Next-Gen Customer Loyalty Programs with Data Streaming

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Classic punch cards (and fishing for them in your wallet or occasionally misplacing one) have become a thing of the past, as today's digital landscape demands more innovative solutions. Today’s customer loyalty programs are increasingly sophisticated—evolving, proliferating, and diversifying across every industry from retail, travel, and hospitality to healthcare (e.g., a discount for paying within 30 days of a hospital visit). And engagement has shifted from exchanging tokens to using mobile apps. Redeemable points and cash back. Free one-hour delivery. Discounts and freebies. Early access and exclusive offers. 

But discounts alone aren’t enough, as customers expect personalized experiences as well. According to Gartner, “By 2026, customer loyalty programs that offer a mix of transactional and experiential benefits will displace programs that solely focus on just offering customers points.” This will grow, as “CMOs plan to increase investment in loyalty program management by 41%.” In an increasingly competitive landscape, businesses need to harness real-time data in their loyalty programs to drive customer retention. Gartner estimates that “One in three businesses without a loyalty program today will establish one by 2027 to shore up first-party data collection and retain high-priority customers.”

Data streaming powers next-generation customer loyalty programs—going beyond transactional rewards to leveraging real-time data to create highly personalized and dynamic experiences. When businesses tap into the continuous flow of real-time data, they're able to understand customer preferences and behavior, transforming static incentives to tailored reward experiences that build lasting customer relationships. Benefits of using data streaming include: 

• Faster reward cycles for instant rewards (i.e., no longer waiting for a full billing cycle)

• More repeat purchases and greater revenue from elevating in-the-moment customer experiences

• Multichannel strategy for loyalty program distribution while fostering a seamless experience at each touch point, providing more choices for customers savvy about vendor pricing (e.g., online web order or in-app order for curbside pick-up) 

• Greater competitive advantage

• Automated decision-making by AI and machine learning to detect purchase patterns and determine which type of reward would best fit a customer based on real-time behavioral data (e.g., offering 10% off browsed items for customer A, a freebie at check-out for customer B)

Today’s business and technical challenges 

In adapting loyalty programs for the digital age, ensuring smooth operations and effective utilization of customer data presents a unique hurdle. From managing vast streams of real-time information to safeguarding sensitive customer data, navigating these aspects are key for companies to establish a competitive edge in the loyalty arena. Business challenges include: 

• Repetitive loyalty programs after time that need refreshing

• Location-based programs that need continuity (e.g., customers going from physical to online stores), with stores across geos needing consistent loyalty tracking

• Shared reward programs that require data sharing—while some businesses offer rewards within their own brand, others offer customer rewards across multiple brands (e.g., flight, car, hotel, spa) 

• Coordinating digital vs. non-digital experiences

• Slow time to market for program rollout resulting in less revenue capture and losses to competitors

• Lack of customer 360 visibility into data coming from different aspects, without a full picture of customer purchase activity

• Manual processes that hinder the shift from generic to personalized rewards

In parallel, existing infrastructure and data challenges hinder the ability of development teams to leverage unprecedented volumes of real-time customer data efficiently for building loyalty programs:

• Disconnected, siloed data systems (e.g., point of sale systems, order management system), lacking a way to consolidate all the data and resulting in having inaccurate data 

• Legacy technologies for which modernization takes time to implement what business teams are asking for and by the time something is implemented, there are new business requirements—making it difficult to stay ahead of the curve (legacy systems include MQs, mainframes, and on-prem databases)

• Batch data processing 

• Traditional integration tools, including ETL and point-to-point data pipelines

• Older monolithic apps with lack of flexibility in updating; and outdated mobile applications on phones with new features, will lose out on functionality (e.g., inability to provide in-store customers with location-specific offers)

• Lack of tooling to capture all data across different channels for analytics (e.g., if no digital presence such as an app, customers wouldn’t know their loyalty status and would have to log into a website or call in—missing opportunities when they’re in store or at a drive-thru) 

• One-off solutions that are difficult to scale and tack onto when requirements change

• Unclear cloud strategy, which can result in restrictions on how quickly business requirements can be turned around

• Inadequate security to protect customer personally identifiable information (PII) data, with vulnerabilities around data encryption, role-based access management, patching, and application administration

From Point-to-Point Integrations to Streaming Data Pipelines

How Confluent brings real time to customer loyalty 

Confluent abstracts away data infrastructure so that development teams can focus on building innovative new features and marketing teams can gain real-time and predictive analytical insights into customer behavior for loyalty programs. To overcome the above challenges, organizations can leverage Confluent’s data streaming platform to stream, connect, process, and govern data at scale: 

• Stream: Confluent runs on-premises and in any cloud, making it easy to stream across any environment to support loyalty programs and 24/7 global operations. Powered by Kora, Confluent provides elasticity, reliability, performance, and cost-efficiency—seamlessly scaling to any workload, throughput, or seasonal traffic peaks to meet demand.

• Connect: Leverage Confluent’s 120+ pre-built connectors (or bring your own custom connector) to easily connect source and destination systems, legacy and new systems. Bring data in from anywhere—CRMs, databases, SaaS apps, mobile apps—to gain a holistic, real-time view of customer activities for building personalized experiences and surfacing offers at the right moment for higher conversion. Streaming data pipelines break down data silos and unlock real-time data flow across the organization.

• Process: Stream processing with Apache Flink® helps join, enrich, and transform data in real time, all through simple SQL syntax. Teams can mask sensitive data when sharing downstream and dedupe data in an order management system. Other use cases include API calls or UDFs from Flink and sending data to AI/ML models for generating real-time recommendations (e.g., in-store customers can instantly get rewards at point of sale).

• Govern: Leverage Stream Governance to ensure data quality, trust, and security so that development teams can focus on building loyalty applications and features. Schema Registry maintains data quality across different systems, where changes to data format are controlled via schema evolution. Stream Lineage visualizes data pipelines (and any disruptions) and Data Portal allows data to be securely shared with other teams.

In addition, Confluent works with technology partners to accelerate your data streaming journey, working with your existing systems and tools. Confluent’s decoupled architecture prevents vendor lock-in, rigid point-to-point connections, and expensive custom integrations. New applications and tools can be added anytime, making it easy to bring on loyalty program vendors helping implement your solution and third-party ordering systems.

Solution implementation

This diagram provides an overview of the deployment architecture for a real-time customer loyalty program using Confluent Cloud.

(See full-size image)

Using Confluent’s pre-built, fully managed source connectors (e.g., Database CDC source connectors, Salesforce CDC source connector, HTTP source connector), real-time data is continuously ingested from heterogeneous data sources including a product & order database, Salesforce CRM system, marketing system, and loyalty program.

Data is written to respective topics (e.g., Order Data, Customer Data, Product Data, Campaign Data, Customer Loyalty Level).

From there, Flink is used to stream process data in flight—joining, enriching, aggregating, and validating data streams from topics such as product data, order data, customer loyalty level, and campaign data. The resulting data products are Loyalty Level Update, Promotion Data, and Promotion Notification. Confluent’s fully managed S3 and HTTP sink connectors stream this processed, ready-to-use data to S3 and data lakes for analysis, the loyalty program and related marketing systems, and push notifications. 

Data can also feed AI/ML models to perform predictive analytics for customer purchases and sentiment analysis. This helps guide new offerings as well as to personalize loyalty recommendations and dynamically price products. Confluent’s fully managed Flink service has an AI Model Inference feature, which allows Flink to make calls to AI engines (e.g., OpenAI, Amazon SageMaker, GCP Vertex). This brings together data processing and AI workflows to improve efficiency and reduce operational complexity—enabling accurate, real-time, AI-driven decision-making by leveraging fresh, context-rich streaming data.

Here is an example of a Flink SQL query used to stream process real-time customer purchase data to instantly calculate their reward level as Gold, Silver, or Bronze. The live customer loyalty status updates are written to a topic and shared with the main loyalty program and marketing system for personalizing reward offers as well as with other downstream consumers such as a microservice for push notifications. 

INSERT INTO customer_loyalty_levels(
 email,
 total,
 rewards_level)
SELECT
  email,
  SUM(sale_price) AS total,
  CASE
    WHEN SUM(sale_price) > 10000 THEN 'GOLD'
    WHEN SUM(sale_price) > 5000 THEN 'SILVER'
    WHEN SUM(sale_price) > 1000 THEN 'BRONZE'
    ELSE 'CLIMBING'
  END AS rewards_level
FROM orders
GROUP BY email;

Conclusion

Confluent’s data streaming platform offers a transformative solution for businesses aiming to boost retention and attract new customers. By leveraging real-time data in loyalty programs, companies can unlock new ways to engage customers effectively. This leads to increased customer satisfaction as well as delivers a greater return on investment for marketing efforts. Data streaming helps businesses continuously optimize customer rewards to stand out from the competition.

To learn more, here are additional resources: 

• How to build streaming data pipelines

• Leveraging data streaming for artificial intelligence