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Introduction to the Empty Interface Problem

Recently, one of our developers raised a question in our Slack chat about how our website appears to new users. This was particularly relevant for pages that lacked content. One of our users commented on a PR that the absence of data gives an impression of incompleteness and reduces trust in the product. We understood that this was a serious issue and decided to find a solution as soon as possible.

Why This Matters

The empty interface problem was not only about the visual perception of the site. We knew that the first impression of a product could play a key role in how users perceive our service. This is especially important for pages with job listings and company offers. If users see empty pages, it may decrease their willingness to engage with our product and increase the likelihood of losing potential clients.

Details of the Problem

One striking example was the job listings page. When users navigated to the page, they only saw a header but found no open positions. This created confusion and a negative experience. We received several pieces of feedback from users indicating that the lack of information made the site unappealing and even raised doubts about its relevance.

Initial Attempts to Solve the Problem

In the early stages, we decided to manually fill the pages with dummy data. However, this approach turned out to be labor-intensive and inefficient. We quickly realized that manually creating compilations for each page was far from optimal. As a result, we began looking for a more automated solution that would allow us to generate data for all pages at once.

Technical Approach

Ultimately, we decided to develop a script for generating seed data. We used Python and the Faker library to create realistic data. The script generated job listings, companies, and candidate profiles based on predefined templates. Here’s an example of the code we used:

from faker import Faker
import random

fake = Faker()

def generate_job():
    return {
        'title': fake.job(),
        'company': fake.company(),
        'location': fake.city(),
        'description': fake.text(max_nb_chars=200)
    }

jobs = [generate_job() for _ in range(100)]

This code allowed us to create 100 job listings in just a few minutes. We integrated the script into the deployment process, ensuring fresh data was available with each update.

Changes to the Product

After implementing the generation of seed data, the website's interface transformed significantly. Users now saw real job listings and companies on the pages, which greatly increased engagement levels. The presence of content on the pages made them more attractive and informative for users. This, in turn, led to an increase in traffic to the job listings pages and heightened interest from job seekers at /jobs and companies at /companies.

What We Learned

• Automation - Creating a script for data generation significantly reduced the time required to populate content.
• Data Quality - Using libraries for data generation allows for more realistic and diverse entries.
• User Experience - The presence of content on pages directly impacts users' perception of the product.

What This Means for Job Seekers

For job seekers, this means that our website now features current job listings they can consider. This greatly simplifies the job search process and makes our resource a more valuable tool in their careers. Users can easily browse offers and find suitable options, enhancing their overall experience with the platform.

What This Means for Recruiters

Recruiters gain access to a larger pool of active candidates due to the content we filled the pages with. The presence of real job listings builds trust and attracts more applicants, which in turn helps recruiters find suitable candidates more quickly and effectively.

Next Steps

Despite successfully implementing the solution, we continue to monitor the quality of generated data and its impact on user behavior. We plan to improve the generation algorithms by adding more diversity and relevance to the data. If we could change anything, we would have spent more time testing different data formats to identify the best approaches for users. ---