We have used multiple B2B platforms and we know how filters play a pivotal point for the platform. The user won’t be able to get the precise result if they are unable to fully utilise the filter of a platform. Well designed filters help B2B users to save a lot of time increasing their efficiency.
Most often when designers are assigned with a task of designing filters; they’re most likely to replicate mundane e-commerce filters in their solutions. Well, it’s not wrong. The solution works almost 70% of the times. But not all the times!
Not all products are made for ordinary masses!
This our story where we learned this lesson the hard way. Our story starts with a task in hand — ‘design filters for applications page on our partner platform.’ Partner platform represents the B2B side of Aasaanjobs’ products. Products where users are pretty much expert in what they do.
A Bit Overview of our Product
Our products majorly revolve around 3 major stakeholders:
Job Seekers who use our platform to find jobs.
Company HRs, Recruiters who hire the candidate from or platform.
Placement Agencies who bridge the gap between employers & candidates.
All of these stakeholders are highly inter-dependent. Each of them has different user goals, different mental models, requirements, and workflows. In this post, I’ll be talking about the Partner Platform.
Daily Workflow for a Partner:
- Source relevant candidates for different jobs & create their applications on the platform.
- Book the interview slot for their candidates once the slot becomes available by the employer.
- Lineup candidates for the interview. Follow up with the candidates before the interview.
- Check with the candidates for interview attendance.
- Track the candidates who are joining/not joining the company after the successful interview.
When we launched the first version of our Partner platform, the filters of the application section were not matching with their requirements. They were unable to do their daily routine work on the platform.
Issues with Older Versions of Application Filters:
- It was hard for Partners to find candidates going for interviews and hence, they failed to follow up with the candidates. Due to this, the percentage of interview attendance was very low.
- Partners were unable to find out how many of their candidates are joining the company post the interview. Which directly affected their payout.
- Partners were not able to book the interview for their candidates, because it was not easy to filter out the applicants based on the availability of interview slots.
- New partners were not able to understand the work cycle. So their work efficiency was very low.
- They were not able to find out the application based upon who has created the application.
The whole interface was not aligned with the partner workflow. It was more like we were forcing the product into the workflow — Instead of creating the product alongside the workflow.
Our platform had similar interaction pattern that of an e-commerce website. Which was definitely not going along with the actual user needs.
In e-commerce, users are nothing but explorers. Not really aware of the final outcome. But in Enterprise software, users are the ‘doers’. They know what tasks they want to accomplish and what will be the outcome.
So, how did we fix it?
Now our goal was to redesign filters to improve efficiency, better discovery and minimise the confusion. And like every design journey, we started off with extensive user research.
Survey, Interviews & lot of Data
We prepared a form to collect the partner’s experience, their pain points, their behaviour towards the recruitment industry. The whole intention behind the survey was to understand their work style.
In parallel, we also started taking interviews with our partners. We went to their offices to understand where they work, what kind of systems they use, internet speed, their staff, and proficiency of their staff. These things set the notion for us to understand and create a better flow for partners. These things help us a lot because now we can think from their point of view.
All this process was fully backed with the data. Checking the data helped us to eliminate the biases and also assisted us to validate the partner’s feedbacks.
Learning from the mistakes and & “revolutionising” the recruitment industry, we were back on the whiteboard with lots of insights into our user’s requirement, their work pattern, their mental modal.
From all the above efforts, we got to know that our partners work style is different from each other. So to address the problem of all kind of partners, we need to segregate them by their work style, requirement. We created the buckets and added the partners in each of the buckets based on their style.
Things We Learned
- Don’t hide the filters from the user’s view if they are important. Though hiding the filters makes your product very neat and clean but sometimes it hampers the usage.
- The success of B2B products depends upon when the users complete their journey from point A to Point B. Prime example of our platform will be if the Partner is not able to filter out that candidate application whose Interview slots are available. In that case, they won’t be able to schedule the interview which in the long run translate into the failure of the product.
- From the research result, we had our clear goal and it was to push the percentage of candidate attendance marking, candidate follow-up metrics higher.
Products like JIRA, Confluence help companies in their software development process. While products like tally are more fit for Banking segment. The mental modal and the requirements of Developers will never match with the Bankers. Each product has its own usage requirement, a different mental pattern.
- So the first thing we did is we categorised the journey of the users based on the real world context. We bucketed the flow of a job seeker journey from start to end. And also what would happen if the journey breaks into another path after certain steps. This helped partner to assess the application quickly where attention is needed. Now instead of remembering the filters, partners can do their task in a single click.
- To increase the percentage of candidate attendance marking, candidate follow-up metrics we provided the filters upfront so the partner can apply the filters based upon Follow up status, Attendance status.
- These layers of filters are business oriented and help the partner to increase the number of candidates turn-ups in the interview. And hence, an increment in their revenue.
- By showing all day to day usage filters upfront we made learning for our users real easy. Now even if we onboard a new partner, he/she would know that he/she can filter the applicants based upon the attendance, screening status, Interview dates.
User can’t see through your products. User can only tell you the thing based upon their previous experience. If they haven’t used a similar product as of yours they won’t be able to testify to your products. That’s why we tested our new product step by step with the real users.
By applying the above methods we certainly have pushed the bar. And now we are able to increase our filters’ performance by 2x. Since the design is a process of learning and iteration; we’re yet miles away from our destination.
We are on our voyage of making the hiring process as smooth as possible for entry-level job seekers in India. Your comments and suggestions are most welcome :)
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