Offering personalised investing advice to millions of customers

When Halifax approached us, they wanted to create a new service that would help unexperienced investors make a decision they could trust. Through a series of quick cycles of research, design, prototyping and testing, we created, built and launched Halifax’s first fully digital investing service.

My role

User testing

Type of project

Product Design

Project setup

The project was split in three phases, each one building on the learning of the previous.

Phase 1

Due to the time constraints of this projects timeline, we knew we needed a fast, iterative approach. This phase was setup to run quick cycles of research, design and testing. Phase 1's objective was to create a proof of concept.

Phase 2

This phase narrowed the focus of the work on the on specific parts of the journey that proved difficult for the user (i.e. educationing user on investing concepts and risk level selection).

Phase 3

The design time of the final phase was dedicated to refining the assets and work alongside developers to build the product.

Key learning: Truly personal recommendations

Problem: making recommendations feel truly personal

How do you create a digital service that feels like you are talking to an advisor? Our initial assumption was that capturing user data in an engaging way was key to make the process feel personal.

Initial explorations tried to capture data via natural language forms as if you were having a conversation with a Financial Advisor.

The solution: it’s not about how you ask, it’s about giving good advice

The key lesson for us was that capturing the data was not the issue. What really made a difference in engagement was to make small recommendations through out the journey.

To make the journey feel more personal and dial up the value of the advising service we used a tone of voice that was personal and conversational.

Key learning: Educate along the way

The complexities of investing lower users' confidence in their choices

In order to help make a choice people were confident in, our service needed to provide some form of education to help them grasp complex investing concepts.

Problem: educating customers

Our initial explorations focused on providing educational content at the beginning of the journey.
But user often skimmed through our content just wanting to see what the service is about. Those who read felt education was dense with unfamiliar concepts.

The solution: contextualise education

In the end we realised that in order to educate our customers we had to feed them information when thery were more likely to retain it. Moreover, people are accustomed to ignoring modals and pop-ups, so we broke up the education into chunks and showed as integral part of the journey.

Key learning: No one gets graphs

Problem: explaining risk and rewards

Our initial design were failing in explaining risk and rewards. One reason for that was the significant change of pace and interaction. The journey went from collecting data via forms to a series of screens with graphs and text. This change of pace and interaction caused confusion.

Secondly there was a significant overload of information. The risk screen combined graphs with text trying to explaining abstract concepts such as loss, reward and historical data.

The solution: clear tone of voice, and no graphs

The winning solution simply dropped any graph from the interface. Instead we used simple figures supported by labels that are easily understood (best, worst, average).Finally the values we used where based on their intended investment value. This helped contextualise the numbers to something they could relate to.

Key learning: Being transparent

For someone to decide to invest large sums of money, transparency is key

Our service needed to be clear on why it was making certain recommendations and why those were the best choice for them.

Problem: people don’t trust a ‘black box’

When investing large some of money users want to be sure they are doing it for the right reasons.

This meant making sure that we took the time to explain how certain recommendations were made.

The solution: protect users from making bad choices

People make mistake, or they underestimate the risk involved with certain decisions.

Our service needed to be clear with the user on how it was protecting them from making bad choices (e.g. investing all of their savings) and stop them when needed.

Our learning was that a bit of friction is welcome when dealing with important monetary decisions.

Delivery: edge cases

This was a complex product, since it had to account for different scenarios and flows. Part of my job was to map it all out account for edge cases.