How Hibbett Built a Customer-First CRM Strategy That Delivers Results

At CRMC 2026, Hibbett and Baesman shared how a decade-long partnership evolved from optimizing a loyalty program into building a customer-centric CRM strategy powered by data, testing, personalization, and AI.

The session offered a practical roadmap for marketers looking to increase retention, improve loyalty performance, and create more meaningful customer experiences.

Watch the replay of the session:

Start with the Data Foundation

Before personalization, automation, or AI can be successful, brands need a reliable customer data foundation.

For Hibbett, that meant creating a more complete customer view by connecting customer records, transactions, loyalty activity, email engagement, SMS engagement, app interactions, and ecommerce behavior.

The goal was simple: create a single customer view that could support smarter decision-making and more relevant marketing.

Without trusted data, personalization becomes guesswork.

Stop Organizing Around Channels

One of the biggest changes Hibbett made was restructuring its customer lifecycle team.

Instead of managing separate teams for email, mobile, and loyalty, Hibbett reorganized around customer outcomes.

The team now focuses on:

• Brand Marketing
• Growth Marketing
• Retention Marketing

This shift encouraged collaboration across channels and allowed the team to focus on what was best for the customer rather than optimizing individual channels in isolation.

The result was faster execution, better alignment, and stronger performance.

Testing Became a Competitive Advantage

Many brands talk about testing. Hibbett operationalized it.

Over the course of a year, the team conducted more than 100 email tests and nearly 200 tests across email, SMS, and push notifications combined.

Every major initiative included holdout groups and incremental measurement.

This approach enabled the team to:

• Prove ROI
• Secure executive buy-in
• Gain support from merchandising and finance teams
• Scale successful programs with confidence

Rather than relying on assumptions, every decision was backed by measurable results.

Personalization Goes Beyond First Names

Most brands personalize messages with a customer's name.

Hibbett took personalization much further.

Using dynamic waterfall content, email experiences changed based on each customer's behavior and lifecycle stage.

Examples included:

• Birthday messaging
• Cart abandonment reminders
• Reward redemption prompts
• New member onboarding
• Mobile wallet adoption campaigns

The content displayed to each customer reflected what was most relevant to them at that moment.

This approach created a more individualized experience while improving engagement and conversion performance.

Loyalty Offers Require Strategy

One of the session's most interesting discussions centered around loyalty incentives and point multipliers.

While bonus point promotions can be highly effective, Hibbett emphasized the importance of targeting offers strategically rather than broadly.

By aligning promotions with specific brands, categories, and customer behaviors, the team generated meaningful incremental lift while maintaining program profitability.

Cross-functional collaboration with finance, merchandising, and marketing teams was critical to making these initiatives successful.

Personalized Birthday Campaigns Deliver Strong Results

Birthday programs are common, but Hibbett found success by moving beyond a one-size-fits-all approach.

Through testing, the team discovered that different customer segments responded better to different birthday offers.

They expanded the program across multiple channels, including email and SMS, while tailoring experiences based on loyalty status and customer value.

The result was stronger engagement and improved offer performance.

AI Is Amplifying Personalization

After building a strong data foundation, Hibbett began introducing AI-powered decisioning into its marketing strategy.

Instead of placing customers into broad static segments, AI now helps determine the best content and recommendations for each individual customer.

This 1-to-1 personalization model uses behavioral data, loyalty status, rewards information, and engagement history to improve relevance.

The key lesson: AI works best when built on clean, trusted customer data.

Key Takeaways for Marketers

The session reinforced several important lessons:

1. Build a strong customer data foundation first.
2. Organize teams around customer outcomes, not channels.
3. Test constantly and measure incrementality.
4. Personalize experiences based on behavior, not just demographics.
5. Use loyalty programs as a source of customer insight.
6. Adopt AI only after your data is ready.
7. Continue evolving your strategy as customer expectations change.

The Future of Customer Loyalty

Hibbett's success did not come from a single campaign or technology platform.

It came from years of building better customer data, creating a culture of testing, investing in personalization, and continuously optimizing the customer experience.

For brands looking to improve retention and loyalty, the message is clear: customer-centric marketing wins when data, testing, personalization, and AI work together.

FAQ

Why is a customer data foundation important for personalization?

A strong customer data foundation gives marketers a complete view of customer behavior, allowing them to create more relevant experiences across channels.

How many tests should marketers run?

The goal is not simply to run more tests. Focus on testing the initiatives most likely to impact customer behavior and business outcomes.

What is a holdout group?

A holdout group is a segment of customers who do not receive a marketing treatment, allowing marketers to measure true incremental impact.

How can loyalty programs improve personalization?

Loyalty programs provide valuable behavioral and preference data that can be used to create more relevant customer experiences.

Should brands implement AI immediately?

AI is most effective when supported by accurate customer data and clearly defined marketing objectives. Build the foundation first, then scale AI initiatives.