How I Redesigned MoEngage's Menu and Cut Support Complaints by 50%

01 — The Problem: Death by Clutter

The Problem: Death by Clutter

MoEngage is a MarTech SaaS platform that lets marketers engage users with their product by sending Push, Email, WhatsApp, and SMS notifications.

Over time, the MoEngage menu had become a dumping ground.

Imagine logging into your marketing tool and spending minutes just hunting for the "Create Campaign" button. That was daily reality for our customers.

And when navigation breaks, support costs rise, complaints pile up, and churn creeps in.

MoEngage menu before redesign

02 — My Role

My Role

03 — The Process

The Process

Instead of jumping into Figma, we started with data.

• –Mapped IA → mind map of the current structure, spotted obvious flaws.
• –Studied metrics → e.g., 40% of users moved from Segmentation → Campaigns, but that flow was hidden.
• –Hypothesis → the true workflow is Analyze → Segment → Campaign.
• –Competitor benchmarking → how B2B SaaS handles IA.
• –Card sorting → marketers vs buyers grouped tasks differently.
• –Prototyped and tested → killed weak ideas, iterated.
• –Rolled out in beta → measured adoption and feedback.

04 — Understand Current Flow & Metrics

Understand Current Flow & Metrics

Created a mind map to see how the IA of the entire product works, and noted down the obvious visual problems.

I looked at the metrics and realized a few of our assumptions were wrong.

05 — Assumptions

Assumptions

• –All the users create campaigns and that is what they want to see mostly
• –They use settings frequently
• –Create campaign is not used from the side nav
• –People access all the settings in one place

06 — Summary of Data Analysis

Summary of Data Analysis

Design Takeaway

The existing information architecture reflected the product's internal structure rather than users' mental models. The redesign focused on reorganizing navigation around users' natural workflows, reducing navigation effort, improving discoverability, and making common tasks easier to complete.

How traffic flows: Sidebar Analytics → Segmentation → All Campaigns

07 — Hypothesis

Hypothesis

Our users analyze data, create a segment, then create a campaign from the All Campaigns page — looking at a previous campaign, since they already know what they want to create.

08 — Competitor Analysis

Competitor Analysis

I looked at how competitors structured their menus, and formed assumptions about why they'd made those choices.

You can find a detailed analysis here →

Competitor menu structures

09 — Top Problems

Top Problems

• –No visual hierarchy
• –No grouping of information
• –No defined principles for how one can bring in new features and promote them
• –Don't have an understanding of user pain points and how they navigate
• –Visually not appealing enough
• –Key feature discoverability is low

10 — Validating Hypothesis Using Card Sorting

Validating Hypothesis Using Card Sorting

I ran an open card sort, asking users to sort items and write their own category names — this showed whether the terminology I was using felt technical to them. I then analyzed that data.

Card sorting sessions with customers and internal stakeholders

I recorded these sessions and analyzed the data using Provenbyusers.com to uncover patterns in how people sorted the cards, then used those patterns to inform design decisions.

After analyzing it came out that there are two types of users.

• –Who buys the product
• –Who actually uses the product

People who buy the product look at high-level metrics only. Marketers, on the other hand, analyze the data, segment users, create campaigns, and track how each one performs.

Settings, dev docs, integration validation, and getting started are all one-time options that are rarely used — you can see this pattern reflected in the card sort above.

Similarity matrix

Dendrogram representing the grouping in card sorts

11 — Prototyping & Design Iterations

Prototyping & Design Iterations

I analysed a lot of data — notes, audio recordings, and card sorts.

I also ran card sorts with internal stakeholders. Using this data, I moved into prototyping and then visual design to gather feedback.

Theme exploration — mint, collapsed labels only

Theme exploration — mint, expanded sub-items

Theme exploration — mint, full navigation tree

Theme exploration — teal, icon-only collapsed rail

Theme exploration — dark grey, icon + label rail

Theme exploration — navy, collapsed rail

Theme exploration — purple, campaigns overview

Theme exploration — navy, campaigns overview

Theme exploration — navy, push campaign creation

Theme exploration — navy, full campaign list

Spacing spec — settings menu redline

12 — User Testing & Validation

User Testing & Validation

Before handing over the Information Architecture for development, we validated it through multiple rounds of internal reviews and usability testing with existing enterprise customers. The objective was to ensure the new navigation aligned with users' mental models, improved discoverability, and reduced navigation effort before launch.

The Information Architecture wasn't finalized after a single design pass. Multiple rounds of validation and iteration helped de-risk the solution before development and increased confidence prior to rollout.

13 — Final Implementation & UAT Feedback

Final Implementation & UAT Feedback

• –Users reported they could now find everything quickly.
• –The UI was visually cleaner and more intuitive.
• –A customer even shared positive feedback on LinkedIn.
• –Ran UAT internally and opened a beta to a few existing customers.
• –The feedback was positive: 90% of users found what they wanted quickly.

14 — Metrics That Improved After Redesign

Metrics That Improved After Redesign

I monitored the following metrics pre- and post-redesign:

• –Navigation Efficiency: Metric: Average time taken to find key options. Impact: Reduced navigation time by 40%.
• –Feature Discoverability: Metric: Number of users accessing specific features. Impact: Increased engagement with underutilized features.
• –Customer Satisfaction (CSAT): Metric: Survey responses post-launch. Impact: CSAT score increased by 15%.
• –Customer Support Complaints: Metric: Number of complaints about navigation issues. Impact: Complaints reduced by 50%.
• –Adoption Rate of New IA: Metric: Percentage of users interacting with the new menu without external help. Impact: 85% of users adapted to the new IA seamlessly.

15 — Challenges & Iterations

Challenges & Iterations

We went back to customers to understand their feedback on the redesign and inform further design decisions.

While the redesign received strong feedback overall, one gap surfaced: some customers couldn't find the Create Campaign action they were used to.

I solved this with a unified Create button that surfaces every creation flow directly from the menu. Below is the design for it.

16 — Next Steps

Next Steps

• –Continuously monitor performance metrics.
• –Conduct qualitative user interviews for deeper insights.
• –Iterate based on ongoing feedback for further enhancements.

17 — Final Thought

Final Thought

This redesign improved usability, discoverability, and user satisfaction. By making data-driven decisions, I transformed MoEngage's IA into an intuitive experience that empowers marketers.