Bite size and good enough is better than perfect for an initial MVP

• Delivering the MVP for Zego was time critical. Here are some of the decisions we made that helped us deliver the agreed scope on time:
• Using Github as a pricing configuration store
• Pricing model acceptance tests helped Zego discover and eliminate bugs
• Deferred building a perfect product in favour of a usable product
• Evolving the product architecture depending on requirements

Throughout the project we fostered transparency and accountability with stakeholders using clear (high level) architecture diagrams on how we would get there. We were then able to slice the deliverables into 6 Sprints, each with a clear goal. This gave busy stakeholders a high-level, realistic understanding of how outcomes would be achieved, ensuring buy-in and realistic expectations. It also allowed the team and stakeholders to prioritise their time to ensure we met targeted timescales.

Accelerating delivery through agile ways of working

We started this project by adopting the build, measure and learn approach. This outcome-based process enabled us to measure the success of the delivery and identify the key learnings of the MVP, whilst simultaneously building and delivering the product. We pursued the following opportunities to maximise efficiencies and deliver an initial working solution rapidly:

• A consistent (and accelerating) cycle of releasing pricing improvements into production.
• Live data pricing experimentation in each insurance product line to drive improvements. We started with Zego’s VAN product then worked extensively with their TAXI product, choosing this as the launch product for the integration and release of the three new platforms (Pricing and Risk, Insurance, and Data).
• Engaging with capacity providers using experimentation and analysis to drive down approval times and build trust.
• Shared cross-functional priorities in the business units and data platform teams.

An intensive two weeks discovery phase prior to initiating inception by the delivery team helped us to define and refine the problem statements, and understand the business and technical landscape so we could scope the inception. It also established close collaboration and transparency with the senior stakeholders at Zego.

In this way we were able to deliver an MVP in just 12 weeks, launching a single prototype pricing capability into production to ensure our foundational capabilities delivered the following outcomes:

• Internal user benefit: a new product configuration store allowing pricing models to be updated quickly and accurately by the pricing teams, reducing engineering effort.
• Benefit to the business: foundation components in place to provide feature parity of the existing engine.

Stakeholders immediately saw the business value and the engagement was extended to advance the solution to live production. This involved radical changes to the existing setup, with enhancements made that would dramatically improve Zego’s customer offering.

We tested the components built in MVP in shadow production, then continued iterating the MVP and building production-ready services to unlock new capabilities like underwriting checks. This ensured scalability for more of Zego’s insurance products to start serving live B2C customers, whilst guaranteeing accuracy and performance.

Adding business value through improved ways of working

This initiative was technology driven, but as we started to take the product towards live production we hit some roadblocks, and it became clear that there was a lack of alignment between business units within Zego and the various platforms they were using.

This was a pivotal moment for the business as we worked to help define a unified roadmap for all dependent services; it triggered a milestone project for Zego which unblocked challenges in serving live traffic and reducing dependencies, and successfully catalysed a change in culture and ways of working to bridge the gap, creating collaboration and alignment they had not seen previously. Senior leadership discovered that product and engineering teams needed to work together seamlessly, and Zego is now a product-driven business.
The result was a pricing and risk engine that provides a consistent view of clearly defined risk data which enables all users – from data scientists and data enrichment teams to pricing actuaries – to work cohesively with the same goals and tools.
The team (on both client and consultant sides) was universally acknowledged as exceptional by key stakeholders:

“Equal Experts is the only team in Technology delivering on time and operating with transparency on progress… There’s nothing we can’t throw at you that Equal Experts doesn’t respond to in a positive way.” Vicky Wills, CTO at Zego.

The Zego project lead said that the EE team was setting a high bar and was the “gold standard” for how teams should operate in Zego.

When the Zego pricing and risk engine went into live production, it was with zero pricing errors on live policies sold, thanks to the novel acceptance test suite now used by all Zego’s pricing analysts. We also saw an efficiency improvement, by reducing the manual steps required of pricing analysts, saving them considerable time.