The problem to solve

I led discovery to reframe the business problem, align executives on risk‑aware scope, and define a phased, evidence‑led approach that prioritised fairness, visibility, and trust.

• Reached agreement on framing the problem, business risks, guardrails, and decisions
• Led product feature ideation workshops, journey mapping, and feature prioritisation
• Prioritised feature roadmap by impact and downside risk using Kano analysis
• Tested assumptions quickly without over‑investment using AI-accelerated design

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I needed to reconsider the customer experience for rewards to introduce points redemption processes for both outright purchase and ongoing repayment plans. I also needed to introduce member authentication to the flow, to enable personalisation and rewards membership functions.

The problem to solve

To design feasible and desirable personalisation, I needed to redesign the customer flow

• When was the earliest that an influential and viable personalised price could be shown?
• How to balance additional complexity of points currency with the desirability of rewards pricing?
• How to design an engaging experience that had a viable backstage process?

The work done

• Analyse competitor strengths and weaknesses with heuristic evaluation
• Synthesise customer experience principles to influence decisions
• Co-create the experience to understand the best fit within constraints
• Test the customer experience with an iterative prototype
• Monitor engagement, conversions and feedback after launch

Strengths, weaknesses and opportunities

To answer the question, “why buy from Telstra?”, we needed to synthesise design principles for our rewards program so that we could evaluate our design ideas against a ‘north star’.

I used a heuristic analysis to evaluate 12 competitor rewards programs. For each key experience, noting moments of joy, integrations, touchpoints, frustrations, trust and transparency, and the ease of use including constraints of how and when rewards currencies could be used.

I synthesised the following rules of thumb from what was working, and what wasn’t.

Preparing for usability testing

After facilitating co-design sessions with key stakeholders from product, design, technology, legal, and customer service, I evaluated 4 options for flows against each of the 4 customer experience principles.

I prepared an interactive prototype of the proposed solution for message placements, progressive personalisation and authentication, rewards redemption, and discounted price.

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I was asked which of two shopping cart designs was more likely to meet user needs. A live test wasn’t practical, so I needed data to inform a high-risk decision.

I proposed a robust and achievable research plan to support a redesign of a shopping cart. When the need arose for unplanned research, I needed a way to get more customer insights and keep the rest of my schedule intact.

Urgent unplanned work is not ideal, but when new discoveries raised important questions, I needed to adapt my plan. Although qualitative activities could have given us more depth, they would have disrupted my scheduled activities, inconvenienced participants and exceeded my budget. Quantitative, unmoderated research was an achievable and effective way to get a ‘quick read’ of categorical preferences.

As part of a broader research program that included multiple research methods, planning a contingency for quantitative design evaluation got the best outcome for the project.

The question, “Which of these two design concepts will we develop?”, could have been researched using multiple methods but the question was one of probability. Which design, A or B, was most likely to meet user needs?

The problem to solve

• Design axioms (rules of thumb) and customer feedback indicated two potential design solutions (A or B)
• Both designs (A and B) followed reasonable practice and weaknesses were not immediately obvious
• Axioms for simplicity and affordance when taken to extremes, appeared to conflict
• Preferences were important and experience (not price alone) was driving channel value

Approach

• Participants used a mobile prototype to perform tasks and answer questions on their own device
• 300 participants were split into two balanced cells with similar characteristics, and each cell rated one design
• Non-parametric tests found statistically significant differences in how the designs were rated
• Results were shared quickly, followed by a report and a solutions workshop

Report

The report drew conclusions by analysing closed-question responses (what responses participants chose) with sentiment analysis of free-text responses (why they chose that), and a statistical test on how customers rated each design (A or B).

Non-parametric tests were used to report on statistical significance of preferences. This analysis was the first step of an ongoing optimisation experimentation program, and the methods evolved after live experimentation capability was deployed.

It’s important to note that although analysis revealed a statistically significant result by comparing 2 variations, and did so at the early stages of design before development, that this experiment wasn’t concluded until an uplift was observed in production.

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Using regular customer feedback, I helped a mobile app developer increase their wallet-share, and built the business case for further customer research.

The research brief

As UX Researcher it was my responsibility to support the decisions of a team of 15 product owners, 15 designers and an executive board.

Product

• Sportsbet is Australia’s leading mobile app in the gaming category
• Half a million customers spend up to $1 million per day
• Thousands of products across two websites and two mobile apps

Business goals

• Differentiate the Sportsbet experience from competitors by being responsive to customers
• Experiment and test to deliver innovative, targeted and safe experiences
• Support development by mitigating potential risks of negative reactions to change

Customers

• Market share is driven by customer choice of provider
• Customer choice is driven by quality experiences and rewards
• Most customers regularly choose two or more providers
• High-value customers have low loyalty and switch regularly

My research approach

Found opportunities to guide decisions

• Embedded myself into the product team (not the development team!)
• Unpacked solutions and assumptions into questions
• Recommended appropriate research methods (not just surveys!)
• Answered research questions and gathered hundreds of new insights
• Participated in regular product ideation and discovery sessions
• Shared design insights the day after, followed by weekly reports

Validated evidence by correlating attitudes and behaviours

• Managed participant recruitment, using data to target specific cohorts
• Observed pain points of customers as they used their preferred products
• Quantitatively measured severity and impact of qualitative issues found
• Prioritised actions taken using quantitative data to size each opportunity

Integrated research into agile development

• Designed a research process that integrated into fortnightly development sprints
• Researched features for each upcoming sprint and shared insights before build commenced
• Collaborated with stakeholders to prioritise research request backlogs
• Collaborated with stakeholders to agree on the value and urgency of opportunities found

Key design challenges

Here’s a small sample of the types of insights generated from my customer research.

For each finding, evidence was written up in a report, analysis was presented, recommendations were discussed, and follow-up actions were prioritised.

Help customers to discover products

I researched customer discovery of entry, exit and re-entry points for core journeys like registration, product selection, and product purchase.

• Product choice added flexibility but placed pressure on users to manage choice complexity
• Useful product suggestions required intuiting customers’ current mindset and goals beyond historical actions
• Poor personalisation trained customers to ignore marketing and product suggestions
• I researched influence opportunities for omni-channel journeys (radio, TV, print and in-person)

Match content to customer mindsets

Recruiting customer participants with carefully managed customer cohort data meant that I could match prototype content to product preferences and better simulate machine learning personalisation.

• Recruited participants that were representative users of the content being tested
• Increased content relevancy prompted exploration and increased perception of service value
• Presentation of personalisation was critical to mitigate ‘creepy’ monitoring that degraded trust
• Personalisation enhanced the memorability and enjoyment of key ‘brand moments’

Ease core journey friction points

By observing participants as they used the product in a naturalistic way, the impact of friction points became clear.

• Poor accessibility on data entry forms increased uncaught human error for important information
• Journeys without consistent error recovery increased anxiety, sense of loss, and degraded trust
• Higher error rates observed on small elements near mobile screen edges
• User accuracy was impeded near physical edge bevels of wrap-around mobile device screens

Redesign shopping cart journeys

Initial experience of product innovation relies more heavily on journey design than instruction.

• Multiple journey entry points were required to complete combination products
• Established behaviours influence combination product choice more than instructions
• Unclear eligibility in marketing messages caused disappointment and loss
• Lower value offers prompted unfavourable comparisons

A solid business case for research

• Costs

  Research costs comprise software, recruitment, incentivises and consultancy at $300,000

• Growth

  Measured growth is 10%, comprising $1,000,000/day (actual) minus $900,000/day (previous trend)

• Cost recovery

  At 10% growth, $300,000 cost is recoverable 3 days after launch of the new design

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