Business Figures: making insights easier for SMEs
FigureNZ
Background:
In 2016, Figure.NZ and ASB partnered, along with Statistics.NZ - As part of this partnership, a POC project was developed to encourage use of data-informed decision-making in NZ small business owners.
The Problem:
Looking into how we could utilise maps onto the platform
Interviews with small business owners found that they lack time and resources to find data about their industry. When they did go looking for information, sources were often confusing to navigate and overwhelming. Information they desired most was benchmark data, industry trends, customer demographics and context on physical locations.
This data is often essential for them to justify decisions to investors or the bank, or to give them confidence in which direction to head in.
The Idea:
Our product feature prioritisation workshop
Create a tool to that simply provides a user with information relevant to their business in a way that is easy to understand, and encourages exploration of other types of information about their industry.
The Process:
Conducted roughly in this order, and looped back a few times. My roles were in the following:
Research:
Paper sketches and planning
Interviews with bank managers on SMEs
Investigate current SME insights tools available and gap analysis
Scope/strategy:
Understanding stakeholder requirements
Monitoring and prioritising budget
Planning out 6 month timeframe
Determine development and design resources available
Discuss technology requirements and current capabilities
Product goals and success criteria
Example of a series of workflows
Discovery/analysis:
Interviews with small business owners
Running team day workshops on product ideation, user scenarios and feature prioritisation, using 1 metric tonne of post-it notes.
Personas and user stories
Customer journey workflows
Design:
Sketching: Paper sketches, so many paper sketches
Wireframing: built in Omnigraffle
Prototyping: interactive prototype built on Omnigraffle and Git-Pages, to present to stakeholder and user testing.
User testing: Recorded sessions on users interacting with prototypes.
Data modelling: introduction of enumerations and relations to Grace, improved elastic search.
Production:
Final product
Minimal Viable Product (MVP) design and testing
Recorded user testing of MVP
Shift from Google Analytics to Woopra and Heap analytics and setup
Release schedule
Post Launch:
Monthly stakeholder reporting
Iterative product development on initial release