From Loyalty Apprentice to Artisan

Achieving Mastery in Customer Data Utilisation 

The journey from loyalty apprentice to loyalty artisan is a critical evolution for organisations striving to master the utilisation of customer data. While basic loyalty programs serve as foundational tools to collect customer data, true mastery involves transforming this data into actionable insights that drive value creation and sustainable growth.

This article outlines practical steps for organisations at various stages of loyalty maturity to progress towards becoming data-driven loyalty artisans. 

The Loyalty Journey 

Loyalty apprentices are organisations with nascent loyalty programs primarily gathering first-party data. However, this data sits in silos, unintegrated and underutilised. Without a unified approach, brands struggle to convert raw data into valuable insights. To evolve from apprentice to artisan, companies must focus on three essential pillars: 

1. Data Integration: Consolidating customer data across all touchpoints into a unified Customer Data Platform (CDP) to provide a single view of the customer. This allows businesses to personalise interactions, improve targeting, and enhance customer experiences. 
   
   
2. Relevance Strategies: Leveraging data to deliver tailored experiences that enhance engagement and drive loyalty. Consumers expect relevant, timely interactions, and failing to meet this expectation risks alienating them. 
   
   
3. Predictive Analytics: Using AI and machine learning to forecast customer behaviour, enabling proactive retention and growth initiatives. Effective use of predictive models can dramatically improve campaign effectiveness and customer lifetime value (CLV). 

5 Steps to Achieve Mastery 

1. Assess Your Current State: Conduct a thorough audit of your existing loyalty program. Identify gaps, opportunities, and areas where customer data is underutilised. Use maturity models to benchmark progress and establish measurable milestones. Are you simply collecting data, or are you actively using it to influence customer behaviour? 
   
   
2. Define Clear Objectives: Align loyalty program objectives with broader business goals. Define metrics for success beyond membership numbers, such as Net Promoter Score (NPS), Customer Lifetime Value (CLV), and engagement frequency. This step requires a clear understanding of what success looks like for your organisation. 
   
   
3. Build Infrastructure: Invest in technology solutions that enable data integration, predictive analytics, and seamless personalisation. Ensure your systems are scalable and compliant with evolving privacy regulations. Modern tools such as AI-driven analytics platforms and CDPs can make data more accessible and actionable. 
   
   
4. Adopt a Test-and-Learn Approach: Continuously measure, optimise, and refine loyalty strategies based on data-driven insights. Implement A/B testing and predictive models to fine-tune your approach. Build feedback loops to ensure continuous improvement. 
   
   
5. Foster Organisational Alignment: Ensure marketing, IT, and analytics teams work collaboratively towards unified goals. Regularly review progress and update strategies as necessary. Cross-functional collaboration is essential for building a cohesive loyalty strategy. 

Artisans See the Big Picture

As Da Vinci saw the figure of David inside the block of marble before he started chipping away, and stonemasons visualise the completed wall before they start, Loyalty Data Artisans understand the changes in customer behaviour they will cause. They know the few truths of Marketing that must be incorporated in their strategies.

The NBD-Dirichlet model is the closest thing marketing has to an empirical law¹.

• The Negative Binomial Distribution (NBD) explains how often customers buy, showing that most are light buyers and a few are heavy buyers. Heavy buyers transact more frequently, while attracting more light buyers to the program allows it to grow.
  
  
• The Dirichlet² model explains which brands people buy, predicting that most customers buy from a repertoire of brands, in line with each brand’s market share.

Together, the NBD-Dirichlet model provides a powerful test of the claim that the loyalty program is changing customer behaviour.

The model predicts that a brand’s Share of Category Requirements (SCR), which represents the share of a customer’s total category spend, should closely align with your market share. If your loyalty program significantly shifts that ratio for engaged members, it is truly influencing behaviour. If not, the program may simply be capturing existing loyalty rather than driving new habits.

Practical Tips for Becoming a Loyalty Artisan 

• Invest in Technology: Ensure you have the right tools to collect, analyse, and act upon customer data. CDPs, AI platforms, and predictive analytics tools are essential. 
  
  
• Build Incremental Value: Focus on delivering small but consistent improvements over time rather than waiting for large-scale initiatives. 
  
  
• Educate Your Team: Ensure all departments understand the value of loyalty data and how it contributes to business growth. 

The transition from loyalty apprentice to artisan is both challenging and rewarding. Organisations that master customer data utilisation are well-positioned to enhance loyalty, improve customer lifetime value, and build sustainable growth. By investing in the right tools, aligning objectives, and fostering collaboration, your organisation can climb the loyalty maturity ladder and secure long-term customer relationships. 

Talk to Ellipsis, the Loyalty Experts^® .

References: 

[1] The author, Byron Sharp, has made a significant impact on marketing strategy with How Brands Grow. He is based at the Ehrenberg-Bass Institute for Marketing Science in South Australia. A. Ehrenberg was the originator of the NBD-Dirichlet model in the 1950s, and F. Bass explained how new brands are adopted, the Innovative-Imitative model in the 1960s. We seem to be slow learners.

[2] A “distribution of distributions” of brand choice that aggregates to the brand’s market share.