Should I Market my College?

Colleges and universities compete with each other to attract the best students to join them.  Student is making a choice among several options available to her, whether a college decides to participate in this competition or not. And that choice is dependent on the information available to the student, about perceptions that other influencers have about the college brand, and about the guidance student receives from these influencers.

Every student aspires to join the best college, yet their choice depends on what is available to them or what is known to them. Decision making happens in two months period of constant see-saw between what is student’s aspiration and what is available.

What is Available
Not every student can join PGDM program of IIM Ahmedabad. Not every course or every college is available to students, it depends on their academic track record, exam results and performance in the selection process. CAT (Common Admission Test) results are announced well before final admissions, however, that is followed by Group Discussions and Interviews which continue till June-July. Final admission (admits) information is available only in June and July or later.

JEE Main results (Joint Entrance Examination - for B.Tech courses) are announced in April and May. That's the peak of admission season. Until the time of results announcement, student and parents know little about what is available to them. In fact, even this is hard to know because each state has different counselling for different state level colleges (for example, Joint Admission Counselling JAC in Delhi), besides the national level counselling JOSAA.  And these counselling sessions are spread over 6 to 8 weeks with 6-7 rounds and with each round what is available and what is not available is changing.

A student can refer to previous years cut off data, ranking, college reviews, refer to friends and seek help from alumni etc. to make a decision regarding course and college. However, this process is challenging because of a very short period time window available for decision making.

Few Elements of Differentiation
The biggest reason for confusion among students is that there is little differentiation among colleges. Over the last few years, I have personally seen students struggling to chose one NIT over another NIT or a new age IIT or a private engineering college.   The challenge for new age colleges is to define their charter in unique manner and constantly innovate to differentiate themselves.

Aspirations and Affordability
Education is seen as path to better future. And investment in education continues to be very high. India is a growing economy with increase in payment capacity of students. At the same time, parental support continues to remain high when it comes to funding education. With improving affordability, students are also opting for more private colleges. In aggregate, student and alumni reviews across colleges suggest that private colleges tend to have better quality of infrastructure and among the top private colleges, there is better engagement with industry and also more opportunities for industrial training.

Awareness before Positive Brand Association

It is important for any college to ensure that student at least is aware about the college. That's the first challenge- making it to the consideration set. And awareness that is associated with positive attributes which helps the student make the right decision. If a college attracts good students (and maintains good faculty and course curriculum), industrial placements are likely to be better. With improving reputation, it starts attracting a higher calibre of students and faculty, and then it becomes a virtuous cycle. 

InfoEdge Merit Awards - Congratulations Naukri Data Science Team

Congratulations to the Naukri Data Science team. InfoEdge Merit Awards for "Improvements in Naukri Search and Matching Engines over the year".

"Search and matching engines are core to Naukri experience for both jobseekers and recruiters. Over the last few years, we have seen a phenomenal improvement through semantic search and better quality recommendations.  Jobseeker experience is now personalized with new contextual autocomplete and suggestors improving the quality of search criteria entered. Semantic search and personalization deliver better discovery and more relevance jobs. Jobseeker feedback about relevance of job alerts has improved from 60% in 2014 to 76% in 2016 and is at present close to 80%. In addition, new job recommendation algorithms – Jobs Applied by Similar Profiles (JASP) and Online Recommendations have been a phenomenal success and brought additional incremental traffic to Naukri. Recommendations are now responsible for 80% of all applies on Naukri and are key driver of user growth in Naukri.  On the recruiter side, semantic search has improved the experience of recruiters and improved the discovery of relevant CVs. Apply Relevance Score has received very positive reviews from recruiters. Today, we don’t send 20% of the applies received as an individual mail. Delete Job Survey from recruiters  has shown a 20% improvement largely because of Apply Relevance Score."

Congratulations to all past and present members of the Naukri Data Science team.

- Vivek Jain

AI in Recruitment : Scoring Applies in terms of Relevance to a Job

Naukri Apply Relevance Score provides an indicator of how relevant an apply is to the job which a recruiter has posted on Naukri.com.  Recruiter receive this score as part of the subject line of the apply mail that is sent after jobseeker applies to the job on the site. Only highly relevant CVs are rated as 5star or *****, while 1 star * applies are the least relevant basis the parameters of the job and the jobseeker profile and resume.

Recruiters can chose to receive applies only above a particular rating and hence spend more time on higher rated applies. Based on the results we have gathered, a 5 star ***** rated apply is 2 times more likely to be viewed and shortlisted compared to a 2 star ** apply.

28th March 2018 Update - Major improvement done by the Naukri data science team, a 5 Star ***** rated apply is now 12 times more like to be viewed and shortlisted compared to an average apply. 

- Vivek Jain

Please also see my blog post on (1) AI in Recruitment - Understanding Skills and Designations, (2) Story of Naukri Job Alerts, (3) AI in Recruitment - Do Job Descriptions Represent the Intent of the Recruiter?, (4) AI in Recruitment - Is Mumbai closer to Delhi than Agra?, and (5) AI in Recruitment - Word2Vec Opens Up New Possibilities

Celebrating new milestone of Naukri Job search App on Android- rating of 4.5

Congratulations to the entire Naukri Team, Naukri Jobseeker Android App crossed the average rating of 4.5. 

Digital Marketing – Landing Page Optimization Must for Each Source

A large amount of marketing money is spent on brand building as well as performance marketing (generation of incoming traffic of customers). Marketing outreach through mass market, conference participations, email marketing or digital media brings curious as well as high intent traffic. Digital media enables very fine monitoring of campaigns and their performance optimization to ensure higher online conversions and revenue.

Customer Segment and Context of Each Source is Different

The intent of the customer depends on the source. Each source represents potentially a different customer segment, with its own differing context. Hence, multiple campaigns landing on the same page, even when the ad copy is same, can lead to different conversions. For example, one source may bring broad based audience with customers having higher propensity to pay, while another source may bring niche set of customers which are strategically important yet not willing to pay a lot.

Tracking Each Source is Important

Sources are often added or deleted in a running business. An experimental campaign without a change in landing page with a very short turnaround time helps in gauging the effectiveness of a new source. However, it is better to track a campaign separately with a tracking parameter rather than use pre and post-performance of the landing page.

Different Campaign Sources Have Different Conversions

Single Landing Page vs. Multiple Landing Pages

While a single landing page helps in managing the customer communications better. There is only one set of content to review and ensure nothing is wrong. Also, each landing page now needs two variants – desktop and mobile. Multiple landing pages helps in optimizing each page for specific campaign. However, if the landing pages don’t differ a lot, source specific customization of a single landing page may be better from content management perspective.

Comparing a New Source with an Existing Campaign

A landing page optimized for an existing campaign when deployed for a new campaign may not truly measure the effectiveness of the new source. Only when the landing page has been optimized for the new source, it makes sense to reach a judgment on the new source.  For example, niche set of customers may have a specific need and if the landing page does not reinforce their needs and wants, conversions may remain poor. And we may wrongly attribute to the payment capacity of the audience from the new source, while the culprit lies with the communication on the landing page.

AI in Recruitment: Word2Vec Opens up Interesting Possibilities

CVs and jobs are text heavy and like all the challenges which exist with natural language – multiple ways of describing the same concept, ambiguity, synonyms etc.  Meaningful interpretation of text requires extracting this knowledge in a machine understandable form. Among others similar problems exist in speech recognition, machine translation and conversational systems like Siri.

AI systems that process images work on high dimensional vector representation for each pixel embedded in a two-dimensional image. Most of the information needed to recognise images is present in the two-dimensional vectors.  However, most text processing system use a “bag of words” representation of text, that is each word is represented by a ID. For example, Infosys and TCS may be represented as say, ID75698 and ID 98603. And don’t use the contextual relationship between the two words, which otherwise recruiters or jobseekers can understand and process.

Latent Semantic Analysis is a technique which condenses the statistical count of co-occurring words into topics or concepts. It has been shown that Latent Semantic Analysis would recognise a shallow kind of topical similarity and not work well where subtle semantic relationship between words is present.

In contrast, predictive methods like Word2Vec learn the function that captures the salient statistical characteristics of the distribution of sequence of words. The function can associate each word with a continuous-valued vector representation that corresponds to a point in a feature space.

Word2Vec takes raw text as an input and the training of the Word2Vec model (skip-gram) is to arrive at vector representations of words that best predict a window of surrounding words. One can imagine that each dimension of that space corresponds to a semantic or grammatical characteristic of words.

The hope is that similar words get to be closer to each other in that space- that is we may expect Infosys Technologies and TCS as companies to be much closer to each in this feature space. That opens up new possibilities for AI in recruitment.

To Learn more on Word2Vec - Please see Getting Started with Word2Vec

- Vivek Jain

Please also see my blog post on (1) AI in Recruitment - Understanding Skills and Designations, (2) Story of Naukri Job Alerts, (3) AI in Recruitment - Do Job Descriptions Represent the Intent of the Recruiter?  and (4) AI in Recruitment - Is Mumbai closer to Delhi than Agra?

Naukri.com featured as an important case study in KrantiNation

Naukri.com has been featured as an important case study in KrantiNation for using Machine Learning. According to the book author Pranjal Sharma, Machine Learning is a key technology for the 4th Industrial Revolution.

For more details on the book, please see KrantiNation: India and the Fourth Industrial Revolution

AI in Recruitment : Is Mumbai closer to Delhi than Agra?

Jobseekers prefer to work closer home, their native town or their current location. They may also prefer specific locations because there are more job opportunities in that city. For example, Mumbai is a hub for financial services and Bangalore for IT jobs. That said, IT companies now have centers across all major metros and even in small cities like Indore, Jaipur, Trivandrum.

Jobseekers are willing to move from (say) Agra to Delhi, however, it is hard for an organization to convince anyone to move from Delhi to Agra. Charm of a large metropolis, with its educational, health, entertainment and modern lifestyle, is attracting talent towards larger cities. It has become a one way street.

As a recruiter (and hiring manager), when I look at a candidate, is he more like to move to Mumbai from Delhi? or will he prefer to move to a location near Delhi, say Agra? Often, geographical distance does not represent the user preferences. Unless there is some personal connect with a smaller town or incentives are offered with a promise for better location in the future, candidates are unwilling to move to smaller city or town. (Note - Agra is also developing very fast, preferences can change in the future).

Location is a simple "Yes" or "No", yet there are many variables which come into play in the Indian context. Some of the jobseekers want to live close to family and some away from it.  And preferences evolve as "the family" evolves and needs of the family change. A large number of jobseekers are willing to change location for the "better opportunity".

Location Preference Within a City
Yet, we see several employees depart because Gurgaon or Noida are too far from their current residence. Within a city, geographical distance or the daily commute is a major driver for employee satisfaction. An employee who was unhappy with his daily commute may eventually change the city itself (and not change his residence within the city).

AI Algorithm Must Understand the Preferences
The nuances of large and small city, distance within the city and also, personal preferences are all challenges for the AI algorithm to overcome.

- Vivek Jain

Please also see my blog post on (1) AI in Recruitment - Understanding Skills and Designations, (2) Story of Naukri Job Alerts, and (3) AI in Recruitment - Do Job Descriptions Represent the Intent of the Recruiter? 

Naukri RMS - Nominated for IDC AP Digital Transformation Awards 2017

Naukri RMS received the IDC India Digital Transformational Award last month.  Congratulations Naukri team and thanks IDC. Naukri RMS is the new age Recruitment Management System which automates the recruitment process end-to-end from Requisition to Offer.  With over 3000 customers is three years of its launch, Naukri RMS has become the leader in this space.

Naukri RMS (earlier known as Naukri CSM) has been nominated for the Regional Awards - IDC AP Digital Transformation Awards 2017.

For more details on IDC Digital Awards, please visit - IDC Digital Summit 2017

AI in Recruitment - Do Job Descriptions Represent the Intent of the Recruiter?

Job descriptions are essential part of recruitment. Once hiring manager creates a requisition and gets it approved, a recruiter will work with hiring manager to create a job description. A job description has dual purpose -

(1) it helps to attract jobseekers by pitching the unique attributes of the role for which recruiter is hiring, the reasons why a jobseeker will like to work in the advertised role, and

(2) it enables the recruiter to specify what kind of candidates she is looking for and also for jobseekers to know whether they are qualified for the requirement or not.

Job descriptions however may fail to deliver on the above two promise.

Recruiters may not have a job description to begin with, and they end up writing it with sketchy details on what a person is expected to do. Often the requirement evolves as the hiring manager and the recruiter meets jobseekers. Once the recruiting team knows what kind of skills are available and if no matching candidates for given set of requirements are found, hiring managers may modify their requirements.

Will recruiters update the job descriptions and re-advertise the positions with the new and updated job descriptions? Sometimes, yes and sometimes, no. If there are sufficient candidates available in the already received "applies", the recruiting team may decide to rely on the existing candidates and not re-advertise the updated requirements.

Now, if the job description is very well documented and the recruiter has already hired against the same position earlier, we can expect the job descriptions to represent the intent of the recruiter. That said, the AI algorithm is typically built on historic job descriptions and the response of the recruiters (in aggregate) to applies, hence, some of the "ambiguity" in the recruiter response is already embedded in the AI algorithm. This "ambiguity" may not always be helpful to the recruiter.

-Vivek Jain

Note- Even if job descriptions completely represent the intent of the recruiter, does the AI algorithm completely understand what is specified by the recruiter in the job description?

Please also see my blog post on (1) AI in Recruitment - Understanding Skills and Designations, and (2) Story of Naukri Job Alerts

AI in Recruitment - Understanding Designations and Skills

Relevance of jobs for candidates and candidates for recruiters is the most important challenge for AI in recruitment. Whether it is an Application Tracking System or a job portal, recruiters want easy mechanism to identify the most relevant candidate. That said, only a recruiter knows what she wants. The AI Algorithm only knows the job description which she shares with the system (there still exists a gap between what she wants and what the description says).

Over the last few years, this has been area of major focus and attention for our team at Naukri.com. I will discuss here on some elements which are important in solving this challenge.

Challenge 1: Complexity of Indian Economy - No one sector or Industry dominates

India is a large country with several 100 industries and sectors with companies of varying size. Every organization has many unique roles and designations that employees carry. Even within the organized sector, we have more than few 1000 roles and may be more than 50,000 designations. AI Algorithm needs to understand what each of the designations stand for.

Challenge 2: Creative Designations

Every organization is creative with designations and often internal designations are created to balance the organization challenges and individual aspirations. In many companies, Software Developers carry the designations like Software Engineer, SSE -1, SSE -2, Member of Technical Staff. However, few companies call their Quality Engineers as Software Engineers.

Often designations are created to represent evolving role descriptions based on the unique organization requirements. For example, few years ago, Mid-Office was created as a designation to distinguish teams from Front Office and Back Office. Similarly, we have seen new age professions emerge, for example, Digital Marketing, SEO Specialist, Social Media Marketing Manager, Data Scientist and so on.

For a system to understand the requirement, AI Algorithm must first understand the designations and the similar designations or related designations which other companies may have.

Challenge 3: Some Designations carry no information about role

Often designations are devoid of specific domains and also, role information. Some jobseekers write designations as Vice President, Manager, Senior Manager, Officer etc.

Challenge 4: Skills, Regions, Divisions are part of Designations

Skills are also part of designations which are often used to differentiate employees in the same role with specialized focus skills or areas of responsibility. For example, Software Developer, C++ Developer, Java Developer, Senior Engineer- COBOL and so on. In Sales function, we may have designations like Sales Regional Manager, Territory Manager - Bhopal, Area Sales Manager- Mangalore, Regional Manager - Paints and Specialty Chemicals etc. As we can observe, Cities and business units have been appended to these designations to differentiate sales managers playing similar role with special focus areas.

The challenge to disambiguate designations is not trivial as new designations are created on an ongoing basis. Skills are often used by jobseekers to distinguish themselves vis-a-vis other jobseekers.

AI algorithm needs a library of Designations & Skills and their inter-relationships. Have we solved the matching problem with regards to designations and skill sets? May be to a large extent. Yet there is scope of improvement and our effort continues. There are many other elements which play an important role in identifying relevant candidates, which I intend to talk about in later articles.

- Vivek Jain

Note - The challenge of overstated or understated skills is a conundrum which can only be solved by assessments. In my view, most jobseekers still faithfully represent what they know and what they don't know. And those who don't, are typically eliminated through the assessment process. Often an expert recruiter will look at signals beyond the stated skills, for example, the educational institution from which the jobseeker graduated or the company the jobseeker is working in.

Also see my blog post on Story of Naukri Job Alerts.

My Keynote Presentation at Data Science Conclave 2017 in Chennai

I am sharing my my keynote presentation at Data Science Conclave 2017 in Chennai. Thanks Rajesh for the invite.

1. Major improvements in accuracy in speech recognition and image recognition opens up a new field in human computer interaction. With computers able to correctly interpret almost all interactions without direct contact with keyboard or mouse, a major data source has opened up for Data Scientists to explore.
2. A system which is 80% accurate may not usable, however, when accuracy crosses 95%, there is a major turnaround in large scale adoption.
3. Self driving cars will lead to major leaps in technologies for object recognition -> not just previously known objects, also to anticipate and correctly handle unexpected objects.
4. In my view, there are four key dimensions of Data science, these are Data, Domain Expertise, Machine learning algorithms and Technology of Deployment. Value creation is possible across all the dimensions of Data Science. Better quality data, higher volume of relevant and contextual data can create value, and domain expertise remains critical in making successful deployments of data science projects. Our focus on machine learning algorithms is important, however, value creation happens across all the four dimensions.
5. We have seen a 5X increase in jobs which require machine learning and neural networks expertise.

Data Science is now mainstream and it is important for every organization to invest in Data Science and benefit from it.

https://www.slideshare.net/vjain99/data-science-conclave-keynote-presentation

Customer Insight - Survey, Data and Interviews

Customer insight is the only sustainable basis for building a business. And in my humble view, there are three ways of gathering customer insight, 1. Conduct a survey, 2. Analyze behavioural data, 3. Conduct in depth customer interviews.

There is a lot of behavioral data on the internet.  From browsing history to clicks and transactions. However, behavioural data only says "what" is the customer doing and when. "Why" is missing !!

Customer interviews can give insight about all that a customer cares about. The only challenge is that it covers only one customer at a time. Often, you may not have a large pool of customers to do indepth interviews, a lot of time may elapse between successive interviews or different customer context may make it hard to correlate insights across them. That's where surveys come into play.

Surveys enable quantitative measure of customer's opinion. If you have access to a large pool of customers, surveys are low cost and quick. Without surveys extrapolation of 1/few customer's opinion as the fact, is fraught with risks.

And without depth interviews, survey insight is shallow.

And without correlation of behavioural data, surveys and depth interviews are just opinions, which may not show up in reality.

If you are wondering on how to move from "I feel" to "I Know", use the three methods of gathering customer insight. You will know when you can say "I Know".

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.

Information and Communication Technology (ICT) Revolution has Touched Everyone

Information and Communication Technology (ICT) Revolution has delivered on its promise.  While our expectations on what it can do are still very high, I believe we have achieved a lot already. Communication revolution has given IT a platform to make life easier for almost every Indian who can be reached via road, rail, on foot or by the mobile network.  I share some of the use cases and associated stories with you, as in my opinion, they provide a vivid picture of how IT and Communication has changed our lives for the better.

1.       Travel and Ticketing - Most visible benefit of computerization that common man saw in late eighties was “Computerized Railway Ticket”.  With charts displayed prior to boarding the train and real time status of railway reservation, in one stroke, it took away the opportunity of rent seeking behavior. Millions of Indians, who travel daily on Railways, not only saw the benefits, but also became the champions of IT in India. That, to my mind, is the single most driver of IT adoption in India. 

2.       Travel and Ticketing - Internet ticket by Railways is also nothing short of a revolution. A local Internet Café accepts payment in cash and delivers me a ticket without the need to approach the local railway station. Savings in cost of travel to the station and back, no time taken to travel and easy access to railway information, all benefits have been delivered with the convergence of IT and communication.

3.       Real time Communication - 20 years ago, an Aunt of mine was coming to visit us. She lost her way and we learned that she had left her residence a long time ago and by all means, should have reached us by that time. Yet, there was no sign of her.  We imagined that she had probably lost her way.  We went in several directions to look for her. An hour long search and we could locate her, safe yet tired.  Just imagine the scenario today, she carries a mobile phone. We can reach her anytime and she will probably never get lost.

4.       Location Services - A similar incident like the one before. A few years ago, while traveling by train, I woke up with a start. My train was expected to reach by destination at 4am in the morning. It was already 3.40 am and I did not know how much time we had. I switched on my Blackberry and checked my location on Google Maps. We were just 2 Km away from the destination.  The train was running ahead of time. I knew we had to rush and get ready before the station arrived. Google map and easy access to mobile network on a train route helped me find the exact location and prepare me.

5.       New Customers for Small Businesses - Until a few years, for any problem with electric fittings or plumbing requirement, I will visit an Electrical or Sanitary/Hardware shop. I will wait for an electrician or the plumber and leave a message for him to visit us.  A number of follow up visits later he will arrive and do the necessary job. Today, every electrician, plumber or even a domestic maid, carries a mobile phone. They are reachable immediately and confirm their availability.  Everyone has benefited in the process. Of course, except the Electrical or Sanitary/Hardware shop owner, who has lost the cut he used to get.

6.       Mobile Payments - A construction worker was due to receive his wages from the contractor. However, he asked the contractor to keep the wages in his safe custody as he was afraid of losing the money since he had no regular place to stay and risk of thefts. A month later, when he wanted to go back to his native place, contractor made him run around for getting the accumulated wages. With no place for safe keeping, he was at the contractor’s mercy. That has suddenly changed with Money Transfer on the mobile phone. He can now transfer money back home without waiting for his next visit.

I am sure all of us have experienced something similar in our lives. ICT has touched everyone. With new innovations, our expectations are also soaring high.

·         Touch and speech are two new interaction elements which are making IT more accessible to every individual.  Touch phone is more intuitive and so, is speech. Siri is a great beginning in this direction. Speech recognition as a technology holds great promise and with a commercial value attached to quality, I am confident it will soon improve to serve the masses.

·         Another promising initiative is Aadhar. That shall help the common man in opening Bank accounts by providing an identity proof required to access services.  Will banking correspondents make up for absence of banking branches? That remains to be seen.

·         Will remote healthcare delivery graduate from being pilots/experiments and scale up to District or State level delivery models? Healthcare workers cannot replace the expertise of a doctor, yet they can at least provide timely help for the patients.

ICT is a force multiplier, yet it is not sufficient to improve the living standards of the common man.  Without roads, it is hard to make services accessible in the hinterlands. Without drinking water and access to healthcare, progress will remain limited. Physical infrastructure is a must for progress.

iPhone5 does not Wow

iPhone5 is thinner, bigger, faster and has more battery life. There are more apps, Facebook is more deeply integrated and Siri has been enhanced further.  The wave of innovation that started with new magical touch interface and apps that led to phenomenal usage on iPhone, is now abating.  Siri which promised another round of innovation with its voice interface and deep integration with existing apps on the device, has not yet made the same impact as Touch interface did. iPhone5 is almost guaranteed to be the best selling iPhone ever and most selling smartphone. The question is “is that enough”. As a customer, it does not matter - I still get the best product albiet at a high price.  As an investor, I care about long term growth and profitability, while share price moves to near term sales potential.

Apple needs to keep the innovation engine rolling, for margins to stay at current levels. The big challenge facing Apple is the open source, Android and copy cat apps on Android. To protect and preserve long term growth and margins, we may see more instances of legal suits between Apple and Android vendors. That said; it is very hard to control the onslaught of free and open source.  

·         Governments and large companies don’t prefer getting locked in to a proprietary and closed system like iOS. We saw a brief upsurge in Linux as Governments and Government owned institutions backed Linux.

·         However, unlike Linux, Android has the support of application developers and device manufacturers.  

·         Android is already dominating traffic across the emerging countries.  World is more web centric than it was a decade ago.

Can Apple innovate fast enough to maintain a strong lead over competition? iPhone5 disappoints on this metric. If Apple fails again, price competition will soon destroy Apple’s margins or worse, Apple may lose market share.

Is it Hard to Customize Mailer Content?

A bank sent a promotional mailer on “Get a term cover of Rs. 1 cr for Rs 8600/- PA”. It was a well crafted mailer with photograph of a young, dynamic gentleman holding a Play Card with “My Family” written on it and a smile with an exclamation mark.  All designed to catch attention and appeal to the feelings for family members – the right emotion to drive purchase of an insurance policy. 

Only catch was that the insurance premium of Rs. 8600/- PA was applicable to a 25 year, non smoker male with 30 year premium paying term.  And the mail was sent to a 35yrs+ old, female.  The recipient was an active customer of the bank and bank had all the demographic details in their records. 

It could have easily customized the mailer content, by replacing the Gentleman holding the play card with a lady, providing the real premium applicable to the recipient, instead of doing a mass broadcast of the mail.  For an interested customer, it could have been a single click purchase, landing on the login page of the bank, to make the payment of the insurance policy and so on.  The task required was to create two creatives and use dynamic variables from a look up table that can populate the premium values basis the age and the gender with all the caveats.  I bet this would have led to more conversions. 

Do share your thoughts on this topic.