Redrob People Search
Redrob People Search
From Failing Tool to $10M Series A Success, transformed a slow, single-purpose recruitment platform into a high-performance lead generation product - increasing queries response time by 60% and smooth task flows.
From Failing Tool to $10M Series A Success, transformed a slow, single-purpose recruitment platform into a high-performance lead generation product - increasing queries response time by 60% and smooth task flows.
My direct contribution
Stakeholder management, User Flows, Product Strategy, Prototyping, Visual design, Micro-interactions, Dev Handoffs
Teams
3 designers, Cross-functional teams,
My Role
Lead UI/UX Designer
Timeline
1.5 month
Impact
Funding raised
$10 M by top korean VC's
User retention
30%
NDA
Task completion
30%
NDA
New user conversion
30%
NDA
Impact
Funding raised
$10 M by top korean VC's
User retention
30%
NDA
Task completion
30%
NDA
New user conversion
30%
NDA
Here's a quick summary, if you're in hurry!
Major Problem
Problem 1:
Current product was not aligned with business goal which preventing the platform from scaling into its full "Access-as-a-Service" potential dissatisfaction from investors.
Problem 2:
Users wants quick information and relevant peoples but due to system failures and response issue it leads to anxiety, and dissatisfaction for current platform.
Solution
A comprehensive platform overhaul that solved technical friction - delivering a 60% faster response time and 40% database optimisation - while redesigning the Information Architecture to transition Redrob from a niche recruitment tool to a dual-purpose B2B lead generation platform.
Here's a quick summary, if you're in hurry!
Major Problem
Problem 1:
Current product was not aligned with business goal which preventing the platform from scaling into its full "Access-as-a-Service" potential dissatisfaction from investors.
Problem 2:
Users wants quick information and relevant peoples but due to system failures and response issue it leads to anxiety, and dissatisfaction for current platform.
Solution
A comprehensive platform overhaul that solved technical friction - delivering a 60% faster response time and 40% database optimisation - while redesigning the Information Architecture to transition Redrob from a niche recruitment tool to a dual-purpose B2B lead generation platform.
My contribution
After setting up the basic user journey and w
While understanding the product vision, I am leading the process in India with the help of our sales & engineering team and been a part of design QA to help developers solve the design bugs faster .
My contribution
After setting up the basic user journey and w
While understanding the product vision, I am leading the process in India with the help of our sales & engineering team and been a part of design QA to help developers solve the design bugs faster .
Context
Redrob is an lead generation platform that helps businesses find and connect with professionals globally. In the competitive world of search tools, speed and data accuracy are everything. If a user cannot find a verified person in seconds, they move to a competitor.
Redrob was struggling to grow. The platform was built strictly for recruitment, which ignored a massive market of B2B sales teams looking for leads. On top of this, the search was so slow that most people left before the results even loaded.
As a result, the platform was stuck with only 8 active users and was losing potential revenue every day.
To save the product, we had to pivot Redrob from a simple "recruitment tool" into a high-performance "Access-as-a-Service" platform that worked for both hiring, sales and connect.
In summary
The Problem:
High search latency caused users to quit immediately also data in profile is hard to navigate.
The Market Need:
Recruiters need talent; Sales teams need verified leads.
The Gap:
A slow, recruitment-only tool that ignored the B2B sales market.
The Decision:
Pivot to a unified "Access-as-a-Service" platform.
The Challenge:
Increase search speed and optimize the database to build user trust.
Context
Redrob is an lead generation platform that helps businesses find and connect with professionals globally. In the competitive world of search tools, speed and data accuracy are everything. If a user cannot find a verified person in seconds, they move to a competitor.
Redrob was struggling to grow. The platform was built strictly for recruitment, which ignored a massive market of B2B sales teams looking for leads. On top of this, the search was so slow that most people left before the results even loaded.
As a result, the platform was stuck with only 8 active users and was losing potential revenue every day.
To save the product, we had to pivot Redrob from a simple "recruitment tool" into a high-performance "Access-as-a-Service" platform that worked for both hiring, sales and connect.
In summary
The Problem:
High search latency caused users to quit immediately also data in profile is hard to navigate.
The Market Need:
Recruiters need talent; Sales teams need verified leads.
The Gap:
A slow, recruitment-only tool that ignored the B2B sales market.
The Decision:
Pivot to a unified "Access-as-a-Service" platform.
The Challenge:
Increase search speed and optimize the database to build user trust.
Objective
Make Redrob, a people search platform rather than focusing for recruitment approach product to increase the user-base and align product with the main company vision. Optimise the people search user flow and enhance the speed of current database.
Current Search Flow
Objective
Make Redrob, a people search platform rather than focusing for recruitment approach product to increase the user-base and align product with the main company vision. Optimise the people search user flow and enhance the speed of current database.
Current Search Flow
How We approached the problem ?
First, I had a discussion with CEO and design team in Korea to understand about the new vision. Then I had a meeting with my product manager to understand the scope and the part needs to be work on after that we collected and analysed all available data (feedback sheet, and call recordings) from the past 30 days of current MVP. Utilized Notebook LLM to gain a more deeper understanding on those
Here are the key Insight we collected:
Platform breaks during the sales calls which leads not to trust and due to that the drop rate is almost 90%
Search took 3-4 minutes only to generate filters - most users abandoned before seeing results
Only 12% of introductory calls converted to paid users
73% bounce rate on search results for trial users
The interaction pattern of new and repeated users with the platform was very different
Lot of unused features make platform hard to focus on one path, which cause user retention drop-off to 40%
There are most of unused features Which has more weightage than primary user tasks
Search results displayed too much lengthy cards without enough data and no clear hierarchy which leads to too much scroll
Hypotheses
The time for generating filter and search is too high, also users are prefering manual filters to adjust their search rather than typing it as a normal text.
User wants to search people/lead fast.
Too less data is shown for a profile if user look for information.
No company data to found the decision maker or relevant people fast for a lead generation platform.
Not enough clarity on other feature like "Book a meeting" and "email campaigns".
Not enough clarity on updated contact information of a person.
How We approached the problem ?
First, I had a discussion with CEO and design team in Korea to understand about the new vision. Then I had a meeting with my product manager to understand the scope and the part needs to be work on after that we collected and analysed all available data (feedback sheet, and call recordings) from the past 30 days of current MVP. Utilized Notebook LLM to gain a more deeper understanding on those
Here are the key Insight we collected:
Platform breaks during the sales calls which leads not to trust and due to that the drop rate is almost 90%
Search took 3-4 minutes only to generate filters - most users abandoned before seeing results
Only 12% of introductory calls converted to paid users
73% bounce rate on search results for trial users
The interaction pattern of new and repeated users with the platform was very different
Lot of unused features make platform hard to focus on one path, which cause user retention drop-off to 40%
There are most of unused features Which has more weightage than primary user tasks
Search results displayed too much lengthy cards without enough data and no clear hierarchy which leads to too much scroll
Hypotheses
The time for generating filter and search is too high, also users are prefering manual filters to adjust their search rather than typing it as a normal text.
User wants to search people/lead fast.
Too less data is shown for a profile if user look for information.
No company data to found the decision maker or relevant people fast for a lead generation platform.
Not enough clarity on other feature like "Book a meeting" and "email campaigns".
Not enough clarity on updated contact information of a person.
User interviews
Now that we have enough data for pain point, what's next?
We reached out to some current users to comprehensively list problems and UX issues for validation with the data and to create a holistic view for redrob users.
I won't delve deeply into the research but will provide insights into what we discovered during interviews and task performance. Based on the tasks, we had specific questions for users.
Insights
Users prefer setting manual filters upfront instead of relying entirely on NLP.
Finding specific user info is slow and requires too much scrolling, which creates unnecessary friction.
NLP search filters are often inaccurate and irrelevant.
Profile search is too slow, leading to long wait times for users.
Primary focus was getting a contact detail.
Finding contact details was the primary goal for most users.
List views are confusing for many and dataset did not provide much brief data about profile; most users would prefer seeing their data in a table format.
Not having a company's data or company search limit user to find the potential leads.
User interviews
Now that we have enough data for pain point, what's next?
We reached out to some current users to comprehensively list problems and UX issues for validation with the data and to create a holistic view for redrob users.
I won't delve deeply into the research but will provide insights into what we discovered during interviews and task performance. Based on the tasks, we had specific questions for users.
Insights
Users prefer setting manual filters upfront instead of relying entirely on NLP.
Finding specific user info is slow and requires too much scrolling, which creates unnecessary friction.
NLP search filters are often inaccurate and irrelevant.
Profile search is too slow, leading to long wait times for users.
Primary focus was getting a contact detail.
Finding contact details was the primary goal for most users.
List views are confusing for many and dataset did not provide much brief data about profile; most users would prefer seeing their data in a table format.
Not having a company's data or company search limit user to find the potential leads.
Solution and design decisons I made
We tried to move manual filter upfront itself while making NLP search a secondary option to access. We provided clear communication about how they can get started and the result update real time based on the filter the choose manually.
Introducing
New People Search Flow
Providing an entry point for their actions also reduces what needs to be shown to the user and what doesn't. Previously, users go back and forth until all filter applied in NLP which cause friction and consumed to much time to adjust what to search.
Expanded Profile Data
Previously, there were no full data for a profile. So after getting a call with dev team and by understanding of API's data we got to know that giving the full profile information is possible.
Clear Signifier for Updated Contacts
By just adding a star into the verified contact help assess the grade A contact to user to which the credit is deducted.
Implementation of Company Search
We discovered that new users have many queries related to company search as keeping this lead generation vision, mainly to get the details of decision makers in comapany or pitch out their services to them. We attempted to address all of these implement a dedicated area to find potiential leads in a company.
Company Information and List of Potential Decision Makers
Overview of comapny and employees
Solution and design decisons I made
We tried to move manual filter upfront itself while making NLP search a secondary option to access. We provided clear communication about how they can get started and the result update real time based on the filter the choose manually.
Introducing
New People Search Flow
Providing an entry point for their actions also reduces what needs to be shown to the user and what doesn't. Previously, users go back and forth until all filter applied in NLP which cause friction and consumed to much time to adjust what to search.
Expanded Profile Data
Previously, there were no full data for a profile. So after getting a call with dev team and by understanding of API's data we got to know that giving the full profile information is possible.
Clear Signifier for Updated Contacts
By just adding a star into the verified contact help assess the grade A contact to user to which the credit is deducted.
Implementation of Company Search
We discovered that new users have many queries related to company search as keeping this lead generation vision, mainly to get the details of decision makers in comapany or pitch out their services to them. We attempted to address all of these implement a dedicated area to find potiential leads in a company.
Company Information and List of Potential Decision Makers
Overview of comapny and employees
Curios about this..
Connect with me @ prakashlibagunbk@gmail.com, for more discussion or
Curios about this..
Connect with me @ prakashlibagunbk@gmail.com, for more discussion or