Leading small team of designers and researchers in crafting a new search concept tailor-made for MyTelkomsel user needs & requirements.
February 2024 - early March 2024. Launched in June 2024.
This was a project for Telkomsel, one of our clients at Aleph-Labs Indonesia.
🫣 What users are looking for aren't showing up
Users often know exactly what they’re looking for, but when they enter their search queries, the results fail to reflect their intent. This disconnect occurs not because the content doesn't exist, but because it simply isn’t included in the current search indexes. As a result, valuable and relevant content remains hidden, leading to frustration and missed opportunities for engagement.
⛓️💥 Constrained discovery from limited queries
Even when the correct results exist within the indexes, the system often fails to identify certain user queries. This is largely due to its inability to handle variations such as typos, synonyms, or loosely structured phrases. As a result, users are required to input highly specific and direct queries to retrieve relevant content—an experience that feels rigid and unforgiving.
🙅🏻♂️ Asking the right questions, but receiving the wrong answers
Some search results were severely misaligned with queries, often surfacing content that had little to no relevance to what was being searched. In certain cases, the system even directed users to entirely unrelated categories, leading to confusion and a breakdown in trust. This inconsistency made it harder for users to accomplish their goals efficiently.
The intention.
User search intent is primarily focused on finding specific products or seeking information related to particular topics such as data plans, features, or FAQs. Rather than using search as a tool for broad exploration, users approach it with a clear goal in mind.
The method.
Although users primarily intend to find specific items or information, the majority queries they input tend to be vague. Instead of using exact keywords, users often rely on partial terms, general phrases, or incomplete descriptions—expecting the system to understand and interpret their intent. This strengthens the need for a more intelligent and forgiving search experience that can bridge the gap between unclear input and precise results.
The results.
When users turn to search, they do so with a clear and specific objective - whether it's finding a package, resolving an issue, or accessing key information - they want precise answers, fast. This behaviour emphasizes the need for a search experience that prioritizes relevance and clarity, enabling users to complete tasks efficiently without unnecessary distractions or detours.
Working our way up from possible scenarios.
We began by mapping out potential user search queries alongside the results they would reasonably expect to see, building a foundation rooted in real user intent. This process allowed us to uncover patterns, edge cases, and gaps in the existing system. By systematically exploring the full spectrum of possible search scenarios—from the most common to the rare—we gained the clarity needed to design a more comprehensive search experience.
Enriching coverage through expanding indexes.
I bridged collaboration between my team and the tech team to expand indexing capabilities of the existing search system, ensuring higher coverage and a more inclusive range of content that could be discovered by users. This effort aimed to bridge the gap between available content and search visibility, allowing more accurate, relevant results to surface. By enhancing what the system could recognize and retrieve, we laid the foundation for a more intuitive and helpful search experience.
Restructuring the results into a smarter, multi-content layout.
Taking inspiration from familiar search structures commonly found on users’ personal devices, we redesigned the search results page to support a more intuitive and multi-layered content layout. This new structure organizes results into distinct, meaningful sections tailored to user intent. By mirroring patterns users already understand, we improved content discoverability, reduced cognitive load, and ensured high functionality.
I implemented a structured approach to sorting search results by leveraging product taxonomy. As shown in the example above, I established specific ranking criteria and ordering logic to ensure relevance and clarity. For instance, highlighted attributes such as data eligibility and price were key decision-making factors for users, so we prioritized packages with longer validity first and surfaced the most affordable options based on final promo prices. Additionally, relevance was enhanced by showing packages similar to the user’s average monthly usage or recently purchased plans—helping users quickly find offers that match their needs.