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Data Science | Segmentation | Strategy | Salesforce | Integration | Customer Engagement
A leading travel company partnered with us to revitalise their partner rewards programme through advanced data analysis and segmentation. By examining historical transaction patterns and engagement behaviours, the project revealed distinct agent segments with unique characteristics and potential. This data-driven approach provided the foundation for targeted and personlised engagement strategies and improved customer experiences that could enhance agent loyalty and increase revenue generation.
The client faced multiple challenges with their travel agent rewards programme. Following the global travel disruption of 2020-2022, they experienced a decline in engagement and a reduction in active participants. They struggled with uncertainty about which agents to prioritise and lacked clear understanding about agents' rewards preferences and claiming behaviours. Additionally, the client recognised a pressing need for a more structured approach to database management and strategic communication to revitalise their programme effectively.
In addition to this, their technology systems were disparate, under utilised and lacked a clear
Phase 1 focused on Data Analysis & Segmentation, beginning with a comprehensive audit of the existing agent database spanning over six years. This involved detailed analysis of transaction patterns, including frequency, value, and product preferences, alongside examination of rewards claiming and redemption behaviours. The team developed a machine learning clustering model using AI to identify distinct agent segments, ultimately creating an actionable segmentation framework based on monetary value, engagement vitality, and claiming behaviour. This work was then supplemented with research and social science analysis to understand some of the drivers behind the behaviours and attitutdes towards the rewards progammes across different segments.
Phase 2 concentrated on Strategic Planning, as we developed targeted marketing and engagement strategies tailored to each identified segment. This phase included the creation of specific tactical recommendations designed to address the unique characteristics and potential of each agent group.
Phase3 focused on desigining a platform solution for improved data management, technology utilisation and improved communication capabilities to support long-term programme success.
The analysis revealed nine distinct agent segments with varying characteristics, behaviours attitudes and needs, and levels of value and importance. From high frequency, highly engaged and low transaction value through to low frequency, high value and moderatly engaged agents. An interactive and comprehensive overview of the overall rewards programme was developed along with bespoke agent profiles using BI which brought the segments to life. This was supplemented with additional research that provided insight into the attitudes of each segment and needs to support with engagement and selling potential.
Having completed the segmentation, a comprehensive list of recommendations for database management was provided to ensure future viability and efficacy. This included implementing data refresh protocols to identify truly dormant accounts, revising archiving policies based on observed patterns, and cleaning and verifying the database against industry registrations. These measures would ensure that efforts would be directed at viable prospects and that dormant but potentially recoverable accounts would not be prematurely archived.
Customer engagement was sporadic and non-specific with limited view into its overall effectiveness. Using the comprehensive segmentation analysis and insights, we were confidently able to advise where to focus resources on segments with the highest retention and growth potential, creating segment-specific, personalised communication and rewards strategies across highly engaged channels. In addition, we developed a comprehensive onboarding programme for new agents to increase programme participation. These tailored approaches would strengthen relationships with the most valuable segments while encouraging growth in promising but underperforming segments.
Existing technology platforms were disparate, with multiple sources of truth for customer profiles and minimal integration. This made it very difficult to track customers across the customer experience and run analsysis on performance, whilst also being able to activate personalised communications within the dedicated martech platforms. A new technology blueprint was designed to enable better integration across booking, rewards, CRM and martech platforms and a single source of truth for customers.
Technology enhancements included building a real-time single customer view in the relevant CRM, establishing live segmentation tagging using the LLM python parameters generated for the segmentation, with periodic methodology reviews to refine the segmentation for continuous optimisation of the programme. These technological improvements would support more responsive and adaptive management of marketing, sales and service activities including the customer engagement programme, allowing for timely interventions based on changing agent behaviours and market conditions.
While Phase 2 implementation was still underway at the time of writing, the segmentation analysis delivered immediate value to the client's business operations. The analysis revealed that less than 10% of agents were responsible for over 40% of revenue, illuminating the critical importance of these high-value partners. The segmentation clearly highlighted which partner groups demonstrated strong recovery following market disruption, contrasting them with those requiring targeted intervention strategies. Additionally, the analysis uncovered specific rewards preferences unique to each segment, providing actionable insights for tailoring incentive programs. This comprehensive understanding allowed for clear prioritisation of retention, growth, and re-engagement efforts across the partner base. Perhaps most significantly, the segmentation established a robust data-driven foundation upon which all future marketing and engagement strategies could be built and optimised.
This case study demonstrates the transformative power of data-driven segmentation in revitalising a rewards and customer engagement programme. By understanding the distinct characteristics, behaviours, and potential of different segments, you can gain insights needed to create targeted, effective engagement strategies that can drive programme success and revenue growth. The analytical approach enabled the development of highly personalised engagement models tailored to the specific needs and value potential of each partner type, replacing previously generic approaches with precision marketing tactics. This strategic refinement positioned the client to maximize returns on their partner investments while strengthening relationships with their most valuable business contributors.
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