In order to find the offer card, the user needs to go to our Cashback and Rewards section and scroll down the screen to the beginning of the offer feed.

Partners are businesses of different sizes that come to the bank to advertise their business or product in exchange for cashback, coupon discounts, or another benefit for users.

It’s important to note that we have both large and small partners, so we intentionally divided the offers into — popular and little-known brands.

The Benefit product is part of the lifestyle products offered by the financial services division.

The T-Bank ecosystem includes such projects as T-Business, T-Investments, T-Mobile (virtual mobile operator), T-Travel, as well as lifestyle-services.

T-Bank (ex Tinkoff) is an online ecosystem or super app.

Team

Role & Responsibilities

Tools

Role

Duration

Key Results

Summary

About T-Bank

How the users’ find the offer?

What is partner offer card?

Loyalty department

As the Staff Product Design with 4+ years building Fintech consumer products, I owned the end-to-end card redesign:

• design strategy, product strategy
• facilitating process, cross-functional collaboration
• discovery, research
• hypotheses validation, JTBD
• user testing, interview
• visual design, concepts
• design hand-off

Furthermore, I directed a 5-person team, mentoring on JTBD.

Figma
Wynde
Amplitude
Optimizely

1 data analytic
1 product designer
1 product manager
1 ux researcher
1 ux writter
4 product engineers

2024 (3 months)

Staff Product Designer

total customers in EU

revenue for 2024

55 000 000

$11 000 000 000

CTR to offer for little-known brands

CTR to offer card to popular brands

+3.1% pp

-1.6% pp

$15 000 000 000

unique user in Loyalty page

payed cashback to users in 2024

15 000 000

After a previous redesign of the main loyalty page, users could more easily discover offers — but data revealed a new challenge: the abundance of options led to choice overload, dropping CTR when opening cards and, ultimately, purchase conversion.

Average CTR per purchase

0.3–0.5%

Average CTR for opening an offer

2–3%

Y — purchase conversion. Choice overload reduced decision-making

Baseline performance before my redesign

The more offers a user viewed per month, the lower their purchase conversion rate became — a clear sign of choice paralysis.

CTR data

Having formed the main user scenarios, I defined the main user job in our product in order to understand the user's motivation.

Working closely with Data Analytics, I identified three primary user scenarios among active loyalty section users:

I conducted 12 interviews with Cashback and Rewards users to explore how they perceive offers and what the problems they came across when they are looking for offer.

I led discovery, synthesizing insights from 10 interviews and analytics, combining quantitative analysis with qualitative research to uncover user behaviors, pain points, and opportunities.

all three scenarios

scroll feed only

6%

54%

Challenges

Mission

Success criteria

Business problems

Finalize discovery

- make partner offers clearer and more emotional
- Tied to business OKRs for +20% acquisition potential.

- don't lower the current CTR
- no vision from the business
- it's not clear how to test

- partners are losing clients
- partners don’t see the value to order ads

- Increase CTR to open offers (target: +3% for niche partners)
- Improve end-to-end purchase CTR

go to the categories only

search only

10%

7%

How do our users interact with offer cards?
The goal is to understand the user path of the people in our section.

- which offers do users notice first?
- what is the user's flows to the offer card, depending on their tasks?

Job Story: When I see an offer, I want to immediately understand exactly what benefit I'm getting so I can quickly decide whether to take advantage of it

The main quantitative inside

On offer cards, users search for images containing items or products they associate with their desired purchase

Users problems

Users sorted the elements as follows:

To deepen our understanding of user priorities, I proposed and facilitated card-sorting to validate user priorities. This revealed exactly which offer elements users notice and value first.

This confirmed that high-quality product imagery is critical for future iterations.

Based on these insights, I synthesized key hypotheses with the product team to guide the new offer card concept.

Prioritizing emotional appeal through strong brand recognition and compelling product imagery emerged as the strongest driver for increasing engagement and CTR. Next: the redesigned offer card concept.

I explored over 10 hypotheses with the team and prioritized these four as the most promising:

From insights, I framed the core question and prioritized hypotheses with team. My guiding question became: “How might we make users feel a stronger desire to open and explore an offer?”

Hypotheses

Hypothesis 1

If we emphasize brand logo and visual identity prominently, users will recognize trusted brands faster — building confidence and curiosity to open the offer.

Hypothesis 2

If we highlight the primary benefit (cashback %, bonus, or discount) upfront, users will grasp the value instantly, reducing cognitive load.

Hypothesis 4

If we generate the need to buy on the offer through emotion and interest in the brand’s product, we will solve the user’s engagement problem and increase CTR to open the offer.

Hypothesis 3

If we minimize secondary details (dates, conditions) and focus attention on one main trigger, users will be more likely to engage and click through.

I explored how to inject more emotion and clarity while preserving the existing card format and size. This approach minimized development resources and reduced the risk of dropping current CTR, allowing for safer testing.

Internal testing and team review revealed this version lacked visual impact: the changes felt too subtle, failing to create sufficient emotional pull or clear differentiation for niche brands.

Why did we reject the solution?

I chose to retain the familiar card format and size to accelerate implementation, control development costs, and mitigate risk to baseline CTR.

Why this particular solution?

First iteration

Building on card-sorting insights that users prioritize product imagery first, I made the image significantly larger and consolidated all key information in one prominent block to create stronger emotional pull.

Team feedback and internal review showed that while the larger image helped, the overall card still felt too subtle and lacked sufficient visual hierarchy to stand out in a dense feed — especially for niche brands.

Why did we reject the solution?

Focused on the image

Re-examining interview notes revealed a key insight:

Previous iterations felt too incremental — mostly rearranging elements without enough impact.

The new insight

Users noted — if they don’t know the brand or product, the offer’s benefit feels less meaningful to them.

Reviewing the interviews

I reformulated the hypothesis and then formed a new vision of the offer.

We reframed the perception model: first evoke desire through emotion and relevance (e.g., craving the product), then show the clear benefit.

New pattern of perception

Before finalizing the design, I reflected on the process: thorough research and multiple iterations gave us confidence in the new direction.

Look back

Final Hypothesis:

If we spark emotional desire through compelling product imagery and relevance, then highlight the clear benefit, users will be more motivated to open the offer — boosting CTR.

1. Emotional Triggers

After exploring 20+ concepts, I landed on this balanced solution — maximizing emotional impact while meeting user and business needs.

Last 20 concepts

Solution

The final concept, grounded in research insights:

Why this solution solves user/business challenges?

Bold headlines and vibrant colors grab attention and spark interest — backed by user research on emotional appeal.

2. Clarity Through Focus

Dominant product image + minimal text let users instantly grasp the offer, reducing confusion.

3. Scalable for Impact

Modular format adapts to diverse ad creatives, empowering partners while driving business goals.

Interaction

In context: Final cards in the live feed

I validated three concepts using a semantic differential survey (Modern vs Outdated, Chaotic vs Understandable, Boring vs Emotional).

Results

Concept 3 won — rated highest as Understandable (39%) and Emotional (28%).

The modular card structure enabled scalability and partner customization.

Implementation

I partnered closely with engineers for pixel-perfect implementation and with PMs to align on goals — from design → guidelines → review → release.

CTR to offer for little-known brands

CTR to offer card to famous brands

+3.1% pp

-1.6% pp

A/B test on 5% of users (~750,000 people).

Results

Prioritizing revenue impact, we paused full rollout — using insights to evolve the concept for balanced performance across all brands.

The redesign lifted CTR by +3.1% pp for lesser-known brands — validating its potential to drive discovery and acquisition.

Key takeaways:
1. Always tie design to core user motivation.
2. Validate fast with clear metrics.
3. Build scalable solutions across teams and products.

Simple changes

Next iteration: Explicit benefit in header for famous brands — balancing emotional pull with clarity.