NCS - Improving programme atendance.

Using data science to ensure 50,000 young people get the best start in life.

National Citizen Service (NCS) is delivered by a national network of hundreds of partners across the country.

80% of programmes are delivered in Summer, the remainder in Autumn. NCS has recently established its Advanced Analytics function to make best use of the performance data in the central data system based on Salesforce. The function aims to provide decision makers across NCS Trust and the partner network with relevant and timely insight based on deep understanding of performance drivers.

Improving no shows in the younger generation

NCS approached Engine as they wanted to reduce the number of young people who sign up to use their services but don’t turn-up, and to improve their programmes. There were three key challenges that needed overcoming using advanced analytics:

Attrition model (drivers/forecast): Tool uncovering indicators of successful recruitment performance

Customer experience drivers: Classification tool taking customer feedback and categorising it using machine learning

Auto-allocation tool: Course recommender tool for when a young person signs up

The desired outcome was to analyse their Salesforce data, build systematic processes and expose via their Business Intelligence Tool. To reach this objective we worked closely with NCS to conduct the following:

Immersion: Assessing the NCS strategy/objectives of the Advanced Analytics Team, systems, data and the key processes and technologies being used.

Data Discovery: There were two areas that we focused on:

1) Access/exploration: Accessing NCS datasets and using analytical tools to interrogate and understand the data structures, relationships and quality.

2) Governance: Looking at the datasets and methods considering GDPR and Data Ethics. The NCS recruitment process gathers diversity and medical information and its use within the ML models was a concern. In addition, we established the NCS Data Governance and Data Ethics Board.

Tool Development:
Using a standard data science approach; our teams used statistical analysis and algorithms to look for ‘markers’ and ‘patterns’ within the datasets that correlated with the key performance drivers for the tool we were developing.

Using machine learning to make better and faster decisions. From classifying text-based feedback, to assigning attrition prediction scores, to automatically allocating young people to the most suitable course.

Upskilling the team:
Engine focused on upskilling the newly formed NCS Advanced Analytics team. We had an ‘enablement’ workstream where we ran workshops and paired with NCS teams to upskill their team. This covered a wide syllabus from process through to adjacent areas such as Data Engineering and DevOps.

The tools Engine developed now sit within the NCS environment, producing outcomes in real-time for teams within NCS to make more timely decisions.

The attrition prediction tool assigns attrition prediction scores in real-time, allowing NCS decision makers early intervention on the journey from expression of interest to sign up.

Via the text analytics tool API daily feeds of texts and calls are categorised and fed back to Salesforce.

The auto-allocation tool acts as a “recommender“ for when a young person signed up. The tool automatically recommends them for the most suitable course based on fill levels, suitability, location, amongst other things. The tool has replaced a very time consuming and costly manual exercise for the NCS allocation team.

Our work has ensured NCS can achieve their analytics vision to empower and equip all decision-makers, from frontline delivery staff to the Board, with the right information and insights, at the right time.

Engine successfully developed and productionised all three tools; this results in potential savings in reduction of no-shows by 5% - 13% which equates to a large saving. We also helped to create organisational capabilities and patterns/process for NCS to advance their analytics team, to develop new tools and refine the ones we helped deliver.

I highly recommend Engine for data and analytics work. They helped us move into the space of Advanced Analytics by designing and deploying three predictive analytics tools. I was very impressed with the width and depth of Engine’s skills set. They brought specialists for every area, from data engineering to data science and from agile project management to DevOps and deployment. They connected well with all relevant parts of out technical teams about the quality of their work. Last but not least, working with Engine was a joyful experience: every member of the team had a can-do attitude and was passionate about delivering results, which fit in really well with our culture.

Jeroen Ssabbe, Director of Advanced Analytics and Insights, NCS