Conversion Rate Optimization for Hired.com

Hired.com is a two sided job-search marketplace that reverses the hiring process and lets companies express interest in a candidate and request for an interview and aids them through the interview process. The company provides talent advocates who help a candidate unblock parts of their hiring journey, and offer them suggestions to find the right job. One of their unique acquisition strategies is to offer candidates a hiring bonus for the recruiting activities that have happened on the site.

What if Hired.com is aiming to increase the number of leads/applicants that complete the signup flow and get ready to be hired? How can a Product Manager go about solving this?

This is a classic conversion rate optimization case where the objective is to optimize the website’s landing page/signup to increase conversions. While increasing the number of people landing on the home page could solve for the problem, it would be an indirect solution. The best way to solve for this is to increase the users flowing through the signup funnel and a popular methodology used for this is A/B testing.

Hypothesis Generation:

An A/B testing hypothesis can be stated like this:

“Adding a resume upload field will help job candidates navigate through the profile completion phase faster and therefore, reduce the churn rates through the funnel.”

It would be prudent to look at the current state of the website and a few pointers that might indicate why visitors behave in a certain way.

1. The preferred browsing location for Hired.com’s visitors is their workplace. The average amount of time spent on the website is close to 3 minutes.
2. There are multiple fields on 4 different pages and most of them are mandatory for a user to signup. The signup process takes 7-9 minutes after multiple back and forths to input information.

One could infer from this information that a lot of the visitors might not have time to complete the signup process because they might be browsing the site from their workplace. A lot of pointers would go into constructing hypotheses and I chose to list hypotheses without going into the pointers (a few of them are from research, experience, and intuition).

1. Adding a resume upload field will help job candidates navigate through the profile completion phase faster and therefore, reduce the churn rates through the funnel.
2. Adding a visual cue pointing towards the signup/ login tabs will help direct users towards a Call to Action item, thereby reducing the bounce rates.
3. Modifying the signup process by providing a progress+milestone bar will help users understand what’s in store, thereby reducing funnel churn
4. Adding icons to fields and highlighting filled in fields will give users a sense of accomplishment, thereby reducing the signup churn.
5. Adding a chat now application that will directly connect you with a talent advisor will help users trust the website more, thereby increasing the number of Calls To Action.
6. Adding social signup options to the ‘SignUp’ tab on the mobile and web platform will help users quickly sign-up, thereby increasing the number of Calls To Action.
7. Adding a free downloadable content option to the homepage will incentivise users to leave their email ids, thereby increasing the leads generated from this funnel.
8. Modifying the color of the “Signup” and “Get Started” boxes to red will reinforce users to take a call for action, thereby increasing the CT rates.
9. Modifying the size of social signup icons and adding them to the signup action will nudge users to quickly start building their profile, thereby increasing CT rates.

I chose to pick the first one because a majority of their users access the website from their workplace and they should be incentivised to complete the process as soon as possible and get back to their work. Instead of prompting potential users to upload the resume after 3/4ths of the process, Hired can ask for it much earlier.

In my opinion, the complexity of making this modification is not significant and it could benefit Hired.com by at least holding on to the resume of the candidate even if they choose to drop out of the signup process. This would provide valuable information to Hired who can then send personalised emails, tips, and even job recommendations to candidates based on the data captured from their resume.

Specifications and Wireframes:

Current layout

Redesigned layout

I redesigned the layout using sketch and the additions/ modifications have been highlighted in a Red box.

1. Addition: An input field to allow users to upload their resumes.

On the backend, a document parser can extract information regarding the ones listed below and populate them in fields during the profile completion process. Native skill taxonomy can extract information about their skills, tools used etc.

1. Skills
2. Languages
3. LinkedIn/ Github profile links
4. Experience as a developer, PM etc.

How to measure this:

A few metrics that a product manager would carefully measure in this case are:

1. Conversion Percentage increase
2. Time saved during the signup process
3. Percentage increase in the number of partial signups

Conversion Percentage increase accounts for the % of users who complete the signup process in two scenarios: A and B. The scenario A deals with users who are shown the current signup process. Scenario B comes with the new signup process. Conversions in both these scenarios is calculated to understand if this change would have any positive impact.

Time saved during the signup process is pretty straightforward. As the phrase suggests, it calculates the time taken to complete the signup process in both the scenarios and its impact is ascertained.

Partial signups would account for the percentage of users who can be brought back to the site by sending them targeted emails on the basis of their interests, and career paths by parsing information from their resumes. A comparison of the percentages can be done in the before and after scenarios to observe the improvement in this metric.

Sample Size:

Optimizely.com has a calculator that can help someone from the product team calculate the sample size of users required to perform the A/B testing on. From a few online sources I’ve figured out that Hired.com’s baseline conversion rate is about 42%. To achieve a 20% increase (to 52%) one would have to consider a sample size of 300 users to measure the change with a statistical significance of 95%.

This is a typical process in which one would understand the conversion problem from the user behaviour, draw up hypotheses, define key metrics, design solutions, calculate the sample size to test the hypothesis on, and eventually implement the solution to optimize for better conversions.

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