Switch
& Save
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
overhead title
Detail
overhead title
Detail
overhead title
Detail
overhead title
Detail
Context & problem statement
Context
A large majority of users carry over balances on their credit cards, often paying high interests on this debt.
We’ve calculated that we could help save ~£200M across our users if they switched their credit cards to balance transfer cards available in our marketplace.
Users don’t currently know how much they could save with a balance transfer card and so are less motivated to switch.
‘Assumed’ user problem
Users with outstanding credit card balances often pay high interest without realising they could save by switching to balance transfer cards.
This leads to missed savings and low motivation to switch while managing their debt.
Existing relevant insights
- Over 231,000 users could save £576 on average by switching to a balance transfer card.
- Previous optimisations around ‘switching and saving’ have performed well.
- Personalised messaging always outperforms generic messages
Goals & Objectives
User value goals
- Delivering real savings by helping users reduce interest payments.
- Educate users on balance transfer benefits.
- Highlight personalised savings for users.
Business value goals
- Increasing card conversions from the marketplace.
- Boost engagement with balance transfer offers.
Success metrics
Metrics are measured against analytics from the live app.
Percentage of users that apply from the new product list page
Success
> 10%
Ok
7-10%
Fail
< 7%
Percentage of eligible users that start the new journey
Success
> 10%
Ok
7-10%
Fail
< 7%
Target Audience
Requirements
- Users we predict can save above £100 by switching to a balance transfer card.
- Users with at least a 70% chance of approval for the balance transfer card.
- Users that have revolved a balance for the last 3 months.
- Active & semi-active users (logged in within the last 6 months)
- Users who are not flagged to be on a 0% promotional rate
- Users with credit score above 300.
Personas
ClearScore has 6 personas — 4 of these are a target audience for this project and described below:
Money Maker - Maria
High earner with strong credit, rarely uses ClearScore.
Growth Phase - Grace
Busy working mum, tech-savvy and credit confident.
Mutual Resources - Mike
Older self-employed user, focused on clearing debt, uses ClearScore occasionally.
Deal Seeker - Deepak
Budget-savvy dad, always hunting for savings, uses ClearScore often.
Design Requirements
- Savings Calculation
• Show real-time savings using actual card data (APR, balance, payment).• Only calculate after eligible cards are returned.
- API Integration UX
• Trigger panel search via partner APIs. • Handle delays with smooth loading/progress indicators.
- Pre-condition Logic
• Require Offers flow completion (30-day + credit card). • Integrate into journey as needed.
- In-App Entry Points
• Add entry points from dashboard, cards tab, or credit report. • Target users with card debt or interest.
- Existing credit card input
• Allow editing of card details (APR, balance, payment).
- Results Presentation
• Show savings for top 1, 3, or all eligible cards. • Include APR, terms, eligibility, example. • Link to product pages.
- Unhappy Path
• Fallback if no savings or BT cards. • Suggest next steps or alternatives.
Design stage 1 OF 4
Discover
Our initial problem statement was based on the saving calculation and success of related optimisations. So needed to know users thoughts about switching or why users are not saving on their interest payments.
Assumed Problem Insights
DISCOVER
Research Topics Gathering
DISCOVER
Research Prioritisation
Importance knowledge matrix
DISCOVER
Top Priority Research Delegation
DISCOVER
Top Priority Research Topics
DISCOVER
Top Priority Research Topics
DISCOVER
Design stage 2 OF 4
Define
We needed to refine the numerous research findings into the most relevant insights and a thorough problem definition to start ideating from.
Unstructured Research Findings
DISCOVER
Findings Themes
DISCOVER
Key Research Insights
Insight 1
Personalised savings must be made clear and tangible
Users are more likely to switch when they clearly see how much they personally will save, presented at the moment they’re comparing options, rather than generic or abstract savings claims.
Insight 2
Educational gaps undermine switching confidence
When users don’t understand switching steps or terminology early in the journey, uncertainty builds and even motivated users hesitate or drop off.
Insight 3
Too many card options lead to decision paralysis
Large lists of similar offers overwhelm users during comparison, making it harder to choose and increasing indecision and abandonment.
Insight 4
Confidence in eligibility drives switching action
Users are more willing to apply when they feel confident they’ll be approved; fear of rejection or credit impact causes them to avoid offers, even when eligible.
‘Researched’ user problem
ClearScore users with credit card debt who are open to switching often feel uncertain and overwhelmed when exploring options in the app.
Unclear savings, a confusing process, and low confidence in approval prevent them from switching, even when it could reduce costs and interest payments.
Design stage 3 OF 4
Develop
With key insights identified and the problem defined, I started developing solutions – from the user flow through to conceptual UI.
Themes Problem Statements
DISCOVER
Themes Problem Statements
DISCOVER
Themes Problem Statements
DISCOVER
Themes Problem Statements
DISCOVER
Themes Problem Statements
DISCOVER
Design stage 4 OF 4
Deliver
Now the chosen solution was brought to life by building, testing, and refining it to ensure it works for our users and the business.
Themes Problem Statements
DISCOVER
Themes Problem Statements
DISCOVER
Themes Problem Statements
DISCOVER
Insights Problem Statements
DISCOVER
Screen Flow Diagram
deliver
Themes Problem Statements
DISCOVER
Themes Problem Statements
DISCOVER
Themes Problem Statements
DISCOVER
Themes Problem Statements
Sub title
DISCOVER
Themes Problem Statements
DISCOVER
Total number of user to apply through switch & save (in first 54 days)
1,471
Average calculated saving per apply click (in first 54 days)
£1,137
Estimated Total saving made for our users
(in first 54 days)
£1,672,527
If you like what you see
— Don’t be a stranger! ...
o.hardisty@me.com
Switch
& Save
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
overhead title
Detail
overhead title
Detail
overhead title
Detail
overhead title
Detail
Context & problem statement
Context
A large majority of users carry over balances on their credit cards, often paying high interests on this debt.
We’ve calculated that we could help save ~£200M across our users if they switched their credit cards to balance transfer cards available in our marketplace.
Users don’t currently know how much they could save with a balance transfer card and so are less motivated to switch.
‘Assumed’ user problem
Users with outstanding credit card balances often pay high interest without realising they could save by switching to balance transfer cards.
This leads to missed savings and low motivation to switch while managing their debt.
Existing relevant insights
- Over 231,000 users could save £576 on average by switching to a balance transfer card.
- Previous optimisations around ‘switching and saving’ have performed well.
- Personalised messaging always outperforms generic messages
Goals & Objectives
User value goals
- Delivering real savings by helping users reduce interest payments.
- Educate users on balance transfer benefits.
- Highlight personalised savings for users.
Business value goals
- Increasing card conversions from the marketplace.
- Boost engagement with balance transfer offers.
Success metrics
Metrics are measured against analytics from the live app.
Success
Ok
Fail
Percentage of users that apply from the new product list page
> 10%
7-10%
< 7%
Percentage of eligible users that start the new journey
> 10%
7-10%
< 7%
Target Audience
Requirements
- Users we predict can save above £100 by switching to a balance transfer card.
- Users with at least a 70% chance of approval for the balance transfer card.
- Users that have revolved a balance for the last 3 months.
- Active & semi-active users (logged in within the last 6 months)
- Users who are not flagged to be on a 0% promotional rate
- Users with credit score above 300.
Personas
ClearScore has 6 personas — 4 of these are a target audience for this project and described below:
Money Maker - Maria
High earner with strong credit, rarely uses ClearScore.
Growth Phase - Grace
Busy working mum, tech-savvy and credit confident.
Mutual Resources - Mike
Older self-employed user, focused on clearing debt, uses ClearScore occasionally.
Deal Seeker - Deepak
Budget-savvy dad, always hunting for savings, uses ClearScore often.
Design Requirements
- Savings Calculation
• Show real-time savings using actual card data (APR, balance, payment).• Only calculate after eligible cards are returned.
- API Integration UX
• Trigger panel search via partner APIs. • Handle delays with smooth loading/progress indicators.
- Pre-condition Logic
• Require Offers flow completion (30-day + credit card). • Integrate into journey as needed.
- In-App Entry Points
• Add entry points from dashboard, cards tab, or credit report. • Target users with card debt or interest.
- Existing credit card input
• Allow editing of card details (APR, balance, payment).
- Results Presentation
• Show savings for top 1, 3, or all eligible cards. • Include APR, terms, eligibility, example. • Link to product pages.
- Unhappy Path
• Fallback if no savings or BT cards. • Suggest next steps or alternatives.
Design stage 1 OF 4
Discover
Our initial problem statement was based on the saving calculation and success of related optimisations. So needed to know users thoughts about switching or why users are not saving on their interest payments.
Assumed Problem Insights
DISCOVER
Research Topics Gathering
DISCOVER
Research Prioritisation
Importance knowledge matrix
DISCOVER
Top Priority Research Delegation
DISCOVER
Research Questions & Goals
DISCOVER
Persona Prioritisation
DISCOVER
Design stage 2 OF 4
Define
We needed to refine the numerous research findings into the most relevant insights and a thorough problem definition to start ideating from.
Unstructured Research Findings
Define
Findings Themes
Define
Key Research Insights
Insight 1
Personalised savings must be made clear and tangible
Users are more likely to switch when they clearly see how much they personally will save, presented at the moment they’re comparing options, rather than generic or abstract savings claims.
Insight 2
Educational gaps undermine switching confidence
When users don’t understand switching steps or terminology early in the journey, uncertainty builds and even motivated users hesitate or drop off.
Insight 3
Too many card options lead to decision paralysis
Large lists of similar offers overwhelm users during comparison, making it harder to choose and increasing indecision and abandonment.
Insight 4
Confidence in eligibility drives switching action
Users are more willing to apply when they feel confident they’ll be approved; fear of rejection or credit impact causes them to avoid offers, even when eligible.
‘Researched’ user problem
ClearScore users with credit card debt who are open to switching often feel uncertain and overwhelmed when exploring options in the app.
Unclear savings, a confusing process, and low confidence in approval prevent them from switching, even when it could reduce costs and interest payments.
Design stage 3 OF 4
Develop
With key insights identified and the problem defined, I started developing solutions – from the user flow through to conceptual UI.
User Needs, Epics & Stories
develop
User flow options
develop
Chosen User Flow
develop
UI options
Capturing users existing credit card details
develop
UI options
Displaying users new credit card options
develop
Design stage 4 OF 4
Deliver
Now the chosen solution was brought to life by building, testing, and refining it to ensure it works for our users and the business.
Funnel analytics
First release
deliver
Data Led Design Iteration
deliver
Funnel Analytics
Second release
deliver
Key Scree
deliver
Screen Flow Diagram
deliver
Component Design
Viewing credit cards by categories
deliver
Designing for Commercial Risk
deliver
Component Design
Viewing & comparing credit card details
deliver
Prototype
DISCOVER
Success analytics
deliver
Total number of user to apply through switch & save (in first 54 days)
1,471
Average calculated saving per apply click (in first 54 days)
£1,137
Estimated Total saving made for our users
(in first 54 days)
£1,672,527
If you like what you see — Don’t be a stranger! ...
o.hardisty@me.com
Switch
& Save
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
overhead title
Detail
overhead title
Detail
overhead title
Detail
overhead title
Detail
Context & problem statement
Context
A large majority of users carry over balances on their credit cards, often paying high interests on this debt.
We’ve calculated that we could help save ~£200M across our users if they switched their credit cards to balance transfer cards available in our marketplace.
Users don’t currently know how much they could save with a balance transfer card and so are less motivated to switch.
‘Assumed’ user problem
Users with outstanding credit card balances often pay high interest without realising they could save by switching to balance transfer cards.
This leads to missed savings and low motivation to switch while managing their debt.
Existing relevant insights
- Over 231,000 users could save £576 on average by switching to a balance transfer card.
- Previous optimisations around ‘switching and saving’ have performed well.
- Personalised messaging always outperforms generic messages
Goals & Objectives
User value goals
- Delivering real savings by helping users reduce interest payments.
- Educate users on balance transfer benefits.
- Highlight personalised savings for users.
Business value goals
- Increasing card conversions from the marketplace.
- Boost engagement with balance transfer offers.
Success metrics
Metrics are measured against analytics from the live app.
Success
Ok
Fail
Percentage of users that apply from the new product list page
> 10%
7-10%
< 7%
Percentage of eligible users that start the new journey
> 10%
7-10%
< 7%
Target Audience
Requirements
- Users we predict can save above £100 by switching to a balance transfer card.
- Users with at least a 70% chance of approval for the balance transfer card.
- Users that have revolved a balance for the last 3 months.
- Active & semi-active users (logged in within the last 6 months)
- Users who are not flagged to be on a 0% promotional rate
- Users with credit score above 300.
Personas
ClearScore has 6 personas — 4 of these are a target audience for this project and described below:
Money Maker - Maria
High earner with strong credit, rarely uses ClearScore.
Growth Phase - Grace
Busy working mum, tech-savvy and credit confident.
Mutual Resources - Mike
Older self-employed user, focused on clearing debt, uses ClearScore occasionally.
Deal Seeker - Deepak
Budget-savvy dad, always hunting for savings, uses ClearScore often.
Design Requirements
- Savings Calculation
• Show real-time savings using actual card data (APR, balance, payment).• Only calculate after eligible cards are returned.
- API Integration UX
• Trigger panel search via partner APIs. • Handle delays with smooth loading/progress indicators.
- Pre-condition Logic
• Require Offers flow completion (30-day + credit card). • Integrate into journey as needed.
- In-App Entry Points
• Add entry points from dashboard, cards tab, or credit report. • Target users with card debt or interest.
- Existing credit card input
• Allow editing of card details (APR, balance, payment).
- Results Presentation
• Show savings for top 1, 3, or all eligible cards. • Include APR, terms, eligibility, example. • Link to product pages.
- Unhappy Path
• Fallback if no savings or BT cards. • Suggest next steps or alternatives.
Design stage 1 OF 4
Discover
Our initial problem statement was based on the saving calculation and success of related optimisations. So needed to know users thoughts about switching or why users are not saving on their interest payments.
Assumed Problem Insights
DISCOVER
Research Topics Gathering
DISCOVER
Research Prioritisation
Importance knowledge matrix
DISCOVER
Top Priority Research Delegation
DISCOVER
Research Questions & Goals
DISCOVER
Persona Prioritisation
DISCOVER
Design stage 2 OF 4
Define
We needed to refine the numerous research findings into the most relevant insights and a thorough problem definition to start ideating from.
Unstructured Research Findings
Define
Findings Themes
Define
Key Research Insights
Insight 1
Personalised savings must be made clear and tangible
Users are more likely to switch when they clearly see how much they personally will save, presented at the moment they’re comparing options, rather than generic or abstract savings claims.
Insight 2
Educational gaps undermine switching confidence
When users don’t understand switching steps or terminology early in the journey, uncertainty builds and even motivated users hesitate or drop off.
Insight 3
Too many card options lead to decision paralysis
Large lists of similar offers overwhelm users during comparison, making it harder to choose and increasing indecision and abandonment.
Insight 4
Confidence in eligibility drives switching action
Users are more willing to apply when they feel confident they’ll be approved; fear of rejection or credit impact causes them to avoid offers, even when eligible.
‘Researched’ user problem
ClearScore users with credit card debt who are open to switching often feel uncertain and overwhelmed when exploring options in the app.
Unclear savings, a confusing process, and low confidence in approval prevent them from switching, even when it could reduce costs and interest payments.
Design stage 3 OF 4
Develop
With key insights identified and the problem defined, I started developing solutions – from the user flow through to conceptual UI.
User Needs, Epics & Stories
develop
User flow options
develop
Chosen User Flow
develop
UI options
Capturing users existing credit card details
develop
UI options
Displaying users new credit card options
develop
Design stage 4 OF 4
Deliver
Now the chosen solution was brought to life by building, testing, and refining it to ensure it works for our users and the business.
Funnel analytics
First release
deliver
Data Led Design Iteration
deliver
Funnel Analytics
Second release
deliver
Screen Flow Diagram
Hover over the flow to zoom to the start
deliver
Key Screens
deliver
Component Design
Viewing credit cards by categories
deliver
Designing for Commercial Risk
deliver
Component Design
Viewing & comparing credit card details
deliver
Prototype
DISCOVER
Success analytics
deliver
Total number of user to apply through switch & save (in first 54 days)
1,471
Average calculated saving per apply click (in first 54 days)
£1,137
Estimated Total saving made for our users
(in first 54 days)
£1,672,527
If you like what you see — Don’t be a stranger! ...
o.hardisty@me.com