Digital Marketing and Relevance

For any brand, digital marketing is now mainstream. Ignoring digital is like ignoring reality and for a marketer, almost a professional suicide. When brick and mortar businesses like hotels and restaurants have to rely on digital marketing for business, no one can really remain untouched.  Any digital marketing initiative must examine relevance of message to the recipient, one it is possible and two, your business will cease to exist without it.
There are learnings we can derive from already established digital businesses. Take ecommerce companies for example, mailers drive traffic for major offers and seasonal discounts.
1. Capturing sufficient information to personalize the message - Relevance has multiple connotations, every person has unique set of requirements and aspirations. Without capturing this information relevance cannot be achieved.
2. Current and correct information - Capturing detailed information that is correct and updated is also an important challenge. Customers provide their location and interest in products and categories through their browsing or purchase behavior. It is obviously unrealistic to expect every declaration of interest to match with displayed behavior. Hence a strong need to constantly update the customer attributes.
3. Classification - Another tool for matching that is used very often is classification. For example, an ecommerce shopper expresses interest in bed sheets, now does that mean we can send offers on Garments or home furnishings. While classification helps find commonality between both parties, classification errors add to the complexity.
4. Error tolerant matching engine -
A vertical specific matching engine can build on the domain expertise present in the organization. To make it more robust for errors in data capture and classifications, matching engine may use behavioral information or the domain expertise.
Improving relevance finally becomes a function of improving all of the above. Do share your thoughts. Wish you success in your digital venture.

Story of a Product Revamp - Naukri Resdex Emails

If you are looking at apply for a Product Manager position at InfoEdge and will like to know the kind of work this team does, take a look at the list of changes we made over the last two years to an established product.

Resdex is the Naukri Resume Database product, which enables Recruiters to identify relevant jobseekers for a specific opportunity and contact them. Recruiters can either call up the jobseekers or send job opportunities as targeted mails. Resdex mails is a popular mechanism of contacting jobseekers and has undergone a major revamp in last couple of years.

1. Addressing Relevance for Jobseekers - Resdex allows recruiters to search jobseekers based on several criteria like key skills, designation, company names, salary, experience, location, education and other criteria as well.  However, Naukri was getting several complaints about irrelevant mails being sent to jobseekers.  Typical complaint being "I have done an MBA from premium college and I am getting call center jobs".  To address this challenge - Naukri introduced mandatory filters for Salary and Experience, however, the choice of the values was left to the recruiters discretion.  We saw a major fall in the volume of email sent (and loss of business) as recruiters started entering the expected salary and experience consciously.  This reduced the irrelevant emails sent to jobseekers who were never the targeted audience in any case.  And since jobseekers saw more relevant mails in their mailbox, the overall response to Resdex mails improved significantly.

2. Apply and Reply from the mail - Naukri has now enabled Apply and Reply from the mail.  Jobseeker can login to Naukri account and apply to the job sent, Or compose a response and attach a resume as part of reply.  The recruiter receives the jobseekers Naukri Profile snapshot along with the resume, the profile snapshot is a standardised template that enables a quick scan of applies. A large number of jobseekers are now checking their emails on the mobile phone and where they may not have access to resume, it gives them an easy way of applying through Naukri and using the uploaded resume to quickly respond to the recruiter message. 

3. Recruiters can view list of Contacted Candidates and Applies by the Job Sent - Contacted Candidates information is now displayed subject line wise (which in most cases represents the Job Title), recruiters can now see which candidate was contacted for which opening. The number of candidates contacted is indicated against each Resdex email subject and link against each subject line can be clicked to view applies received for that email in EApps. Since jobseekers now apply through Naukri, list of applicants is also available to recruiters.

4. Send a Job as Email - After posting a job on Naukri, you can search the most relevant candidates in Resdex and send them the job. Send a Job as Email thus combines the power of Job Posting and Resdex, and helps in closing the positions at the earliest.

5. Automatic Shortlising of Candidates - Searching and shortlisting of relevant candidates from Resdex may require significant amount of time and effort. Now, Naukri can also help in identifying the matching jobseekers. To begin with, this functionality is available if you have posted a job where annual salary mentioned is more than Rs 15 lakhs per annum. The candidates are selected using the Naukri iMatch technology which also powers Naukri Job Alerts. The matched candidates are available in a folder in Resdex which carries with the same name as the job title for convenient access. If you want, you can further shortlist candidates from this set or send the job as email with a single click.

Who made this revamp possible - of course, the product managers, along with the User Experience Design, the Technical Team and the Analytics team.  Kudos to Abhijeet Anand and Praveen Chandran, the two product managers who have led this revamp.

Naukri Referral Hiring Product

The new Naukri Referral Hiring Product enables organization to manage the Employee Referral program with considerable ease.  Here is why I think this is an amazing product -

1. Easy sharing of the referred job by employees on their social networks -  Employees can share the jobs on their social networks with a single click.

2. Tracking of applies - Applies can be tracked even if a friend of friend of the employee applies.  That is, if a job is shared by an employee, a friend likes the job and friend's friend applies to the job, employee will get credit of the apply.

3. Management of contact lists - You can manage the list of employees to send the referral mail to. For example, if it is the sales manager job, it can shared with employees in sales team, who are more likely to have sales managers as friends.

For more details, check out the Naukri blog - http://recruiterzone.naukri.com/?p=3173

Do give it a try and share your feedback.

Story of Naukri Job Alerts

Naukri.com is the market leader among with career sites in India, with market share currently at 63%. Naukri.com has 30 million+ registered profiles and a large part of these registered members receive a job alert every alternate day or on a weekly basis. Job alerts only contain freshly posted jobs on Naukri.com in last two/three days. It is probably the main reason why Naukri Job Alerts have one of the highest open and click through rates. Yet, jobseekers complained of relevance of jobs sent. That was identified as one of the important problems to address in early 2010.

The process of improving the job alerts was an incremental one. We built the logic step-by-step and with every incremental step, our understanding of the relevance problem improved.

1. Discovery of “Role” – I tend to believe one major variable than we discovered was “Role”. A deep dive in the behavioral data showed several interesting patterns. Jobseekers were not sticking to their Functional Areas (departments) and were applying across Functional Areas.

a. Pattern of apply clearly indicated that Role was more important than functional area.

b. We had roles which were very similar present in multiple Functional Areas, for example, sales role existed in Industry specific functional areas. GM Accounts existed in Accounts Functional Areas as well as the Top Management Functional Area. Also, several functional areas were close to each other, for example, Accounts and Banking.

2. Limitation of Keyword search – Key skills entered by jobseekers represents what they consider as important. Logically, a search on jobs should use the key skills entered by the jobseeker. However, some of the jobseekers had not entered their key skills. A large gap existed in the key skills entered and their skills as apparent from the CV. We needed a robust mechanism which did not fail because of the data inadequacy.

3. Handling of Categorical Variables – When we compare two jobs and their relevance to the jobseeker, attributes like “role” were important. The key challenge was to translate this into a distance function that can be used in predicting relevance for the jobseeker. Similarly, attributes like Industry, Location required identification of a good distance function.

4. Jobseeker Resume – A match between a jobseeker’s expertise and the requirements from the recruiter is essentially a match between the CV/resume and the Job Description. Of course, there are challenges – if a CV is old or a job description is incomplete, this may not work very well. Yet, we needed a mechanism for matching the candidate CV and the job description.

5. Apply Behavior - It is very much possible that apply behavior of a jobseeker will deviate from the CV/resume.  Apply behavior can provide insight into asiprations of the jobseekers as well as help identify classification errors. Incorporating apply behavior in identifying matching jobs for jobseekers is another significant challenge.

Naukri Analytics team identified the above challenges and incrementally solved them in association with the product team and the technology team. And of course, we noticed a major improvement in relevance feedback from jobseekers.

We are not done on solving this technical challenge. Analytics team is looking to hire smart Data Scientists to join its rank and work on solving these – if you are interested, please click to here to apply.