Evaluation and user experience design of a financial trading platform
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While it is easy to measure the financial performance of a service, the quality of its user experience is intangible and is what drives success. Embedded within our client’s team, we measured the service outcomes of a trading platform, and improve the onboarding of new financial products.
We started by creating journey maps of traders and financial advisor. We identified expected outcomes for each service interaction, to evaluate their work processes.
Starting from outcomes, we prototyped metrics around user, business and operational goals and validated them with stakeholders, to understand how metrics would inform their decisions and help self-evaluate their work.
We finally developed the ‘Path to Data’ that visualizes the flow of data that feed the metrics and how to source them, which is a guideline to design new touchpoints that automate data acquisition.
The Process in a nutshell
1 — Understand the work model: Instrumenting the Journey
We interviewed digital and business analysts to create a blueprint of the current state and identified pain points and service outcomes for each user type — client, business and operations.
An outcome is what a service accomplishes for its user with a specific interaction. (e.g. The outcome of a successful client onboarding is that client support is not overwhelmed with requests).
2 — Temporary Work Environments: Prototyping Service Metrics
We adopted a service metric framework shared with Livework, that connects each qualitative outcomes with signals that allow to observe its related interaction. Once we recognized signals , we drafted metrics that could quantify them, in form of numbers or percentages.
(e.g. phone calls are the main signal to measure efficiency of product launch, because traders call advisors if something is not in order or unclear; the metric can be ‘Number of inquires per day’).
3 — Scaling solutions and design touchpoints: Path to Data and Future State UX
We worked toward activation of the metrics by defining what data sources to establish and new touchpoints to design.
To do that we created a tool named ‘Path to Data’, that gathers all information for the data on each metric, to flow from the traders interactions to the service team.
Work Model
We started the project by understanding how the service was run from an internal perspective, collecting insights from all teams involved on operational aspects and client relationships. We paired this discovery activities with client interviews to produce a journey map of the current state and define what we call Level 0 Architecture of the service: the general view of the end-to-journey, from the perspective of the user, business and operation stakeholders.
Instrumented Journey
On this Level 0 we mapped additional layers of pain points, gaps in satisfy user needs and sub-journeys; user types and exceptions.
We also ‘instrumented’ this complex mapping system with outcomes for each service phase and for each organizational layer — client business, operations.
Mapping the outcomes on the Level 0 of the service show stakeholders connections for implementations within their areas.
Temporary Work Environment
For a period of time we tested 12 drafted metrics (4 for each service outcome category) by presenting them to traders and data analysts and asking if those ways of measuring outcomes were valuable to inform their decisions.
Some key characteristics differentiated the research process for the validation of metrics, from a discovery research: it allowed to iteratively detail the existing blueprint; It was highly agile and iterative, we updated metrics after each interview; we asked not only about the value of the metrics, but also about the syntax and relevant language that could work within the organization to understand what the metrics indicate.
At the end of the prototyping time, we annotated signals and metrics on each outcomes’ swim lane on the Service Blueprint, to keep them in synch with the user experience of the service.
This structure of the Blueprint illustrates the work model behind the trading platform, including how outcomes and signals can be observed at any stage.
Scaling Solutions
To start using the metrics, we operationalized this framework working directly with the client’s service and digital team. Key to this activation was a document that we called Path to Data: a map containing the full narrative and profile of each metric, that is in sync with the blueprint, and drives the implementation of metrics by showing where and how to source and track data. This map informed changed in the blueprint to include touchpoints that would allow to track data and qualitative information while using the platform.
To close the project we translated all the changes in a future state journey, including the new touchpoints and we visualized it into a high level UX story to hand over for the implementation phase and the automation of the metric tracking.
Reflection
— Service metrics are not ‘objective’, nor solely based on efficiency, they are affected by underlying values that the service providers want to embed in the service interactions. (e.g. Speed of delivery should be a metric if the provider values time investment from users).
— Measuring a service is the result of a human centered research process, to uncover information and facts that motivates service providers to change behaviors or work processes. Prior to the design of metrics, the organizations has to evaluate its willingness and capacity to change.
