Best use of FastStats 2013 Award

RNLI & Qbase

Using FastStats to realign the charity around supporter journeys based on segmentation progression


The RNLI is the charity that saves lives at sea. They provide, on call, a 24-hour lifeboat search and rescue service around the UK and Ireland, and a seasonal lifeguard service. With their lifeboats, lifeguards, safety advice and flood rescue, they are committed to preventing drowning tragedies. Their lifesaving service is provided wherever possible by volunteers, generously supported by voluntary donations and legacies. Their work is based on and driven by their values. Their volunteers and staff strive for excellence and are selfless, dependable, trustworthy and courageous. Crucially they are independent of Government and do not seek funding from central government. Although a major charity they are community based and operate through local teams, centrally directed and resourced. They have a proud history and tradition and have been saving lives over nearly two centuries. Since the RNLI was founded in 1824, its lifeboats, and since 2001, its lifeguards, have saved more than 140,000 lives. Traditionally, fundraising at RNLI has been product based and driven by direct marketing activities.

Clusters segmentation was first tested for the Christmas Appeal 2012 and delivered a 6% increase in response and income despite a 10% reduction in the campaign circulation which saved the RNLI £25,000.


In 2012 the RNLI underwent a large-scale business process review. The idea was twofold, to make efficiency savings in the organisation and to put the supporter at the heart of their fundraising activities. Before 2012 the RNLI traditionally structured fundraising efforts around their products and subsequently operated in product or departmental silos. For example, the supporter acquisition team were separate to the regular giving team, who were separate from the lottery team and so forth.

  • What did this mean? RNLI staff focussed on their team’s targets at the expense of the supporter. This led to two eventualities, either the best supporters were marketed to by all teams and thus created over-marketing, or certain supporters would be ring-fenced against certain products at the expense of other teams who may in fact offer a more suitable product to a supporter. In essence it meant the transactional approach to fundraising focussed on getting a donation now without considering the long-term effects of that appeal.
  • Knowledge on supporters was either departmentalised or focussed only on one element of the relationship with a supporter. For example, the research team understood supporter profiles and motivations and the insight team understood supporter behaviour for individual appeals but very rarely did this knowledge come together. This concentration on recency and frequency also meant supporters could quickly lapse out of the selection model. Coupled with ring-fence activities it meant supporters were not provided alternative forms of support and once lapsed had little chance of ever being considered for future campaigns.

During 2011 and early 2012 the RNLI had invested heavily in a complete Single Supporter View which combined 8 previously disparate sources of supporter data into a single entity. Qbase subsequently redesigned the RNLI FastStats system around this new SSV and for the first time the RNLI had a complete view of the relationships and engagement with supporters and prospects.

Reimagining Supporter Relationships

In the past when the RNLI talked about supporter relationships they used labels. Someone was a regular giver… a volunteer… a lottery player. But what did that say about engagement? What about motivation? What about progression? And most importantly, were they supporting in the way that was right for them or was it the way that was right for a single department at RNLI?

Therefore the first objective was decided. How can we put supporters in control of their own relationship with the RNLI?

  • The solution started with a piece of insight driven from FastStats and the new SSV. Working with Qbase the RNLI started to measure and score supporter engagement. A scorecard model was devised that measured all the touch-points and response from a supporter. This was more than transactional measures of products and donation levels, it measured on-line interaction, open rates of e-mails, volunteering, attendance at events, depth of information held on a supporter and their tenure and history.  
  • Qbase proposed existing RF scores could be compared against engagement levels to identify specific behaviours. The RNLI had extensive measures of RF as this had been a core strategy in supporter campaign selection for 5 years, but it hadn’t been carried out against the entire SSV base before which now included purchasers from the mail order business, visitors to lifeboat centres and guests at the RNLI college hotel.
  • Qbase carried out an aggregated RF of all sources of supporter transactions to understand when a relationship started and if it was continuing. The new RF measure provided a completely new level of supporter behaviour which included previously ignored relationships such as purchases through the shop, but in isolation it told RNLI nothing of how each supporter had engaged with RNLI to become valuable and therefore the engagement score and the RF score needed to be combined and compared.   Qbase therefore used the clustering tool within FastStats Modelling to compare RF against engagement but with a particular aim. Were there patterns within the data that could be segmented into behaviours? The idea being that supporters who engaged in a certain way would subsequently behave in a certain way.

5 distinct clusters of behaviour were identified by the analysis:

RNLI Clusters

RNLI Cluster Descriptions


The RNLI now had a measurable segmentation methodology which provided a progression route to alter behaviour, but it did not tell them anything about who they were speaking to which meant they faced the same problem of segmentation and Insight not supporting marketing. The solution was to overlay an additional dimension to the segmentation and profiling was introduced. Using the FastStats PWE profiling tool to test potential profile segments, variables such as ACORN, Income, lifestage and other demographics were analysed. Lifestage was selected as the profile differentiator as it was the most predictive and provided the most complete categorisation method. Therefore Qbase built a simple decision tree model that would categorise a supporter based on this profile model and converted this into an expression to be applied to all records on the database.

This then led to a 30 segment model as illustrated below:

RNLI Segment Model

The RNLI now had a measure of supporter behaviour, a progression framework for supporter journeys (moving supporters towards cluster 5) and a basic understanding of who they were speaking to, but again, there was still a belief that there was a missing piece of the jigsaw and that was more in depth behaviour and motivations.

The Insight team therefore engaged with the research team to produce sample selections from each of the 30 segments. They provided profile reports against the make-up of each segment and headline details of the breakdown of how they supported the RNLI. The research team were then asked to carry out primary research with these supporters about what made them tick, why did they support the RNLI, what would make them become more engaged and from this persona’s were built.

The persona reports were carried out at 2 levels, an overall Cluster level and then at a more detailed cluster by lifestage segment. This extensive piece of work revolutionised RNLI’s understanding of their supporters. Now the RNLI had a full insight tool-kit to take to the marketing department. They could segment supporters based on engagement, behaviour and value and provide a description of what they looked like and what their motivations for supporting the charity were. They could provide journey paths where they wanted to take supporters (i.e. they wanted supporters to move up the cluster segments) and they could provide a profile breakdown of the products consumed in each cluster.

RNLI Cluster 5


But this caused a problem. The RNLI was not equipped to support this strategy. They still operated in departmental silos with departmental targets. The new strategy proposed transactional analysis and supporter journey planning be the basis to make selections. It paid no attention to ring-fenced supporters or internal targets, and so in an extraordinary move, this new segmentation strategy suddenly resulted in a commitment from the board to restructure the entire organisation around facilitation of implementing this piece of work. The data, insight and research team were combined into a single unit. The product-focussed departments were re-aligned to stages within the supporter lifecycle and supporter journey and Lifestage Brand Managers were employed to manage the movement of supporters through the cluster journey.


The RNLI have now completed an entire restructure of the fundraising, insight, research data, marketing and fundraising teams. This new structure looks at facilitating a supporter journey rather than forcing a supporter down a particular path. Clusters segmentation was first tested for the Christmas Appeal 2012 and delivered a 6% increase in response and income despite a 10% reduction in the campaign circulation which saved the RNLI £25,000. The appeal used the personas developed from the segments to create personalised and relevant content that was different to each target segment. It also concentrated on the channels that were relevant and used by each segment and used decision tree models to make selections within each segment.

Following this success, Clusters segmentation was expanded for the summer campaign and delivered staggering results. The campaign allowed more supporters to be included with a greater execution of personalisation and messaging based on the segment profile and in doing so they delivered results of an increase of 53.5% in responders on the previous year which equated to a £285,756 increase in donations, a 9.28% response rate and a slight increase of 2.2% on average gift value.

Results In Summary

  • New Marketing structure - 4 new Lifestage Planners, 5 Innovation team members (developing new fundraising idea based on Lifestages and data) and a marketing department focused on delivery of the right experience and product rather than targeted on income
  • Measureable results – 53.3% increase in income on the first campaign
  • Unified Insight team – comprising of data analysis, research team linked with Insight managers delivering true insight
  • Put Insight and data analysis at the heart of marketing – truly delivering Data Driven Marketing.

Future plans - What next?

The RNLI are now in the process of rolling out Clusters Segmentation across all campaigns and are using the personas to brief the supporter acquisition agencies which is already showing significant early signs of success with increases in conversion rates for cold campaigns. The measures of success across the organisation are changing. The board are now focussed on LTV, retention, acquisition and reactivation rates rather than short-term product measures.

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