each mentioned by 19.6% of respondents, while CLV/LTV appears in 14.3%. Teams are balancing member-base growth with keeping customers for longer and increasing long-term value.

#### Only half of loyalty teams put a number on improvement

Direction is useful for setting strategy. A number is what makes progress measurable. Without an explicit numeric target, teams can show that engagement, revenue or retention improved, but it becomes harder to judge whether the program delivered what the business expected.

| | Share | Definition | Examples |
| --- | --- | --- | --- |
| Set an explicit numeric improvement target | **51.8%** | Respondents gave at least one quantified goal, such as a percentage uplift, percentage-point change, multiplier, or absolute business target. | 15–20% purchase frequency; 20–30% member adoption; >50% active redemption; 5× MAU; 8M incremental GMV |
| Described improvement directionally | **48.2%** | Respondents described the direction of improvement without defining a specific numerical threshold. | More engagement; higher purchase frequency; more sign-ups/paid membership; higher retention and referrals; more personalized rewards |

*Directional responses shouldn't be read as evidence that no numeric targets exist internally. Some respondents may simply have chosen not to disclose commercially sensitive KPIs or exact growth expectations.*

#### The loyalty growth stack

Expected improvements can be grouped into four connected layers, from activating participation to building longer-term customer value.

| Layer | Improvement area | Goal |
| --- | --- | --- |
| 1. Activate | Engagement/Participation | Create active member behavior |
| 2. Grow | Membership/Acquisition | Expand program reach |
| 3. Monetize | Revenue/Sales Growth | Turn activity into business impact |
| 4. Compound value | Retention/CLV | Build longer-term customer value |

#### Expert view: what level of improvement would you like to see in 2027?

> "I'd target a 10–15% improvement in retention and purchase frequency, 10%+ growth in share of wallet, and a meaningful uplift in advocacy, while ensuring incremental revenue clearly outpaces loyalty investment."
>
> — Dilpreet Singh, Head – Loyalty & CRM. Industry experience: ITC Hotels Limited, American Express, Dominos – Jubilant Food Works Ltd, The Oberoi Group.

> "Don't be too generous with your rewards. One of our clients halved what their loyalty program offered and got zero negative feedback from customers, saving a considerable amount of money. The recommendation to cut the rewards came from us."
>
> — Mike Walder, Head of Loyalty. Industry experience: Avolut, Allink, Positive User, Cavatina Holding.

### Key challenges

*Survey question: What are your biggest challenges when it comes to implementing a successful loyalty program in your organization?*

#### Challenge categories

| Challenge | Share of respondents |
| --- | --- |
| Integration | 28.6% |
| Strategy | 26.8% |
| Data | 25% |
| Org alignment | 23.2% |
| Tech | 23.2% |
| Personalization | 12.5% |
| Resources | 12.5% |
| Culture | 10.7% |
| Engagement | 10.7% |
| Competition | 10.7% |
| Costs | 10.7% |
| Adoption | 8.9% |
| Rewards | 7.1% |
| Customer needs | 7.1% |
| Communication | 7.1% |

**Integration creates the most friction.** Connecting loyalty with the wider tech stack is mentioned by 28.6% of respondents, while 23.2% point to Tech. The recurring issue is how loyalty connects with CRM, POS, ecommerce, apps, and customer data across the stack.

**Strategy is still being worked out.** Program direction appears in 26.8% of responses, making it one of the most common themes. Teams point to questions around priorities, program design, and how loyalty should support broader business goals.

**Data and alignment slow teams down.** Data appears in 25% of responses, while Org alignment is mentioned by 23.2%. Both can make targeting, measurement, decision-making, and rollout harder to manage across teams.

**Pressure comes from several directions.** Personalization and Resources are each mentioned by 12.5% of respondents. Culture, Engagement, Competition, and Costs each appear in 10.7%. The spread shows that teams are often dealing with several operational and customer-facing constraints at the same time.

#### Challenges: differences by industry

The most frequently mentioned challenges within each industry group.

| Industry | Most frequently mentioned challenges |
| --- | --- |
| Retail | Integration, Data, Org alignment |
| Technology | Strategy |
| Hospitality, Travel and Airlines | Integration |
| Restaurants/QSR | Org alignment |
| Consumer Goods/FMCG/CPG | Tech |
| Energy/Telecom/Utilities | Tech, Competition |
| Services | Innovation, Customer needs |
| Health, Beauty and Pharma | Tech, Resources |
| Gaming/Entertainment | Engagement, Adoption |
| Sports and Fitness | Org alignment, Innovation |

*Industry sample sizes vary significantly, so results should be read as directional patterns rather than representative industry benchmarks.*

#### What sits behind the biggest loyalty challenges

| Challenge area | What respondents are dealing with | Respondent proof | Open Loyalty read |
| --- | --- | --- | --- |
| Technology and integration | Legacy architecture, disconnected systems, and difficulty connecting loyalty with wider business infrastructure. | "Technology and IT Architecture isn't in place. Also we have many projects that are or should start which have an interlink to loyalty"; "Systems and AI integration" | The platform is rarely the whole issue: programs depend on how well loyalty connects with CRM, POS, eCommerce, and customer data. |
| Data and personalization | Fragmented customer data, inconsistent identifiers, and difficulty turning data into relevant experiences. | "The main challenges are technical integration with current systems, limited internal team capacity, and collecting clean data to create personalized customer offers."; "Data has been an issue - use of different email addresses, etc" | Personalization hits a ceiling when customer data is fragmented. AI-driven decisioning inherits it. |
| Resources and capacity | Limited time and internal capacity to continuously develop, optimize, and analyze the program. | "time of team to prepare more mechanics/activations/features/analytics"; "The resources required for the programme's ongoing optimisation and technical development" | Loyalty is an ongoing product. Limited capacity slows continuous improvement even when the strategy is clear. |
| Organizational alignment | Coordinating Marketing, IT, Finance, Operations, and other stakeholders around shared priorities. | "Aligning cross-functional teams (Marketing, Finance, and IT) to adopt new agile processes and fully utilize enterprise platforms."; "Getting everyone internally onboarded" | Many loyalty blockers sit inside the organization. Clear ownership and faster cross-functional decision-making matter as much as technology. |
| Adoption and relevance | Getting customers interested, reducing drop-off, and making the program meaningful across touchpoints. | "To find the proper motivation for customers to be attracted to it"; "brand awareness of loyalty program"; "high drop off rate" | A technically sound program can still underperform if customers do not understand its value or stay engaged after joining. |
| Budget, costs and commercial pressure | Investment constraints, legal costs, point liability, fraud, and the ongoing cost of operating the program. | "tech and investment"; "liability on outstanding points"; "At the moment it's Loyalty Fraud and Franchisees Support." | Loyalty economics go beyond rewards: investment, operating costs, liabilities, and fraud all shape how fast a program evolves. |

#### Seriousness of challenges

**1. The connected-stack bottleneck. Seriousness: 5/5, critical.**
- What's happening: Loyalty depends on CRM, POS, ecommerce, customer data and other systems working together. When they don't, personalization, measurement and program development slow down.
- Why it matters: Fragmented infrastructure makes data harder to use, customer experiences less consistent and even simple changes more difficult to deliver.
- Expert view: Connected architecture and reliable data flows are becoming baseline requirements for loyalty teams that want to test, personalize and scale faster.

**2. The capacity and alignment squeeze. Seriousness: 4/5, high.**
- What's happening: Loyalty increasingly spans Marketing, IT, Finance, Operations and other teams, while ownership and delivery capacity are often spread across the organization.
- Why it matters: Strong strategies lose momentum when responsibilities are unclear, decisions are slow or teams lack capacity to keep improving the program.
- Expert view: Clear ownership, shared priorities and dedicated capacity are becoming as important as the loyalty strategy itself.

**3. The loyalty economics squeeze. Seriousness: 3/5, moderate.**
- What's happening: Loyalty teams face pressure from investment costs, reward economics, point liabilities, fraud and ongoing development.
- Why it matters: When the economics are unclear, programs become harder to defend internally and short-term cost pressure can limit experimentation.
- Expert view: Loyalty performance needs to be assessed across incremental revenue, customer value, liabilities and operating costs (not only the rewards budget).

**4. The relevance gap. Seriousness: 3/5, moderate.**
- What's happening: Getting customers to join is only part of the job. Programs also need to stay visible, relevant and easy to use across the customer journey.
- Why it matters: Weak onboarding, generic experiences or redemption friction can quickly turn membership into inactivity.
- Expert view: Sustained engagement comes from the whole experience: clear value, relevant interactions, consistent touchpoints and low-friction participation.

#### Expert view: challenges and lessons learned

> "The biggest challenge is moving from transaction-led loyalty to relationship-led loyalty: breaking down organizational silos, integrating data and technology, reducing customer friction, personalization at scale, and consistently delivering experiences that build emotional connection, community, and advocacy, not just points and rewards."
>
> — Dilpreet Singh, Head – Loyalty & CRM, on the biggest challenges in implementing a successful loyalty program. Industry experience: ITC Hotels Limited, American Express, Dominos – Jubilant Food Works Ltd, The Oberoi Group.

> "I once built a business case for a client, and the honest conclusion was that a loyalty program didn't make sense for their business. I was disappointed, since I'd been looking forward to designing it, but I'd saved the client a lot of money and a lot of headaches. Sometimes the business case tells you to stop, and you have to listen."
>
> — Pavel Los, Customer Loyalty Consultant, sharing a lesson learned from a recent project. Industry experience: Shell, New World Loyalty.

---

## 5. 2026 vs 2027 survey results

*A directional view of what is changing in loyalty strategy.*

### Loyalty success is moving closer to the P&L

The 2027 definition of success puts more weight on customer and commercial outcomes.

Engagement remains a core operating signal, but the 2027 responses put more emphasis on its downstream effect. Activity matters when it contributes to retention, customer value, incremental revenue and a stronger business case for loyalty.

| 2026 | 2027 |
| --- | --- |
| Engagement, retention and revenue sat relatively close together as the main signals of success. | Retention and revenue come through more strongly in how practitioners define success. |
| Program activity (active members, participation and usage) was often treated as proof that loyalty was working. | Engagement is more often treated as an operating signal that should lead to a wider business outcome. |
| ROI was one of several ways to demonstrate value. | Incremental revenue, CLV, profitability, share of wallet and member-vs-non-member uplift appear more explicitly in the language of success. |

### Core outcomes remain. The levers are becoming more explicit

Member behavior and business impact are connected more clearly in the 2027 responses.

The priorities themselves are familiar. What is clearer in 2027 is the performance logic behind them: activate members, influence purchase behavior, retain customers for longer, and convert that change into higher CLV and measurable commercial impact.

**2026: track program performance**

- Grow revenue and sales contribution
- Increase engagement and participation
- Improve retention
- Expand the member base
- Prove ROI and customer value

**2027: connect behavior to business impact**

- Activate more members
- Increase purchase frequency
- Reduce churn and strengthen retention
- Grow CLV and share of wallet
- Translate member activity into incremental revenue

### The technology problem is becoming an architecture problem

Execution barriers are described in much more specific terms in the 2027 responses.

Technology is increasingly an architecture and operating-model question. A capable loyalty engine is only one part of the setup; fragmented identity, weak data flows, slow integrations, and cross-functional dependencies still determine how quickly teams can launch, test, personalize and scale.

| 2026 | 2027 |
| --- | --- |
| Technology was often described as a broad limitation. | The language becomes more specific: integration, data structure, IT architecture and platform utilization. |
| Budget and proving ROI were prominent constraints. | Responses give more detail on delivery capacity, resources and cross-functional coordination. |
| Organizational alignment was already a recurring challenge. | Alignment is often connected with technology, with Marketing, IT, Finance and data teams involved in the same loyalty setup. |
| Personalization was often discussed as an ambition constrained by technology. | Personalization is more explicitly tied to clean data, integration and customer understanding. |

---
## 6. Market pulse

*What loyalty teams are prioritizing, what challenges they face, and how they plan to address them.*

We analyzed responses from loyalty professionals representing 150+ companies to understand what is shaping loyalty priorities over the next 12 months. The analysis covers three connected areas: what teams want to improve, which challenges are getting in the way, and which solutions they are actively evaluating.

- 210 responses reviewed
- 150+ companies represented
- 88.6% budget holders or strong influencers

*Results are reported at respondent level. The sample is predominantly European and includes multiple respondents from some companies. This dataset is analyzed separately from the annual benchmark survey, and all percentages in this section refer only to this source. Respondents could select multiple areas, so percentages do not sum to 100%.*

### Where loyalty teams are focusing next

| Area of focus | Share of respondents |
| --- | --- |
| Retention and repeat purchase | 68.1% |
| Customer lifetime value growth | 63.8% |
| Personalization and offer relevance | 56.2% |
| Customer engagement and activity | 47.2% |
| Incremental revenue and spend growth | 29% |
| Loyalty program launch and scale | 23.8% |
| First-party data growth | 14.3% |

**Retention is the commercial anchor.** Retention and repeat purchase leads the agenda at 68.1%, with customer lifetime value growth close behind at 63.8%, while customer engagement and activity sits lower at 47.1%. Teams appear to expect loyalty to influence customer economics. Engagement KPIs should sit in the same measurement model as repeat purchase, retention and CLV, so teams can show what that activity actually creates.

**Personalization needs to prove behavior change.** Personalization and offer relevance is a focus for 56.2% of respondents, making it one of the strongest near-term priorities. But personalization should be treated as a lever. The useful benchmark is how many personalized experiences a program delivers and if those decisions improve frequency, retention, spend or customer value.

**The emphasis is on getting more value from the customer relationship.** Loyalty program launch and scale sits at 23.8%, while first-party data growth is at 14.3%, well below retention, CLV and relevance. For established programs, optimizing the behavior and economics of the existing member base may create more value than adding another layer of reach.

### Areas of focus: differences by company size

Retention and customer value cut across company sizes. Large-company respondents put more emphasis on personalization and offer relevance, while customer engagement and activity appears among the leading areas for medium companies. The small-company subgroup is too limited to support a strong comparative conclusion.

| Company size | Share of respondents | Example companies | Most common areas of focus |
| --- | --- | --- | --- |
| Large (global/national brands) | 78.6% | IKEA, ASICS, adidas, Decathlon, Lidl, TUI, ING, LEGO, Sainsbury's, Samsung | Retention and repeat purchase 69.1%; Customer lifetime value growth 63%; Personalization and offer relevance 61.2% |
| Medium (regional/mid-market) | 17.6% | MR MARVIS, Bloom & Wild, Paula's Choice, HolidayCheck, PhotoSì, Sunweb Group, Moonpig, Freeletics | Retention and repeat purchase 70.3%; Customer lifetime value growth 70.3%; Customer engagement and activity 45.9% |
| Small (startups/SMBs) | 3.8% | Little Wonderland, LW Cosmetics, O My Bag Amsterdam, Triverna, Reviva Softworks, Massima Energia | Customer lifetime value growth 50%; Retention and repeat purchase 37.5%; Personalization and offer relevance 37.5%; Loyalty program launch and scale 37.5% |

### Where loyalty execution breaks down

| Challenge | Share of respondents |
| --- | --- |
| Sustained member engagement | 45.7% |
| Scalable personalization | 45.7% |
| ROI and commercial proof | 31.9% |
| Loyalty stack integration | 21% |
| Ownership and stakeholder alignment | 21% |
| Customer data and identity | 20% |
| Budget and delivery capacity | 19% |
| Program differentiation | 13.3% |

**Execution is a bigger constraint than program design.** Sustained member engagement and scalable personalization both reach 45.7%, and ROI and commercial proof follows at 31.9%, while program differentiation sits at just 13.3%. Teams appear to struggle less with defining what a loyalty proposition should look like and more with making it work consistently. Adding more mechanics won't solve an execution problem if the existing ones cannot be personalized, measured or scaled.

**Personalization at scale depends on the stack around loyalty.** The challenge does not stop at campaign design. Loyalty stack integration is cited by 21%, customer data and identity by 20%, and budget and delivery capacity by 19%. Before increasing targeting complexity, teams should check if identity resolution, data availability and integrations can actually support real-time or high-frequency decisioning. Otherwise personalization simply creates more operational work.

**Ownership is part of the architecture.** Ownership and stakeholder alignment appears in 21% of responses, putting organizational friction alongside technology and data issues. Loyalty rarely sits inside one system or one team anymore. A clear operating model across CRM, marketing, data, IT and commercial teams is becoming as important as the platform itself.

**ROI needs to be designed into the program.** Commercial proof is a challenge for 31.9% of respondents. That is too high to treat measurement as a reporting problem at the end of the process. Teams need incrementality logic, agreed commercial KPIs and a clear way to connect member behavior with business impact before launching the next initiative.

### Challenges: differences by program size

| Program size | Share of respondents | Example companies | Most common challenges |
| --- | --- | --- | --- |
| Large (global/national brands) | 78.6% | IKEA, ASICS, adidas, Decathlon, Lidl, TUI, ING, LEGO, Sainsbury's, Samsung | Scalable personalization 49.1%; Sustained member engagement 48.5%; ROI and commercial proof 31.5% |
| Medium (regional/mid-market) | 17.6% | MR MARVIS, Bloom & Wild, Paula's Choice, HolidayCheck, PhotoSì, Sunweb Group, Moonpig, Freeletics | ROI and commercial proof 37.8%; Scalable personalization 35.1%; Sustained member engagement 32.4% |
| Small (startups/SMBs) | 3.8% | Little Wonderland, LW Cosmetics, O My Bag Amsterdam, Triverna, Reviva Softworks, Massima Energia | Sustained member engagement 50%; Customer data and identity 37.5%; Loyalty stack integration 25%; Scalable personalization 25% |

### Which capabilities loyalty teams are exploring

| Capability | Share of respondents |
| --- | --- |
| Personalization and decisioning | 46.7% |
| AI-enabled loyalty and automation | 45.7% |
| Engagement mechanics and gamification | 42.9% |
| Rewards and benefit operations | 37.1% |
| Omnichannel engagement | 27.1% |
| Loyalty management technology | 22.9% |
| Predictive analytics and measurement | 21.4% |
| No active solution evaluation | 13.8% |
| Customer data and identity infrastructure | 12.9% |
| Payments-linked loyalty | 11.4% |

13.8% of respondents aren't currently evaluating loyalty solutions at all.

### Where loyalty teams look for solutions

**AI interest is being pulled by a real operating constraint.** Personalization and decisioning leads the evaluation list at 46.7%, with AI-enabled loyalty and automation almost level at 45.7%. At the same time, scalable personalization is one of the biggest current challenges, also at 45.7%. The market is looking for a way to make relevance work at a volume manual campaign operations cannot reach. The strongest use cases will be the ones tied to an existing constraint: next-best action, churn intervention, reward selection or offer relevance.

**Gamification is almost level with AI for a reason.** Engagement mechanics and gamification reaches 42.9%, only 2.8 percentage points behind AI-enabled loyalty and automation. Meanwhile, sustained member engagement is a challenge for 45.7%. That makes gamification less of a decorative feature and more of a behavioral tool: AI can improve which action is relevant, while mechanics such as missions, progression or challenges give teams a way to turn that decision into member behavior.

**The capability stack is more advanced at the top than at the bottom.** AI and personalization attract substantially more attention than predictive analytics and measurement at 21.4% or customer data and identity infrastructure at 12.9%. Advanced decisioning layered onto fragmented identity or weak measurement will simply automate poor inputs faster. Teams should treat data, identity and feedback loops as part of the AI investment.

**Orchestration matters more than collecting features.** Rewards and benefit operations still attracts 37.1%, while omnichannel engagement reaches 27.1%. The market is still evaluating several layers of the loyalty stack at once. The stronger setup is a connected flow from customer data, decisioning, and then mechanics, delivery, and measurement.

---

## 7. Loyalty in practice

*What 500+ industry conversations reveal about real-world loyalty implementation.*

### Recurring themes

We reviewed recurring themes from loyalty conversations to understand what organizations deal with when launching, replacing or expanding their loyalty setup. These conversations show the issues teams raise when strategy turns into an implementation decision.

- 504 industry conversations reviewed
- 1,500+ hours of loyalty conversations

| Program stage | Share of conversations |
| --- | --- |
| Exploring a new loyalty ecosystem | 62% |
| Replacing an existing loyalty ecosystem | 29% |
| Expanding an existing loyalty ecosystem | 8% |

### Three loyalty journeys, three different problems

*Percentages show the share of company conversations where the theme was raised. Multiple themes could be raised in one conversation, so percentages do not sum to 100%.*

| Program stage | Core question | Most common issues | What changes at this stage | Our take |
| --- | --- | --- | --- | --- |
| Launching | How do we get this into market? | 50.4% integration complexity; 36.5% no platform/legacy limitations; 35% budget and ROI justification | Readiness is the constraint. Before teams can optimize loyalty mechanics, they need enough infrastructure, delivery capacity and internal commitment to get the program live. | Integration mapping, ownership and MVP scope should be settled early, otherwise time-to-market becomes a technology problem rather than a loyalty one. |
| Replacing | How do we migrate without carrying old constraints forward? | 81.2% current-platform/legacy limitations; 56.2% integration complexity; 34.4% budget and ROI justification | Replacement is primarily an architecture and migration decision. The strongest signal in the whole stage analysis is the 81.2% citing limitations in the existing setup. | Define the target architecture and migration path before comparing feature lists. A replacement only creates value if customer data, loyalty logic and integrations move without recreating the same constraints. |
| Expanding | How do we scale without adding more complexity? | 47.1% current-platform/legacy limitations; 41.2% multi-brand fragmentation; 35.3% integration complexity | Scale turns loyalty into an orchestration problem. More brands, markets and use cases increase the cost of fragmentation. | Expansion needs a shared loyalty core with enough local flexibility for different propositions, while customer identity, data and measurement remain consistent across the ecosystem. |

### Expert advice by program stage

#### Paweł Dziadkowiec, Open Loyalty expert

**Companies launching a loyalty program:** "Before investing in technology, define the program's business goals, for example increasing retention, visit frequency or average order value. Analyze your customer base, ideally using existing segmentation, to match mechanics and appealing rewards to specific customer groups. Compare costs and point liabilities against projected revenues to calculate ROI accurately. Once approved, launch with a simple MVP model to test customer response, and introduce more complex rules step by step."

**Companies replacing an existing program:** "Start with a solid diagnosis of your current program to pinpoint why it failed, whether due to outdated technology, unappealing rewards or overly complex rules. Before designing new mechanics, reward lists or gamification, gather feedback from customers, especially the high-value segments who will feel the changes most. Ensure a stress-free migration by guaranteeing a smooth transfer of existing points and earned statuses."

**Companies expanding an active program:** "In the coming years, brands that can accurately predict customer needs will gain the edge. The key direction is moving away from mass communication toward smart solutions that deliver the right value at the exact right moment in the buying journey. In a world of growing privacy protection, a well-designed program becomes the company's main marketing engine. Its foundation is trust, where customers willingly share their data and preferences in exchange for personalized benefits."

#### Sheryl-Lynn Omar, Loyalty, Rewards & Gamification expert

**Companies launching a loyalty program:** "Start with the behavior you want to drive or change, not the benefit you want to offer, and be careful not to reward behavior that would have happened anyway. Challenge the customer journey with honest friction audits and hard questions, because no incentive can fix a broken journey. Invest in a dedicated data and analytics capability – find someone who can spot shifts in habit and translate them into commercial impact, not just someone who knows how to automate personalized, real-time rewards. And once the design is approved, keep protecting it: the small touches, animations, pop-ups, and playful interactions, are often the first things cut under time pressure, and that "fun layer" is functional, not just decoration."

**Companies replacing an existing program:** "From a business perspective, design for the customer you'll have in the future, not only for the one you have today. Fixing what already exists isn't enough. From the customer's perspective, it's about value: make it visible at the right touchpoints throughout the journey, and let members feel it early after the change. Pair that with steady education and communication that reinforces what's new, explains the value, and helps customers build new habits around the program."

**Companies expanding an active program:** "AI and gamification top my list here, but two less glamorous drivers matter just as much: time and passion. Go back to the beginning of the program and reassess the behaviors you're trying to drive, understand what barriers still stand in the way, and design interventions you can actually measure, keeping the approach simple and effective."

#### Pavel Los, Customer Loyalty Consultant

**Companies launching a loyalty program:** "Spend more time with the people who'll have to explain the offer to customers. Ask someone working a busy shift to walk you through the benefit in their own words, then walk through what happens when a reward is missing or a customer is unhappy. Build staff training and support into the budget from day one: their experience of running the program becomes part of the customer's experience."

**Companies replacing an existing program:** "Look closely at what running the new system will actually involve for your team. Ask a vendor to show you how someone changes an earning rule, processes a refund, or corrects a member's balance, who does that work, and where the extra charges start. A demo only shows you the feature, not a normal working week. And be honest about where your current problems come from: poor data or slow approvals won't disappear just because you bought a new platform."

**Companies expanding an active program:** "Test whether AI is actually improving decisions about your reward budget, not just automating them. If a customer already buys from you every Friday and you send a Thursday discount they'd have used anyway, you've given away margin rather than changed behavior. Use a control group and include the cost of the reward when you measure the result."

#### Bartosz Żochowski, Head of Loyalty & CRM

**Companies launching a loyalty program:** "Start with two questions: why do you actually need this program, and what should it change in your organization? Get that answer right at the start, or an undefined goal will resurface later and the program gets written off as a cost center. It also needs to stay consistent with your brand and its values, not live as a separate product."

**Companies replacing an existing program:** "Rebuild starts with an honest audit: decide what to keep and what to cut. If customers love certain elements, carry them into the new version; if something isn't working, don't keep it out of sentiment. Then, let data decide, not guesswork: check what your transactional and behavioral data says, and ask members directly what they need."

**Companies expanding an active program:** "Growth in 2027 rests on four things: treating new and loyal customers differently, a customer journey personalized enough to respond to real behavior, gamification tailored to your audience, and quality over quantity. Three clear, powerful benefits will always outperform ten average promises."

#### Luke Galvin, Senior Manager – Loyalty Strategist, Deloitte LLP

**Companies launching a loyalty program:** "Start with an objective assessment of commercial performance. Find out which behaviors are incremental, which benefits customers' value, and where the program creates cost without differentiation. Address existing liabilities, data, partnerships, and customer expectations before redesign begins. Members see accumulated points, status, and benefits as earned value, so any migration has to be transparent, equitable, and phased. Remove mechanics that no longer influence meaningful behavior. A new platform or a refreshed rewards catalog won't be enough on its own: align the customer proposition, financial model, operating model, and technology architecture around a clear purpose, and protect the trust and accumulated value of existing members."

**Companies replacing an existing program:** "For 2027, invest in real-time data, AI-enabled decisioning, and consistent orchestration across every customer interaction. The aim is relevance at the moment of choice. Connect identity, commerce, payments, and service so recognition travels with the customer across channels, and strengthen consent, measurement, and governance as personalization becomes more automated. Plan for AI agents too. They play a growing role in discovery, comparison, and purchasing, so your proposition has to be understandable to the systems acting on customers' behalf. That takes structured product and benefit information, interoperable customer identity, and real-time decisioning, alongside the emotional and experiential relevance customers still value."

**Companies expanding an active program:** "A company set out to transform its loyalty offer by adding numerous tiers, earning rules, and partner benefits to maximize customer choice. In practice, customers found the proposition hard to understand and colleagues found it hard to explain. Engagement fell short because the value wasn't immediately recognizable. The program was later simplified around fewer meaningful benefits and clearer progression. The lesson was that perceived value matters more than the volume of features, and that simplicity comes from disciplined proposition design."

### What companies talk about when defining loyalty impact

How loyalty impact is framed once program objectives move from strategy into execution (share of industry conversations).

| Outcome | Share of conversations | What it covers |
| --- | --- | --- |
| Engagement | 44.8% | App usage, participation and active customer behavior. Engagement is the most common signal because it shows whether the program is actually changing day-to-day customer behavior. |
| Retention | 32.1% | Keeping customers active and reducing churn. Retention turns loyalty from an activity metric into a relationship metric. The real test is whether the program gives customers more reasons to stay. |
| Purchase frequency | 30.8% | Encouraging customers to return more often. Purchase frequency is where engagement starts to become commercially meaningful. More interaction only matters if it changes how often customers come back and buy. |

Other outcomes raised: referrals 19%, first-party data 18.1%, acquisition 17.6%, revenue 15.4%, AOV 14%, CLV/LTV 10%.

**Customer behavior is the first proof point of loyalty impact.** Engagement, retention and purchase frequency are raised more often than direct financial outcomes. That suggests implementation conversations are usually framed around which customer behavior loyalty should change first, before teams look at the downstream effect on revenue, AOV or CLV.

The strongest measurement model connects both levels. Behavior tells you whether loyalty is working; commercial metrics tell you whether that behavior is worth creating.

---

## 8. Strategic recommendations from Open Loyalty experts

### Recommendation #1: Make integration a competitive capability

**Why it matters.** The old architecture playbook is getting outdated. Loyalty teams still need API-first systems that are easy to connect and build on, but complex custom integrations bring their own development and maintenance load. For enterprise programs, the bigger question is how much IT capacity it takes to integrate, customize, and maintain the loyalty setup.

**What to do.** Design the architecture for low, repeatable integration effort while keeping the option to replace or extend individual components. Choose a platform that handles common tasks such as data sync and exchange through APIs, so IT can focus on the use cases that need customization.

**Prioritize:**
- API-first architecture that is easy to connect and build on
- Built-in synchronization and data exchange for common tasks
- One customer identity across channels
- Components you can replace or extend without touching the core
- IT time spent on use cases that need customization

> **Watch out!** "The days when composable architecture itself was a competitive advantage are largely behind us. The heavily promoted MACH approach is giving way to a more pragmatic model: minimizing integration effort while keeping components replaceable and scalable. The balance is shifting toward architectures that are easier to maintain, require less development time and fewer resources, and allow teams to add or replace platform components without unnecessary complexity. API-first architecture, AI and MCP can push this shift further. Configuration work that previously required custom code can increasingly be handled by AI agents using MCP, allowing a single instruction to coordinate actions across multiple platforms to achieve a business outcome."
>
> — Cezary Olejarczyk, Open Loyalty CEO

### Recommendation #2: Launch smaller. Learn faster. Expand later.

**Why it matters.** Loyalty programs often become over-engineered before they reach customers. Trying to launch the full long-term vision at once increases delivery risk, slows time-to-market and makes it harder to understand which mechanics are actually changing behavior.

**What to do.** Start with the smallest version of the program that can influence the target behavior, then expand based on evidence.

**Build in phases:**
1. Core earning + one meaningful redemption loop
2. Segmentation + targeted campaigns
3. Tiers, challenges, referrals, partners or gamification
4. Deeper personalization and optimization

> **Watch out!** "Launching every feature at once hides what actually drives loyalty versus what just burns your margin. An iterative approach builds core customer habits while setting a clean baseline for ROI. On that foundation, you can safely and profitably add new layers."
>
> — Paweł Dziadkowiec, Open Loyalty expert

**Where implementation friction comes from** (share of industry conversations where the theme was raised):

| Theme | Share of conversations | What it covers | What it looks like in practice |
| --- | --- | --- | --- |
| Architecture | 73.8% | How loyalty connects with the wider technology stack | Difficult integrations, legacy platform constraints, systems that cannot exchange loyalty data cleanly |
| Delivery | 57% | Whether teams have the capacity to build and change the program | Tight launch timelines, limited development resources, missing app or frontend capacity |
| Fragmentation | 44.3% | Whether customer data and loyalty operations work consistently across the organization | Disconnected customer data, separate setups across brands or markets, duplicated processes |
| Economics | 33.5% | Whether the program can justify its cost and commercial contribution | Budget pressure, difficulty proving ROI, investment competing with other priorities |

> **Watch out!** "Architecture friction appears in 73.8% of conversations, more than twice as often as budget. This is also the gate on everything teams say they want next. Real-time personalization, predictive churn scoring, and AI decisioning all assume customer events move across the stack cleanly. Where they don't, the AI conversation stops at the proof of concept."
>
> — Marcin Dyguda, Head of AI Innovation, Open Loyalty

### Recommendation #3: Prove business impact before asking for more budget

**Why it matters.** Loyalty teams often have plenty of activity metrics, but activity alone does not prove commercial impact. If measurement is added only after launch, it becomes much harder to show whether the program actually changed customer behavior or simply rewarded customers who were already valuable.

**What to do.** Build the measurement model before adding more mechanics. Use three connected layers:
- **Customer behavior:** activity, redemption, frequency, referrals
- **Customer economics:** retention, AOV, CLV, cross-sell
- **Business impact:** incremental revenue, margin, ROI

Where possible, compare members with matched non-members or control groups instead of relying on member revenue alone.

> **Watch out!** "The same rule applies to AI. If personalization or decisioning is added without a control group, you will be able to show that targeted members spend more and you won't be able to show that the targeting caused it."
>
> — Marcin Dyguda, Head of AI Innovation, Open Loyalty

### Recommendation #4: Compete on behavior, not discounts

**Why it matters.** Discount-led loyalty is easy to copy and can train customers to wait for the next incentive. The stronger opportunity is to design mechanics around the specific behaviors the program needs to influence.

**What to do.** Start with the behavior, then choose the mechanic:
- **Increase purchase frequency:** challenges, streaks, milestones
- **Grow category breadth:** cross-category missions, progressive incentives
- **Build advocacy:** referrals, recognition, member status
- **Drive app activity:** non-transactional earning, content, games
- **Improve retention:** lifecycle triggers, personalized reasons to return

> **Watch out!** "Discount-led loyalty only creates bargain hunters, eroding margins without building true brand attachment. Competing on behavior focuses on forming new customer habits, like higher frequency or category expansion, before offering any reward. This protects profitability by ensuring you only pay for actions that generate true incremental value."
>
> — Paweł Dziadkowiec, Open Loyalty expert

### Recommendation #5: Turn loyalty into a first-party data engine

**Why it matters.** Loyalty can do more than reward transactions. It can create a continuous stream of customer signals that improve segmentation, personalization and lifecycle decisions. The challenge is making that data usable across the wider customer stack.

**What to do.** Design the program to capture signals customers are willing to exchange for a better experience:
- **Explicit signals:** preferences, surveys, profile completion
- **Behavioral signals:** app activity, referrals, category interest, reward usage
- **Identity signals:** authenticated purchases and cross-channel identification

Then, connect those signals back into CRM, decisioning and campaign workflows so they actually influence the next customer interaction.

> **Watch out!** "These signals are also what any AI layer runs on. Preference data, behavioral history, and authenticated cross-channel identity are the difference between a model that predicts what a member wants next and one that guesses from last week's transaction. Programs that collect these deliberately end up with an advantage that is difficult to buy."
>
> — Marcin Dyguda, Head of AI Innovation, Open Loyalty

### Recommendation #6: Design the program for change

**Why it matters.** The problems loyalty teams face change as programs mature. What works for launch can become a constraint when the program expands across more markets, brands, channels and use cases.

**What to do.** Build flexibility into the program from the start:
- **Organizational scale:** support multiple brands, markets and customer segments
- **Program flexibility:** allow different earn rules, rewards, tiers and currencies
- **Technical flexibility:** change mechanics and integrations without rebuilding the core
- **Data continuity:** keep customer data usable as the surrounding stack evolves

> **Watch out!** "Designing for change is an insurance policy for the organization: it lets you adjust mechanics, markets, and rules without overhauling the core engine. This turns every new business step into a fast adaptation rather than an expensive migration project."
>
> — Paweł Dziadkowiec, Open Loyalty expert

### Recommendation #7: Build the data foundation before the AI layer

**Why it matters.** AI is the second most evaluated capability in this year's market pulse, named by 45.7% of respondents. Personalization at scale is a top challenge for the same share: teams turn to AI because manual personalization has hit its limit.

Architecture friction appears in 73.8% of implementation conversations. Data quality and single customer view is a challenge for 20% of the market pulse sample, while only 12.9% are evaluating customer data platforms and identity resolution.

Interest in AI runs about three and a half times ahead of the identity layer it depends on, and that gap is where most loyalty AI projects will stall. A model does not repair fragmented identity, unreliable event streams, or a program with no control group. It scales whatever it's given, including the errors.

**What to do.** Sequence the work. Treat AI as the fourth step:
1. **Identity first.** One deduplicated customer profile across channels.
2. **Events second.** Purchases, loyalty actions, and channel activity flowing in near real time.
3. **Measurement third.** Control groups and holdouts before AI touches targeting.
4. **Then, apply AI where scale is the actual constraint:** offer selection, next-best action, churn scoring, reward personalization.

> **Watch out!** "The most common loyalty AI failure isn't a bad model. It's a good model connected to customer data that was never unified in the first place."
>
> — Marcin Dyguda, Head of AI Innovation, Open Loyalty

### Recommendation #8: Match the loyalty plan to the program stage

**Why it matters.** Launching, replacing and scaling loyalty create different risks. A new program needs clear goals and a simple MVP. A replacement needs diagnosis, migration safety and customer trust. An active program needs better prediction, personalization and community-driven engagement.

**What to do.** Use the program stage to decide what comes first:
- **No program yet:** define business goals, segment customers, model ROI, launch simple and expand gradually.
- **Changing an existing program:** diagnose what failed, protect points/statuses, communicate benefits clearly and migrate without recreating old constraints.
- **Active program:** move beyond mass campaigns toward timely, personalized value supported by trust, data, gamification and community.

> **Watch out!** "Business goals must evolve as the program grows. Early stages validate unit economics and habits, migration protects existing revenue, and scaling maximizes CLV and share of wallet. Expecting full financial returns too early paralyzes growth, while failing to scale caps your profit potential."
>
> — Paweł Dziadkowiec, Open Loyalty expert

---

## About Open Loyalty

Open Loyalty is the research sponsor of this report: a gamified loyalty platform behind 100+ enterprise brands. Teams use it to connect loyalty with CRM, POS, ecommerce, CDP and marketing tools through an API-first setup, and to run challenges, tiers, rewards and other mechanics that change customer behavior.

- Platform overview: https://www.openloyalty.io
- Talk to loyalty experts: https://link.openloyalty.io/demo

The report was created as part of the Loyalty Builders network, a community of 4,000 loyalty practitioners.

### Report authors

- Weronika Masternak, Loyalty Trends Research Lead
- Kacper Skoczylas, Head of Marketing
- Natalia Chronowska, AI-Native Content Specialist
- Carlos Oliveira, Product Marketing Manager
- Karol Bzik, CPO

© Open Loyalty sp. z o.o.