True personalization can only happen when you understand the motivations and preferences that drive behavior.
Driving Incremental Revenue Through a Focus on the Highest Value Customers
Despite the profusion of digital data on consumers’ behaviors, there is a critical gap between the insights required to personalize loyalty and e-commerce marketing programs and the data currently used to do so.
This gap is created by the fact that the current approaches to personalization rely wholly on observed shopping and transaction data. Marketers are missing the underlying motivations and preferences that drive shopping behavior or purchase.
Transaction optimization techniques can erode customer loyalty by:
- Training customers to buy with discounts
- Providing irrelevant product recommendations while missing the motivation or intent of the purchase
relationships builds loyalty by:
- Managing promotions with
an eye toward enhancing customer economics
- Tailoring the product, message and offer based on the motivations and preferences that drive brand choice
Rosemark has created the Quantitative Persona™ Method and services to decode how motivation & preference drive brand choice, allowing the marketer to enhance personal relevance and thereby effectiveness.
- Identify High-Value QP™️ Clusters
- Develop QP™️ Typing Tool and Scoring Algorithm
- Capture First Party Data
- Score and Append QP™️ Cluster Identity to Customer Records
- QP™️ Driven Personalized Product & Marketing Programs
- Test, Measure, and Optimize
The Quantitative Persona™️ scoring service enables marketers to harness the full power of their assets: data, behavioral predictive models, and orchestration / delivery tools across consumer touchpoints. Rosemark’s approach leverages its proprietary AI/ML data modeling to convert a client’s strategic segmentation into a targeting algorithm, which is integrated via an API into the marketing automation stack. Rosemark’s QP™️ helps marketers target and personalize their brand marketing to drive materially higher penetration and share of requirements among the brand’s most valuable consumers.
Implementing QP™️ insights into the marketing process and the corresponding QP™️ machine learning based algorithms into the technology stack enables marketers to develop and execute a personalized customer management strategy based on the drivers of brand choice and category usage, which when tuned and optimized through testing over time, drive material improvements in loyalty and thereby Customer Lifetime Value (cLTV).
The QP™️ Personalization Method integrates and enhances the most common consumer insight frameworks such as customer journey and predictive behavioral modeling in service to making the marketing treatments more personally relevant.
The following diagram illustrates how combining QP™️ Clusters with customer journey and behavioral models provides more effective personal and relevant marketing treatments.
PERSONALIZATION ENRICHED BY THE QP™️ METHOD
J4 x M5 x QP4™️
J4 x M5 x QP™️1
Same Journey Stage and Behavioral Model but now enhanced with insights on consumer motivation and preference.
The QP™️ personalization method uncovers the drivers of brand choice, deepening personalization and thereby the relationship which leads to increased customer LTV
SUMMARY OF ROSEMARK TEAM’S PRIOR CUSTOMER MANAGEMENT CLIENT WORK AND IMPACT
Personalized relationship marketing communication
Improved Consumer Engagement
Retail & Apparel
Merchandising and promotions targeting and program design
Customer Management strategy and communication
Customer acquisition and expansion marketing