Onchain Customer Loyalty: A New Paradigm for Engagement and Retention
Onchain Customer Loyalty: A New Paradigm for Engagement and Retention
Implementing the NAPL and DCIU frameworks in consumer onchain commerce
In today's rapidly evolving business landscape, converting inactive customers into active ones is crucial for improving Lifetime Value (LTV) and Gross Merchandise Value (GMV). This goal, highlighted in my recent post Blackbird whitepaper, underscores the importance of customer relationship management (CRM) and customer data platforms (CDP) in building lasting customer loyalty.
In this article, I will explore the effective strategies for building customer loyalty based on Happy Lee’s article "NAPL x DCIU ：會員模型的交乘應用’’, with a focus on segmentation models and innovative approaches like NFT-based loyalty programs.
Purchase Cycles vs. Customer Lifecycles
Active Members and Purchase Cycles
Active members typically define a brand's average purchase cycle. Their relatively stable repurchase frequency contributes significantly to this metric. However, it's important to note that an active member's purchase intent can vary greatly within this cycle.
For instance:
An active member nearing the end of the typical 3-month purchase cycle is likely to have high purchase intent.
Conversely, immediately after a purchase, the same member's purchase intent drops to nearly zero as their immediate needs have been satisfied.
This fluctuation highlights the dynamic nature of purchase intent, even among the most loyal customers.
NAPL Model: Tracking Customer Lifecycles
The NAPL model represents a customer's lifecycle with a brand:
N (New): First encounter and initial purchase
A (Active): Regular engagement and purchases
P (Potential): Decreased activity, but still engaged
L (Lost): Significant decrease in engagement or complete disengagement
This model tracks long-term changes, typically measured in months or years. The progression through these stages is often unidirectional - once a customer reaches the 'Lost' stage, reactivation can be challenging.
DCIU Model: Capturing Short-Term Purchase Intent
The DCIU model reflects the rapid changes in purchase intent throughout a customer's purchase cycle:
D (Deciding): High intent, close to making a purchase
C (Considering): Actively researching, comparing options
I (Interesting): Casual browsing, low immediate intent
U (Un-interesting): No current purchase intent
A customer's DCIU status can change rapidly, even daily, based on factors like recent purchases, payday cycles, or immediate needs.
Integrating NAPL and DCIU Models
The integration of these models provides a more nuanced understanding of customer behavior:
An 'A' (Active) customer in NAPL might cycle through all DCIU stages within their purchase cycle:
'D (Deciding)' as they approach their typical repurchase time
'U (Un-interesting)' immediately after a purchase
Gradually moving through 'I (Interesting)' and 'C (Considering)' as the next purchase cycle approaches
The presence of 'I (Interesting)' or 'U (Un-interesting)' DCIU statuses among 'A (Active)' NAPL customers doesn't necessarily indicate disengagement. It may simply reflect their position in the purchase cycle.
DCIU statuses can help predict NAPL transitions:
A 'P' (Potential) NAPL customer showing 'D (Deciding)' DCIU behavior might be close to returning to 'A (Active)' status.
An 'A (Active)' customer consistently showing 'U (Un-interesting)' or 'I (Interesting)' DCIU behavior might be at risk of moving to 'P (Potential)' status.
Practical Implications for Customer Relationship Management
Tailored Communication:
Adjust messaging frequency and content based on both NAPL status and current DCIU stage.
For example, limit promotional content to 'A' NAPL customers in 'U' DCIU stage to avoid message fatigue.
Predictive Interventions:
Use DCIU trends to predict and prevent negative NAPL transitions.
Implement re-engagement campaigns for 'A' customers showing prolonged 'U' or 'I' DCIU behavior.
Lifecycle-Appropriate Offers:
Design promotions that align with both the customer's lifecycle stage (NAPL) and their current purchase intent (DCIU).
For instance, offer loyalty rewards to 'A' NAPL customers in 'C' DCIU stage to encourage faster progression to 'D'.
Resource Allocation:
Prioritize marketing efforts towards 'A' and 'P' NAPL customers showing 'C' or 'D' DCIU behavior for maximum efficiency.
Customer Value Assessment:
Develop a more accurate Customer Lifetime Value (CLV) model by considering both long-term NAPL trends and short-term DCIU fluctuations.
Why Active Members Matter
Recovery efforts for lost members typically yield conversion rates below 1%.
Operations targeting active members can achieve conversion rates up to 7%.
Active members represent the most valuable segment for brands to nurture.
To illustrate how these principles can be applied in a modern context, consider the following simulated example of an NFT-based loyalty program:
Case Study: CryptoBlend Digital Art Marketplace
Program Overview:
Points earned for purchases, reviews, and successful referrals.
NFTs represent loyalty tiers (Bronze, Silver, Gold).
Each tier grants access to special features.
Tier Benefits:
Bronze: Early access to new art drops
Silver: Discounts on purchases
Gold: Exclusive artist meet-and-greets in virtual galleries
Segment Targeting:
New to Active: Quick path to Bronze tier to encourage engagement.
Active Retention: Clear progression to higher tiers with increasing benefits.
Potential Reactivation: Time-limited opportunities to claim tier NFTs based on past activity.
By implementing such a program, businesses can leverage the uniqueness and perceived value of NFTs to enhance traditional loyalty strategies, potentially increasing engagement across all customer segments.
Conclusion
Understanding and effectively managing customer lifecycles is crucial for businesses aiming to maximize their Gross Merchandise Value (GMV) and Customer Lifetime Value (LTV). Two complementary models help achieve this goal:
The NAPL (New, Active, Potential, Lost) model tracks long-term customer lifecycles, providing insights into customer engagement over time.
The DCIU (Deciding, Considering, Interesting, Uninteresting) model captures short-term purchase intent, offering a snapshot of a customer's current mindset.
By leveraging these models, businesses can develop effective customer loyalty strategies that directly impact GMV through increased purchase frequency and LTV through extended customer relationships. To achieve this, companies should:
Gain a deep understanding of customer segments
Implement targeted approaches for segment transitions
Create innovative programs that resonate with modern consumers
Focusing on active members and implementing creative solutions, such as NFT-based loyalty tiers, can help businesses build lasting relationships with their most valuable customers. This approach not only enhances customer engagement but also drives long-term success by fostering loyalty and increasing customer value over time.
NAPL x DCIU: Claude Artifact Application Guide
It may take some time to fully understand how this can be applied to your specific use case. In the meantime, feel free to use my artifact with the prompt below
NAPL x DCIU Claude Artifacts:
https://claude.site/artifacts/9a8efaf8-cd13-4471-af2e-4dd6f40d925f
prompt:
apply this in {{your context}} and explain it with gradually complexityHi, I'm Kevin Wang!
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