Key Takeaways
At Activate Summit 2026, leaders from Travelex and Contentsquare shared what they’re learning as they build loyalty strategies around AI, customer data, and changing customer behavior. Their experiences point to five priorities for marketers:
- AI is making it easier for customers to compare products, increasing the importance of differentiation and customer loyalty.
- Clean, connected customer data is the foundation for personalization, lifecycle marketing, and AI.
- Loyalty programs create stronger results when they launch with a focused value proposition and evolve through customer feedback.
- One-to-one personalization remains an aspiration for most organizations because of the data and operational complexity required.
- Long-term loyalty should be measured through customer lifetime value rather than campaign metrics alone.
AI is making products easier to copy and customer attention harder to earn. At the same time, it is giving consumers better tools to compare prices, evaluate alternatives, and switch brands with very little effort. As a result, loyalty has become one of the few durable competitive advantages companies can still build.
That was the central theme of a panel featuring Chris Frost, Head of Product at Travelex, Daniel Agudelo, Senior Marketing Operations Manager at Contentsquare, and Claire Dansie of Iterable. Although Travelex serves consumers and Contentsquare serves enterprise customers, both organizations reached the same conclusion: AI raises the ceiling on customer experience, but only when the underlying data, products, and operating model are already strong.
The discussion moved beyond loyalty programs and AI features. Instead, it focused on the work organizations often overlook: building customer data foundations, setting realistic expectations for personalization, and designing experiences that give customers a reason to return.
Editor’s note: Watch the full Activate Summit 2026 session, “Rethinking Loyalty in the Age of AI,” on demand.
AI Makes Loyalty More Important, Not Less
AI gives marketers more ways to personalize experiences, automate engagement, and identify customer opportunities. It gives customers those same advantages when evaluating brands.
Chris Frost described the shift directly: “Customers are getting very close to perfect knowledge now, because the tools are there to really scan the market in seconds.”
He continued: “Commodity products are going to suffer that comparison. Where you have differentiation in how well you meet your customer needs, AI will help you reach those customers.”
Daniel Agudelo highlighted another challenge: “By implementing AI agents, we are kind of losing some of this data… the challenge is to start understanding how we can use the data generated by the agents to continue creating experiences that can delight them.”
Together, those shifts create a new operating reality:
- AI helps customers evaluate more options with less effort.
- AI reduces some of the behavioral data marketers have historically used.
- Differentiation becomes more valuable as comparison becomes easier.
- Customer relationships depend more heavily on relevance and experience.
The opportunity is significant, but so is the challenge. AI can amplify great customer experiences, yet it also makes weak products and undifferentiated experiences easier for customers to identify.
Takeaway: AI raises expectations on both sides of the relationship. As customers gain better information, loyalty increasingly depends on delivering experiences that are genuinely worth returning to.
Data Foundations Come Before AI
Organizations often begin AI discussions with the technology they want to build.
Chris Frost recommends starting somewhere much less exciting: customer data.
When Frost joined Travelex three years ago, the company had almost no connected customer data. The business had operated for decades as a transactional retailer serving travelers through airports and retail locations, making it difficult to build long-term customer relationships.
The first priority was creating a single customer view by connecting identity, card usage, and transaction data that previously lived across separate systems. Once that foundation existed, Travelex could activate the data through lifecycle marketing and begin delivering more relevant customer experiences.
Frost outlined the progression clearly:
- Identify the customer.
- Build a unified customer profile.
- Connect data across systems.
- Activate that data through lifecycle marketing.
- Measure long-term customer outcomes.
That sequence matters because artificial intelligence magnifies the quality of the underlying data. Frost shared a common example of organizations rushing toward AI projects simply because the technology is available, such as building a chatbot before defining the customer problem it should solve or the business outcome it should improve.
Measurement also had to evolve. As Travelex invested in lifecycle marketing, traditional campaign metrics no longer captured the impact of stronger customer relationships. The team shifted its focus toward customer lifetime value, allowing it to measure how personalization influenced long-term business performance rather than individual campaigns.
As Frost summarized:
“If you try and build AI on top of bad data, you’re going to get bad results.”
Takeaway: AI cannot compensate for weak customer data. Organizations that invest first in identity, unified data, and customer measurement create a stronger foundation for personalization, lifecycle marketing, and long-term loyalty.
One-to-One Personalization Is Closer Than Ever, But Still Out of Reach
One-to-one personalization has become one of marketing’s most ambitious goals. It has also become one of its most misunderstood.
Daniel Agudelo argued that truly individualized experiences remain the exception rather than the rule. Building, maintaining, and activating the data required for one-to-one personalization demands significant investment, making it practical for only a handful of high-value customer journeys.
Most organizations concentrate that effort where it delivers the greatest return:
- Customer onboarding.
- Trial experiences.
- High-value lifecycle moments.
- Product adoption and expansion.
The challenge extends beyond technology.
A single customer can appear differently across marketing, sales, customer success, and support. Each team interprets the same data through its own priorities, making personalization as much an organizational alignment problem as a technical one.
AI is beginning to reduce some of that complexity.
Agudelo shared an example from Contentsquare, where an internal AI agent can query Snowflake on behalf of marketers. Instead of waiting for analytics teams to build reports or define events, marketers can retrieve the information they need almost instantly. A request that previously required multiple conversations and technical resources can now happen in minutes.
That doesn’t deliver one-to-one personalization overnight. It shortens the distance between the data marketers need and the experiences they want to create.
Takeaway: AI is making personalization more accessible by reducing operational friction. The bigger challenge remains aligning customer data, teams, and business goals around a shared understanding of the customer.
Build Loyalty Programs Like Products
Many loyalty programs fail because they launch with every feature the business wants instead of the benefits customers actually value.
Travelex deliberately took the opposite approach.
The team began with a broad list of ideas that included tiers, points, partner offers, and additional rewards. Before building any of them, they returned to customer research and asked people to rank what mattered most.
The results simplified the roadmap.
- Customers overwhelmingly prioritized price-related benefits.
- Lower-priority ideas were removed from the initial launch.
- The program launched as a focused MVP.
- New features could be added later based on customer feedback and performance.
That discipline paid off.
Travelex’s loyalty program increased six-month customer lifetime value by 12%, loyalty members reloaded their travel cards twice as often, and the company achieved opt-in rates as high as 45% by offering customers a meaningful value exchange.
The team also discovered that loyalty cannot simply be copied from one market to another. Regulations and customer expectations varied across countries, requiring different acquisition and engagement strategies. Australia, for example, presented very different opt-in dynamics than the UK.
As Frost reflected:
“If we’d gone for the full-bean solution with everything, we’d still be talking about it and it wouldn’t be live.”
Takeaway: The strongest loyalty programs begin with the customer problem, not the feature list. Launching a focused program creates faster learning and a stronger foundation for future growth.
Customer Engagement Looks Different in B2C and B2B
Loyalty follows different patterns depending on how customers use the product.
For Travelex, purchases happen infrequently. Most customers travel once or twice a year, making the challenge less about generating frequent transactions and more about remaining relevant between trips.
As customer data improves, the company is beginning to build richer travel profiles that identify meaningful behavioral patterns. Someone who takes multiple weekend trips each year may benefit from a different lifecycle strategy than someone planning a single annual vacation. Machine learning helps identify those opportunities without increasing communication for every customer.
Contentsquare faces a different challenge.
Its highest-value customers are already paying for the product, making adoption and expansion just as important as acquisition. The company applies the same product signals used during free trials to existing customers, helping teams identify adoption opportunities, encourage product discovery, and support future upsell conversations.
The company’s Champions Program reinforces that strategy by recognizing highly engaged users each quarter.
What motivates participation surprised the team:
- Recognition within the community.
- Friendly competition through leaderboards.
- Opportunities to connect with peers.
- Greater visibility within the customer community.
The rewards matter, but Agudelo found that community recognition consistently generated more enthusiasm than promotional incentives alone.
Takeaway: Customer engagement should reflect how customers build value from the product. B2C organizations may focus on staying relevant between purchases, while B2B organizations often deepen loyalty by encouraging product adoption, community participation, and long-term success.
Organizational Alignment Determines Whether Loyalty Scales
Technology often receives the most attention during transformation projects. Daniel Agudelo argued that alignment is the factor that determines whether those projects succeed.
Contentsquare’s growth through acquisition created a difficult integration challenge. Different products, engineering teams, data warehouses, and customer experiences all needed to become part of a single platform without disrupting existing customers.
The team learned three lessons that apply well beyond mergers and acquisitions:
- Executive alignment has to come before implementation so teams have the resources and long-term support to succeed.
- Employees from acquired companies should help design the future state because they understand how their products actually work.
- Building a new foundation often creates a better long-term outcome than forcing incompatible systems to work together.
That last point proved especially important.
Contentsquare initially explored reusing existing systems to save time, but the underlying data models and warehouses were fundamentally incompatible. Starting fresh required more work upfront, but it created a stronger platform for future customer experiences and personalization.
The same principle shaped how the company approached customers. Rather than attempting a perfect migration, the team introduced changes incrementally, communicated openly about what customers would gain and lose, and expanded only after each phase was working successfully.
Takeaway: Customer loyalty depends on organizational alignment as much as technology. Shared priorities, cross-functional collaboration, and realistic implementation plans create stronger customer experiences over time.
Loyalty Compounds From Strong Foundations
AI has expanded what marketers can accomplish. It can automate analysis, accelerate personalization, and help brands respond to customer behavior faster than ever before.
The discussion between Travelex and Contentsquare makes another point just as clearly. AI amplifies the systems already in place. Organizations with connected customer data, realistic personalization strategies, and disciplined measurement can create more valuable customer experiences. Organizations without those foundations simply automate existing problems.
The lessons from both companies point in the same direction:
- Build a unified customer data foundation before expanding AI.
- Personalize the journeys where it creates meaningful customer value.
- Launch loyalty programs with the smallest feature set customers actually want.
- Measure customer lifetime value instead of campaign performance alone.
- Align teams around a shared view of the customer before introducing new technology.
Those priorities may not be the most visible part of an AI strategy, but they create the conditions that allow AI to improve customer relationships instead of simply making marketing faster.
Editor’s note: Watch the full Activate Summit 2026 session, “Rethinking Loyalty in the Age of AI,” on demand. Link to come.
Frequently Asked Questions (FAQs)
How is AI changing customer loyalty?
AI gives customers faster access to product comparisons, pricing, and alternatives while helping brands automate personalization and lifecycle marketing. That combination makes differentiated customer experiences more important because switching between brands requires less effort.
Why is customer data important for AI?
AI depends on accurate, connected customer data to deliver relevant experiences. Travelex built a unified customer view before expanding personalization, allowing the company to activate lifecycle marketing and measure long-term customer value.
Is one-to-one personalization realistic?
According to Daniel Agudelo, true one-to-one personalization remains expensive and operationally complex for most organizations. Many companies focus their highest levels of personalization on high-value journeys such as onboarding and product adoption while using AI to reduce the operational effort required to move closer to that goal.
What makes a successful loyalty program?
Travelex started with customer research rather than a long list of features. By launching an MVP focused on the benefits customers valued most, the company increased six-month customer lifetime value by 12%, doubled card reload rates among loyalty members, and created a foundation for future enhancements.
What should marketers measure instead of opens and clicks?
Chris Frost recommends using customer lifetime value as the primary measure of loyalty. Campaign metrics remain useful, but long-term customer outcomes provide a more accurate view of whether lifecycle marketing and personalization are strengthening customer relationships.