Dan Foster
Learning Experience Design

I help organisations move beyond tick-box compliance and one-off training events. Engaging, evidence-based learning experiences designed to change behaviour.

• PROJECTS
• SKILLS
• EXPERIENCE
• CONTACT

Projects

Skills

I combine creativity, analytics, and instructional expertise to deliver impactful digital learning experiences.Core strengths include:

Contact

Regulatory Remedy

Transforming regulatory learning at a major UK insurer

Why was this project a success?

I used data to create a mandate for change: Nobody wants to do a bad job. But user data told us that our colleagues demanded more. I created an interactive dashboard to visualise user feedback and began to build the case for change – first with my leadership team, then with each of the 12 compliance topic owners.

I brought a compelling vision: “I want to build something we can be proud of. Something that feels modern and impactful. I want to present your topic in a way that delivers value for our business and our people. No more eye-rolling every quarter. I have the skills and vision to get us there, all I need is your trust and expertise.”

I made decision-making the focus of compliance learning: Reorienting my stakeholders away from what people need to know, to what people need to do was crucial. Following this change, training centres on action rather than theory – identifying triggers and choosing the appropriate response.

What was the impact?

Tools and technology

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Building a Data Culture Inside L&D

Most L&D teams measure learning through completion rates and post-course surveys. I wanted to know more. Over the course of a Level 4 Data Analyst apprenticeship, I designed and delivered three analysis projects that gave my team something it had never had before: a systematic, evidence-based view of how learning was performing – and the automated tools to keep it updated.The technical progression was real, moving from Excel dashboards through to Python and machine learning. But the bigger shift was cultural. By the end, my team was making decisions differently.

The Problem

Learning & Development teams generate enormous amounts of data – LMS interactions, user feedback, training records, survey responses. At Direct Line Group, almost none of it was being used. Reports sat in spreadsheets. Feedback was buried in a web interface nobody checked. Decisions about which content to commission, renew, or retire were based on intuition and anecdote.
I set out to change that – project by project, tool by tool.

Overview: Three Projects, One Direction

Project 1: MindGym Usage Dashboard
Who is using our learning content, and when?Built in Excel with Power Pivot. Revealed that promotional campaigns drove short-term spikes but not sustained engagement — directly informing a commercial contract decision.

Project 2: Learning Management System Ratings
What do learners actually think?Built in Power BI with full end-to-end automation via Power Automate. Turned a feedback system nobody was reading into a weekly, self-updating intelligence tool.

Project 3: ILT Ratings – Does quality of materials predict learner confidence?
What actually drives learning effectiveness?Built in Python with Jupyter Notebooks and scikit-learn. Used linear regression and statistical hypothesis testing to find a measurable link between training material quality and learner confidence.

The Outcome

By the end of these three projects, my L&D team had:

And a proof of concept: that end-to-end data automation was possible within our existing tech stack.

Tools and technology

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Does Our Training Actually Work?

Finally, an answer.

It’s the question every L&D professional should be able to answer and almost none can. For years, DLG’s Learning Experience team evaluated training through engagement rates and user feedback. Both are useful. Neither tells you whether the training changed anything.For my Level 4 Data Analyst EPA project, I set out to answer it properly. That meant accessing data that L&D had never touched, building relationships across HR, operations, and a data warehouse team, and engineering a dataset complex enough to track individual employees from their first week on the phones through to twelve months in role.The answer – and what to do with it – is what this case study is about.

The Problem

My organisation’s Motor Insurance contact centre runs a structured induction programme for all new Retention handlers. These employees are critical to the business: their role is to prevent customers from cancelling their policies. Marginal improvements in their performance translate directly to revenue.Despite this, nobody had ever looked at whether the induction programme was working. Not because the data didn’t exist - it did, in abundance - but because it lived across three separate enterprise systems that L&D had no access to, no relationships with, and no framework for joining.The hypothesis was simple: if we can benchmark new inductee performance against experienced staff, and track how that gap closes over time, we can make targeted decisions about training content, timing, and sequencing.

What I Did

Data sourcing and access:
I worked with the Motor Performance team to gain access to trading reports held on DLG’s AWS cloud data warehouse — data that L&D had never accessed before. Nine source files were identified across three systems (Workday, iLearn, and the iMI trading platform), totalling over 1.2 million records and requiring a secure Citrix connection for export.

The analysis, in brief:
After an extensive data engineering phase, I conducted an exploratory analysis of Retention KPIs — call volumes, call-to-save rates, renewals, customer feedback (MyCustomer), and quality assurance scores — across four employee populations: new inductees, existing staff, offshore contractors, and Sensee third-party contractors. I then built four linear regression models (one per tenure band) to predict the number of policies saved per 100 calls, and investigated the relationship between cross-skill training and performance uplift.

The recommendation:
New inductees become broadly comparable to experienced staff on most KPIs at around four to six months. Call-to-save — the most commercially significant metric — takes longer, and the data suggests the turning point coincides with a cross-skill training programme typically delivered at six months. My recommendation: pilot bringing elements of that training forward, measure the results against the benchmarks established here, and use A/B testing to validate impact.

The Outcome

Tools and technology

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Your Voice

Enabling knowledge sharing and amplifying colleague voices through user-generated content.

Why was this project a success?

The studio was designed for everyone, not just creatives: Most colleagues don’t think of themselves as video producers – and they shouldn’t have to. The studio’s user-friendly system meant anyone could walk in and start recording without technical knowledge. To make the experience even smoother, I created digital training resources accessible via QR code, so users could quickly launch step-by-step guides. The result was a space that felt exciting and empowering rather than intimidating.

It broke a bottleneck that was slowing the business down: Before the studio, video production meant joining a long queue for the company’s London-based Design Team, or settling for low-quality webcam recordings. The studio gave teams across the business a fast, professional alternative – putting high-quality video capability in their hands without the wait, cost, or compromise.

It opened up new ways of learning across the organisation: The impact stretched well beyond the L&D team. Teams who had always relied on face-to-face delivery, like those responsible for health and safety training across our vehicle repair network, were able to create professional video-based training for the first time, reducing costs and enabling on-demand access for a hard-to-reach workforce.

What was the impact?

Tools and technology

• HOME