Digital Transformation in Recruitment

A few years ago, the impact of digitization was only established in top industries like Banking, Insurance, and Retail. Now times have changed – the recruitment industry is also adopting digital transformation.

Everyone is a candidate at some point in their journey. Whether you are an employer or a candidate searching for a job, the digital transformation is helping the recruitment industry to implement smarter hiring strategies.

Recruiters are the brand ambassadors of a company as they are the first people with whom a candidate interacts. But getting the right people with the right skills, at the right price, has been a long-running headache for recruitment teams.

It’s a competitive marketplace for talent, with demand for skilled labor far exceeding the supply of qualified candidates actively looking.  This makes it more important need for recruiters, to master new digital techniques to find, reach and engage right skilled potential candidates.

Candidates seeking jobs in the market are also now tech-savvy and expect fast and easy application processes and communication. Their behaviors and preferences are changing. Power has transferred from recruiter to candidate.

There are multiple ways digital can help:

Assess the digital footprint of the candidate: Recruiters can use this data to get important insights into the skills of potential candidates. Correlations between social media profiles can reveal important aspects such as interests and hobbies, as well as an overview of the candidate’s personality. How suitable is the candidate to an organization’s culture can be assessed based on her/his social media sharing habits on subjects like gender, age, race, and politics.

Online language/skill assessment:  It is one of the easiest ways for companies to filter through the pool of candidates efficiently. Recruiters can ensure the quality of their hires via psychometric and other tests.

Online job portals:  Monster, Glassdoor, Indeed, Naukri and TimesJobs have helped in reaching out to candidates across different geographies and industries. They have also helped in building good candidate pipelines for recruiters. LinkedIn has started this revolution long back and has the credibility of companies as well as candidates equally.

Advantages of Digital Transformation in Recruitment:

·      Your website messaging can be targeted to different candidate personas

·      Candidates can easily engage with your company on social and mobile

·      Helps create the company brand in the digital world

·      Machine learning is helping is processing piles of applicants to find the right candidate

·      The organization can nurture candidates over time by relevant job alerts, talent campaigns

·      Social media presence generate the better response from talents to the brand

·      Digitization helps in ease of entire recruitment process and in turn happy candidates

·      Job seekers get an inside view of a company through a site like Glassdoor, which includes information on compensation, organizational culture, career progression, learning opportunities, etc.

·      Through the use of Big Data, learning companies can find suitable candidates, cut recruitment time and costs

·      Consolidated database of CVs becomes a powerful mining tool and cost saver

Digital is helping to convert the chaos of recruiting into “Smart Recruiting”.

What Chatbots are doing in the Digital Age

Our lives have changed for good due to the digital tsunami – it started with internet in 1995, then in 2004 social media stormed the world with Facebook, then came iPhone in 2007 and the whole world changed. Every mobile company started making smartphones for easy user interaction.

Today’s game changer is Chatbot !!

A bot is nothing more than a computer program that automates certain tasks, typically by chatting with a user through a conversational interface with help of natural language processing (NLP).

You don’t have to scroll through thousands of products on the website, just order to a bot and it will take care of delivering it.

There are 2 types of bots making inroads - Informational bots & Response bots.

Information bots are like subscribing to a breaking news or weather alerts based on your reading habits while response bots are responding to a user requested problems.

These bots should know everything about the customer they are talking to resolve the issues.

Many companies are considering voice interface to ease out the interaction and save the efforts of typing texts. Users converse with chatbots just like they do with family and friends.

Instead of asking your customers to visit the website & ask to find what they want or call the customer service, it is much quicker and easier to get direct answers via chatbot.

With digital assistants like Apple Siri, Google Home, Amazon Alexa voice has become an integral part of the interaction.

Chatbots, powered by artificial intelligence and machine learning, can learn our habits, understands our tastes and preferences and can be much more pleasant than a sequence of scroll, taps, and clicks to get to where the customer wants to go to complete an action.

Imagine how easy it is by chatbot to order pizza, book a ticket, complain about a defect of the product you just purchased than calling a customer service, waiting for IVR choices and spend more time just listening to music or customer rep.

Some of the technological advances in chatbots are:

·      They can be multilingual very easily than humans

·       Can be available 24/7 without any extra money to be paid

·      Anyone can interact regardless of device or channel or browser

·      Faster adaption by users

·      Ability to access data instantly than humans

The travel industry has plenty of areas where chatbots can help to take the burden off the staff, and also enhance the experience for customers. Everything from booking hotel rooms and flights to renting a car or providing assistance during a trip could be successfully handled by a chatbot.

Chatbots in healthcare are helping patients with common treatments by asking simple question and answers about symptoms.

Customer service is the most common area where chatbots are used today.

As NLP is constantly undergoing breakthroughs, chatbots will be able to interact with humans more seamlessly.

Artificial Intelligence in Financial Lending

I remember the 90s when I wanted to get a home loan and it took me 3 months to complete the process from providing all the hard copies of my income, tax returns, identity proofs then bank checked my creditworthiness & provided the approval.

Today everybody has some kind of loans like home loan, auto loan, education loan, two wheeler loan or even loan to buy appliances like HD TV and Refrigerator.

How do they assess your creditworthiness? There are so many cases of defaulters, which keeps increasing and hence established banks or lenders constantly looking for ways to improve the returns or proactively identify risks.

Lenders traditionally make decisions based on a loan applicant’s credit score, a three-digit number obtained from credit bureaus such as the TransUnion, Experian, and Equifax.  But these credit scores are based solely on credit-history and do not take into account rich data available, which can potentially give lenders access to data points as varied as online purchases, the strength of social connections and travel patterns. When viewed this data holistically, lenders can get a complete picture of potential borrowers & can significantly improve their ability to predict loan defaults.

Today digital transformation has changed everything. While the interest rate and closing costs on loans are still primary considerations, the speed, simplicity, transparency and customer service of the entire process is important.

As the purchasing power among millennials & gen Z continues to increase, they tend to purchase property and acquire assets that will provide stability & generate wealth.

The ability to cross-sell to these customers on loan products drives a significant portion of new loans. The difference for a digital-first customer is that they do their shopping online and may select an alternative provider based on the right combination of cost and ease of process.

Artificial Intelligence is used today, to determine the creditworthiness of those who don’t have any credit history like students or immigrants etc. It also helps to improve customer experience, e.g. by showing pre-approved loan amount. AI makes loan approvals quick and easy, reduce operational costs and these savings can then be extended to customers in the form of lower rates. Artificial Intelligence can process large amounts of data that human underwriters would simply not be able to make sense of.

Machine learning streamlines the process, drastically reduces the likelihood of errors and significantly cuts down the time it takes to approve a loan and disburse funds to the borrower, thereby enhancing the customer experience.

AI & Machine learning also helps to detect fraud by comparing customer behavior with the baseline data of normal customers and removing outliers.

Today apart from credit score and income, lenders are also looking at the digital footprint, payment data from other sources, purchase history, professional reputation from LinkedIn and other sources.

This is called alternative data sourcing. The use of machine learning to analyze this alternative data in loans and credit rating is going to raise some privacy, ethical, and legal concerns.

The future of digital lending will reduce the friction associated with the borrowing process, eliminating paperwork and moving all of the steps of the customer journey to an online and mobile capability. AI and Machine learning will become an inherent part of financial lending.

How Robotic Process Automation helping Digital Age

Digital has brought in so many technological advances to this age and one of them is Robotic Process Automation (RPA).

A simple definition of RPA is, automation of business processes across the enterprise using software robots. Any repetitive task which requires some decision making is an ideal candidate for RPA. Automation has become an integral part of Digital Transformation. Implementing these software robots to perform routine business processes and eliminate inefficiencies is the key for business leaders.

Today’s organizations often need to execute millions of repetitive and time-intensive business processes each day. Using RPA they can automate administrative functions such as customer address changes, registrations and other high-volume tasks and transactions. This helps avoids human errors & also allows employees to spend more time & focus on customer-related functions for better customer experience.

RPA is well suited for processes that are clearly defined, repeatable and rules-based. Any company that uses workforce on a large scale for general knowledge process work, where people are performing high-volume, high transactional process functions, will boost their capabilities and save money and time with RPA software.

Process automation can expedite back-office tasks in finance, procurement, supply chain management, accounting, customer service and human resources, including data entry, purchase order issuing, a creation of online access credentials.

By adding the cognitive computing power of Natural language processing, speech recognition, and machine learning, businesses can achieve high-end tasks which require human interventions.

Automation of front-end operations typically involves chatbots or conversational agents. RPA can provide answers to employees or customers in natural language rather than in software code. This can help to conserve resources for large call centers and for customer interaction centers.

As RPA brings more technologically-advanced solutions to businesses around the world, they bring a multitude of benefits as below.

·       Increased Speed: routine tasks are carried out swiftly by RPA without any intervention, thereby faster time to resolution

·       Reduced labor costs due to software robots than humans

·       Enhanced employee experience: since repetitive tasks as taken care by RPA, employees can spend quality time for strategic work and enjoy their work life

·       Higher quality: better consistency & accuracy due to minimized variations and better customer service

·       Enhanced insights: by automating the data collected and applying Big Data analytics for improved efficiency

·       Scalability: robotic workforce can be scaled to any level required

BPO industry is the most benefited sector due to RPA. Insurance, Banking industries use RPA for KYC, claims processing, policy admin, statement reconciliation, credit card application processing etc.

Automation Anywhere, Blue Prism, UiPath & Verint are some of the few top vendors in the market today.

Be ready for RPA storm coming in near future with the addition of artificial intelligence capabilities. 

How machine learning APIs are impacting businesses?

In this Digital age, every organization is trying to apply machine learning and artificial intelligence to their internal and external data to get actionable insights which will help them to be closer to today’s customer.

A few years back it was the field only for data scientists and statisticians, who used to analyze the data, apply several techniques and provide results.

Today many of the organizations are using APIs to access the ready-made algorithms available in the market as they make it easy to develop predictive applications. In fact, you don’t even need to have an in-depth knowledge of coding or computer science to introduce them into your apps.

APIs provide the abstraction layers for developers to integrate machine learning into real-world applications without worrying about which technique to use or how to scale the algorithm to their infrastructure.

These APIs can be categorized broadly into 5 groups:

·        Image and Face Recognition: It understands the content of the image, classifies the image into various categories, detects individual objects and faces, detects labels and logos from the images.

·        Language Translation: Translate text between thousands of languages, allows you to identify in which language any text that you need to analyze was written. Some APIs allows organizations to communicate with the customer in their language.

·        Speech Recognition and Conversion: Today most of the customer service is handled by Chatbots with underlying APIs helping simple question and answer. Speech to text APIs are used to convert call center voice calls into text for further analysis.

·        Text /Sentiment Analytics using NLP: With the rise of Social Media, consumers easily express and share their opinions about companies, products, services, events etc. Companies are interested in monitoring what people say about their brands in order to get feedback or enhance their marketing efforts. These APIs can identify, analyze, and extract the main content and sections from any web page. They further help in to analyze unstructured text for sentiment analysis, key phrase extraction, language detection and topic detection. There are some tools also helps in spam detection.

·        Prediction: These APIs, as the name suggests helps to predict and find out patterns in the data. Typical examples are Fraud detection, customer churn, predictive maintenance, recommender systems and forecasting etc.

Google Cloud, Microsoft Cognitive Services, Amazon Machine Learning APIs & IBM Watson APIs are the leaders in the market.

With growing number of free/reasonably priced APIs and tsunami of data generated every day, the race is on as to which is the best Machine Learning API.

These machine learning APIs are not yet perfect or matured and they will take some time to learn and act accurately. But they allow faster time to market-based on ready availability, rather than asking data scientist to code the algorithms.

In future, machine learning will lead to revolutions that will intensify human capabilities, assist people in making good choices and help navigate through the world in powerful ways, like Iron Man's Jarvis.

Machine Learning - The brain of Digital Transformation

We are all familiar with machine learning in our everyday lives. Both Amazon and Netflix use machine learning to learn our preferences and provide a better shopping and movie experience.

Artificial intelligence (AI) has stormed the world today. It is an umbrella term that includes multiple technologies, such as machine learning, deep learning, and computer vision, natural language processing (NLP), machine reasoning, and strong AI.

Organizations are using machine learning for various insights they want to know about consumers, products, vendors and take actions which will help grow the business, increase the consumer satisfaction or decrease the costs.

Here are some top use cases for machine learning:

·     Predicting & preventing cyber-attacks: With WannaCry making havoc in many organizations, machine learning algorithms have become extremely important to look for patterns in how the data is accessed, and report anomalies that could predict security breaches.

·     Algorithmic Trading: Today many of financial trading decisions are made using algorithmic trading at higher speed, to make huge profits.

·     Fraud Detection: This is still one of the key issues in all the financial transactions. With the help of deep learning/artificial intelligence, the identification and prediction of frauds have become more accurate.

·     Recommendation Engines: In this digital age, every business is trying hyper-personalization using recommendation engines to give you a right offer at right time.

·     Predictive Maintenance: With embedded sensors as the Internet of Things, many of the heavy industrial machinery manufacturers are applying machine learning to predict the failures in advance, to avoid the costly downtime and improve efficiency.

·     Text Classification: Machine Learning with NLP is used to detect spam, define the topic of a news article or document categorization.

·     Predict patient’s readmission rates: By taking into consideration patient’s history, length of stay in hospitals, lab results, doctor’s notes, hospitals now can predict readmission to avoid penalties and improve patient care.

·     Imaging Analytics: Machine learning can supplement the skills of doctors by identifying subtle changes in imaging scans more quickly, which can lead to earlier and more accurate diagnoses.

·     Sentiment Analysis: Today, it is important to know consumer emotions while they are interacting with your business and use that for improving customer satisfaction. Nestle, Toyota is spending huge money and efforts on keeping their customer’s happy.

·     Detecting drug reactions: With Association analysis on healthcare data like-the drugs taken by patients, history & vitals of each patient, good or bad drug effects etc; drug manufacturers identify the combination of patient characteristics and medications that lead to adverse side effects of the drugs.

·     Credit Scoring & Risk Analytics: Using machine learning to score the creditworthiness of cardholders, defaulters, and risk analytics.

·     Recruitment for Clinical Trials: Patients are identified to enroll into clinical trials based on history, drug effects

With today’s advanced cognitive computing capabilities, image/speech recognition, language translation using NLP has become a reality which is used in very innovative use cases.

Machine learning is nothing new to us but today it has become the brain of digital transformation. In future, machine learning will be like air and water as an essential part of our lives.

Why Data Visualization matter now?

Data Visualization is not new, it has been around in various forms for more than thousands of years. 

Ancient Egyptians used symbolic paintings, drawn on walls & pottery, to tell timeless stories of their culture for generations to come.

Human brain understands the information via pictures more easily than writing sentences, essays, spreadsheets etc. You must have seen traffic symbols while driving…why do they have only 1 picture instead of writing a whole sentence like school ahead, deer crossing or narrow bridge? Because you as driver can grasp the image faster while keeping your eyes on the road.

Over last 25 years technology has given us popular methods like line, bar, and pie charts showing company progress in different forms, which still dominate the boardrooms.

Data visualization has become a fundamental discipline as it enables more and more businesses and decision makers to see big data and analytics presented visually. It helps identify the exact area that needs attention or improvement than leaving it to the leaders to interpret as they want.

Until recently making sense of all of that raw data was too daunting for most, but recent computing developments have created new tools like Tableau, Qlik with striking visual techniques, especially for use online, including the use of animations.

There is a wealth of information hiding in the data in your database that is just waiting to be discovered. Even historical complicated data collected from disparate sources start to make sense when shown pictorially. Data Scientists do a fantastic job of analyzing this data using machine learning, finding relationship but communicating the story to others is the last milestone.

In today's Digital age, we as consumers generate tons of data every day and businesses want to use that for hyper-personalization, sending right offers to us by collecting, storing & analyzing this data. Data Visualization is the necessary ingredient to bring power of this big data to mainstream.

It is hard to tell how the data behaves in the data table. Only when we apply visualization via graphs or charts, we get a clear picture how the data behaves. 

Data visualization allows us to quickly interpret the data and adjust different variables to see their effect and technology is increasingly making it easier for us to do so. 

The best data visualizations are ones that expose something new about the underlying patterns and relationships contained within the data. Data Visualization brings multiple advantages such as showing the big picture quickly with simplicity for further action.

Finally as they say “A picture is worth a thousand words” and it is much important when you are trying to show the relationships within the data.

Data is the new oil, but it is crude, and cannot really be used unless it is refined with visualization to bring the new gold nuggets.

5 ways to improve the model accuracy of Machine Learning!!

Today we are into digital age, every business is using big data and machine learning to effectively target users with messaging in a language they really understand and push offers, deals and ads that appeal to them across a range of channels.

With exponential growth in data from people and & internet of things, a key to survival is to use machine learning & make that data more meaningful, more relevant to enrich customer experience.

Machine Learning can also wreak havoc on a business if improperly implemented. Before embracing this technology, enterprises should be aware of the ways machine learning can fall flat. Data scientists have to take extreme care while developing these machine learning models so that it generate right insights to be consumed by business.

Here are 5 ways to improve the accuracy & predictive ability of machine learning model and ensure it produces better results.

·       Ensure that you have variety of data that covers almost all the scenarios and not biased to any situation. There was a news in early pokemon go days that it was showing only white neighborhoods. It’s because the creators of the algorithms failed to provide a diverse training set, and didn't spend time in these neighborhoods. Instead of working on a limited data, ask for more data. That will improve the accuracy of the model.

·       Several times the data received has missing values. Data scientists have to treat outliers and missing values properly to increase the accuracy. There are multiple methods to do that – impute mean, median or mode values in case of continuous variables and for categorical variables use a class. For outliers either delete them or perform some transformations.

·       Finding the right variables or features which will have maximum impact on the outcome is one of the key aspect. This will come from better domain knowledge, visualizations. It’s imperative to consider as many relevant variables and potential outcomes as possible prior to deploying a machine learning algorithm.

·       Ensemble models is combining multiple models to improve the accuracy using bagging, boosting. This ensembling can improve the predictive performance more than any single model. Random forests are used many times for ensembling.

·       Re-validate the model at proper time frequency. It is necessary to score the model with new data every day, every week or month based on changes in the data. If required rebuild the models periodically with different techniques to challenge the model present in the production.

There are some more ways but the ones mentioned above are foundational steps to ensure model accuracy.

Machine learning gives the super power in the hands of organization but as mentioned in the Spider Man movie – “With great power comes the great responsibility” so use it properly.