Background
Guide Dogs consists of stakeholders, including customers, volunteers, donors, employees, and trustees. Guide Dogs work as a team to deliver mobility and access services that meet the needs and aspirations of blind and partially sighted people in the United Kingdom.
Guide Dogs was founded in 1931 and is a charity that receives no government funding for the guide dogs service. The guide dog service is entirely dependent on voluntary donations. The guide dog service costs the guide dog owner a nominal fee of just 50 pence to ensure equality for all in the UK.
Guide Dogs are a world leader in breeding and training guide dogs and are a co-founder of the International Guide Dog Federation. The charity employs 1000 professional staff in the UK supported by 10,000 volunteers, including puppy walkers, brood-stock holders, dog boarders and thousands of local fundraisers.
Guide Dogs wished to identify location hot spots in the UK for holding small pledging events to target prospects, to improve the cost effectiveness of their pledger recruitment programme and to increase the number of charity supporters who pledge to leave a legacy gift in their will. The project was developed through a team effort involving the Guide Dogs data team and legacy fundraising team and included data analysis, model building, strategy development, and implementation and the subsequent roll out the strategy in a production environment.
The Apteco Solution
Guide Dog’s supporter management system provided automated weekly data feeds of all supporter and transactional data into a single customer view database. Data was then cleaned before being delivered to users through Apteco FastStats, who could then create and implement their data-driven supporter journeys. The project untaken by Guide Dogs and their partner Qbase, had 4 stages – 1: Identify the variables that predicted pledger activity, 2: Create models to score supporters based on propensity to become a pledger, 3: Geo-spatial analysis to plot top segments and compare to known pledgers, 4: Action and implementation.
Stage 1 was to identify the variables that predicted pledger activity. Transactional variables were created and tested based on 4 giving products - cash, raffle, Sponsor-a-Puppy and Dogalogue. Profile reports were created, enabling comparisons to be made across each variable and to rank them according to predictiveness. This was done by comparing the profile statistics for known pledgers versus general supporters. Stage 2 of the project was to create Decision tree and Predictive Weight of Evidence (PWE) models to score supporters based on propensity to become a pledger. Selections were then made from each model based on the PWE score, and the resulting supporters were captured in Apteco FastStats to produce the final audience. Geographical analysis was then undertaken in stage 3, using Apteco FastStats integrated with MS Mappoint. Actual pledgers and the top segments of modelled potential pledgers were plotted, and the most suitable locations were identified, based on drive-times, profiles and pledger propensity scores. As a result of the extensive analysis, the implementation stage – stage 4 could be actioned. Invitations were then sent to the best potential pledgers inviting them to attend events held at hotels around the country. The top propensity potential pledgers were asked if they would like to receive a home visit if they couldn’t attend the event. The next propensity segments were telephoned if they didn’t respond. The lower propensity segments were sent a direct mail piece if they didn’t attend.
All objectives were achieved – more pledgers and less cost! Overall, the success of the project meant that the number of pledger prospects and legacy pledgers had increased above target.
The impact on the business
Project metrics were determined and used to judge the success or failure of the approach. Benchmarking was based on comparisons to previous years. With all KPI’s from the previous year being beaten the project was deemed a success and showed that the quality of the attendees was much better, with conversion rates being higher than had ever previously been achieved. During the telephonic activity, it became clear that many of the attendees wanted to attend and were interested in leaving a legacy but were unable to travel. Based on the telephone feedback, the strategy was then amended to include home visits to supporters with the highest propensity scores. The legacy team also moved many of the events to out of town-based hotels and into smaller venues that were closer to the attendees. This was reported to improve interaction and one-on-one conversations even further. Understanding the supporter experience and being able to proactively evolve the supporter journey had the knock-on effect that event costs were reduced through cheaper, smaller venues. A more personal service was delivered to supporters via home visits to the highest propensity supporters and minimising travel to events for the lower-scoring supporters.
Guide Dogs now invite fewer attendees to events (costing less) but are able to recruit more pledgers
Following this project pledger conversion increased by 4%, prospect conversion improved by 7%, and pledger and prospect income per attendee also increased significantly
Due to the events being smaller than previously, the number of attendees per event also decreased and so cost less to run, yet interaction improved as the events were more personal than before
The skills and learnings from this project enabled Guide Dogs to move the strategy into a production environment with internal data and legacy teams working together to continuously improve the strategy
Understanding their supporters pain-points meant that the team was able to proactively evolve the supporter journey, improving supporter experience
All data is 'hygiened' and deduplicated in the SCV, before being fed into Apteco FastStats on a weekly basis. All the data manipulation and analyses are undertaken within FastStats and Guide Dogs have 10 members of staff who all utilise FastStats to create and implement their data-driven supporter journeys.