How HR Analytics play in Digital Age

Today every company is acting on the digital transformation or at least talking about digital transformation. While it is important to drive it by analyzing customer behavior, it is extremely important to understand who from your organization is acting upon it – your employees.

Hence knowing your employees first and then go to explore the outer world of customers, is the best strategy for digital transformation.

HR plays an important role in knowing the employees. As the volume of data available to HR has increased exponentially over the few years. The data on recruitment, onboarding, employee personal data, their training, performance and attrition data can give tremendous insights to an organization.

HR analytics is focused on making the most of the vast amounts of HR data, organizations have gathered as mentioned above.

Collecting employee sentiments, while they are in office is a good way of improving their life. Happy employees go an extra mile to make your customers happy.

Sentiment analytics can be done for all the touch points:

·      How do employees commute to the workplace – office transport or their own

·      How do they find the parking – nearby or hectic to reach to office

·      How are they welcomed in the office – good fresh smell or closed stuffed environment

·      How is hygiene maintained in the office premises

·      How is the canteen facility, food options, quality of food

·      How the official communication flows through employees, what transparency is maintained

·      How is performance measured and rewarded

·      What is the feedback on the onboarding process

·      Emotions due to promotions or rewards

·      What are the main reasons for attrition

These data points can help HR a lot in improving the 8-12 hours employees spend in the office.

Descriptive analytics help analyze the current scenario for HR:

·      Who is performing well and who may need some additional training or support in order to raise their game

·      Where your best employees come from

·      What recruitment channels are most effective

·      Which projects are delivering on time and what are their project pyramids

·      Which band employees are leaving frequently

Further predictive analytics can be used in several ways:

·      Which candidates are likely to join your organization

·      Which new hires will become your highest performers in two years

·      Which employees are likely to churn and join other organizations

·      Which band or grade employees will leave early

·      Which skills will be in most demand next quarter and later

·      How many billable employees needed to achieve the revenue targets

HR analytics open new ways to recruit, train, and engage employees. Benefits include a streamlined hiring process, better-prepared & unbiased recruitment managers, reduced attrition and higher customer satisfaction.

Top 5 Big Data use cases in Digital Age

Today data volumes are growing exponentially and it is coming from various sources like sensor data from the Internet of Things, log files, social media files like audio/video, call center call logs and all the organization internal data. 

An organization who harness this data and exploit it for their advantage are surviving the competition even from nontraditional players.

Big data has become the foundation for digital transformation.

Though the big data opportunity is growing rapidly, the top two big data challenges that organizations face are determining how to get value out of big data and defining a big data strategy.

Unless you acquire, store and retain the internal data from organization coupled with all the external data from call logs, audio/video files, customer surveys etc. there will be fewer chances of applying analytics on top of it.

Here are top 5 use cases businesses are deploying to get a competitive advantage.

1.   Customer 360-degree view: A lot of companies had problems integrating large bulk of customer data between various databases and warehouse systems. They are not completely sure of which key metrics to use for profiling customers. Hence creating customer 360-degree view became the foundation for customer analytics. It can capture all customer interactions which can be used for further analytics.

2.   Fraud detection and prevention: Financial crimes, fraudulent claims and data breaches are the most common challenges faced by organizations across various industries. Thanks to big data analytics and machine learning, today’s fraud prevention systems are much better at detecting criminal activity and preventing false positives. Today with help of Big Data platforms, banks can store all the historical data they have which can help in better fraud detection.

3.   Recommendation engines: In this digital age, every business is trying hyper-personalization using recommendation engines to give you a right offer at right time. organizations that haven't taken advantage of their big data in this way may lose customers to competitors or may lose out on upsell or cross-sell opportunities

4.   Sentiment Analysis: Today, it is important to know consumer emotions while they are interacting with your business and use that for improving customer satisfaction. Big data and social media channels together help in analyzing customer sentiments which gives organizations a clear picture of what they need to do to outperform their competitors. Disney, Nestle, Toyota is spending huge money and efforts on keeping their customer’s happy.

5.   Predictive and preventive maintenance: With internet of things and sensor technology data is captured from machines, equipment, and devices in real time. All the data is put to use for predicting the failures up front and reduce unplanned downtime and maintenance costs. Companies like GE are using Digital Twins in their wind farm to drive down the cost of electricity.

Big Data is nothing new today and companies are building data lakes to take advantage of storing and retaining any number of years’ worth of history.  

There are many more use cases but which other use cases you can think of that measure the success of an organization?

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.

How Customer Analytics has evolved...

Customer analytics has been one of hottest buzzwords for years. Few years back it was only marketing department’s monopoly carried out with limited volumes of customer data, which was stored in relational databases like Oracle or appliances like Teradata and Netezza.

SAS & SPSS were the leaders in providing customer analytics but it was restricted to conducting segmentation of customers who are likely to buy your products or services.

In the 90’s came web analytics, it was more popular for page hits, time on sessions, use of cookies for visitors and then using that for customer analytics.

By the late 2000s, Facebook, Twitter and all the other social channels changed the way people interacted with brands and each other. Businesses needed to have a presence on the major social sites to stay relevant.

With the digital age things have changed drastically. Customer is superman now. Their mobile interactions have increased substantially and they leave digital footprint everywhere they go. They are more informed, more connected, always on and looking for exceptionally simple and easy experience.

This tsunami of data has changed the customer analytics forever.

Today customer analytics is not only restricted to marketing for churn and retention but more focus is going on how to improve the customer experience and is done by every department of the organization.

A lot of companies had problems integrating large bulk of customer data between various databases and warehouse systems. They are not completely sure of which key metrics to use for profiling customers. Hence creating customer 360 degree view became the foundation for customer analytics. It can capture all customer interactions which can be used for further analytics.

From the technology perspective, the biggest change is the introduction of big data platforms which can do the analytics very fast on all the data organization has, instead of sampling and segmentation.

Then came Cloud based platforms, which can scale up and down as per the need of analysis, so companies didn’t have to invest upfront on infrastructure.

Predictive models of customer churn, Retention, Cross-Sell do exist today as well, but they run against more data than ever before.

Even analytics has further evolved from descriptive to predictive to prescriptive. Only showing what will happen next is not helping anymore but what actions you need to take is becoming more critical.

There are various ways customer analytics is carried out:

·       Acquiring all the customer data

·       Understanding the customer journey

·       Applying big data concepts to customer relationships

·       Finding high propensity prospects

·       Upselling by identifying related products and interests

·       Generating customer loyalty by discovering response patterns

·       Predicting customer lifetime value (CLV)

·       Identifying dissatisfied customers & churn patterns

·       Applying predictive analytics

·       Implementing continuous improvement

Hyper-personalization is the center stage now which gives your customer the right message, on the right platform, using the right channel, at the right time

Now via Cognitive computing and Artificial Intelligence using IBM Watson, Microsoft and Google cognitive services, customer analytics will become sharper as their deep learning neural network algorithms provide a game changing aspect.

Tomorrow there may not be just plain simple customer sentiment analytics based on feedbacks or surveys or social media, but with help of cognitive it may be what customer’s facial expressions show in real time.

There’s no doubt that customer analytics is absolutely essential for brand survival.

See this simple introduction to Natural Language Processing (NLP)

Today, with Digitization of everything, 80 percent the data being created is unstructured. 

Audio, Video, our social footprints, the data generated from conversations between customer service reps, tons of legal document’s texts processed in financial sectors are examples of unstructured data stored in Big Data.

Organizations are turning to natural language processing (NLP) technology to derive understanding from the myriad of these unstructured data available online and in call-logs.

Natural language processing (NLP) is the ability of computers to understand human speech as it is spoken. NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Machine Learning has helped computers parse the ambiguity of human language.

Apache OpenNLP, Natural Language Toolkit(NLTK), Stanford NLP are various open source NLP libraries used in real world application below.

Here are multiple ways NLP is used today:

The most basic and well known application of NLP is Microsoft Word spell checking.

Text analysis, also known as sentiment analytics is a key use of NLP. Businesses are most concerned with comprehending how their customers feel emotionally adn use that data for betterment of their service.

Email filters are another important application of NLP. By analyzing the emails that flow through the servers, email providers can calculate the likelihood that an email is spam based its content by using Bayesian or Naive based spam filtering.

Call centers representatives engage with customers to hear list of specific complaints and problems. Mining this data for sentiment can lead to incredibly actionable intelligence that can be applied to product placement, messaging, design, or a range of other use cases.

Google and Bing and other search systems use NLP to extract terms from text to populate their indexes and to parse search queries.

Google Translate applies machine translation technologies in not only translating words, but in understanding the meaning of sentences to provide a true translation.

Many important decisions in financial markets use NLP by taking plain text announcements, and extracting the relevant info in a format that can be factored into algorithmic trading decisions. E.g. news of a merger between companies can have a big impact on trading decisions, and the speed at which the particulars of the merger, players, prices, who acquires who, can be incorporated into a trading algorithm can have profit implications in the millions of dollars.

Since the invention of the typewriter, the keyboard has been the king of human-computer interface. But today with voice recognition via virtual assistants, like Amazon’s Alexa, Google’s Now, Apple’s Siri and Microsoft’s Cortana respond to vocal prompts and do everything from finding a coffee shop to getting directions to our office and also tasks like turning on the lights in home, switching the heat on etc. depending on how digitized and wired-up our life is.

Question Answering - IBM Watson is the most prominent example of question answering via information retrieval that helps guide in various areas like healthcare, weather, insurance etc.

Therefore it is clear that Natural Language Processing takes a very important role in new machine human interfaces. It’s an essential tool for leading-edge analytics & is the near future.

A to Z of Digital Transformation

Today every part of the business is subject to new expectations, competitors, channels, threats, and opportunities. Every business has the potential to be a digital business.  

Businesses that digitally transform will be able to connect more closely with customers, speed up the pace of innovation and, as a result, claim a greater share of profit in their sectors. Today digitally transformed companies have an edge; tomorrow, only digital businesses will succeed.

Here is my version of A-Z of Digital Transformation.

Artificial Intelligence: AI is the capability of a machine to imitate intelligent human behavior. BMW, Tesla, Google are using AI for self-driving cars. AI should be used to solve real-world tough problems like climate modeling to disease analysis and betterment of humanity

Big Data: This serves as a foundational backbone for digital transformation. Hyper-personalization and real-time recommendations based on the geo-location of the customers will be the key to success. Big Data Analytics is driving force for action.

Customer Centricity: The most important theme of digital transformation is it should be customer focused with a consistent, enjoyable and very personal experience to customers.

Disruption: Digital has disrupted many businesses so far who did not take it seriously like Blockbuster, Kodak, Borders and brought new leaders on the horizon like Airbnb, Uber, Netflix, Spotify.

Employee-driven: Digital Transformation will be successful only when employees are involved, educated and empowered to enhance the customer experience.

Fail Fast: Digital is all about deploying quick PoCs and check the outcome. If it does not work then drop the concept/idea and move on to a new one. Be prepared to change the pace or direction as necessary.

Growth Hacking: Focuses on building products that fit the market needs, with a viral push to market with a very simple idea & all the digital tools like email, tweets, blogs. Zynga used FB alerts and online adverts while DropBox offered extra storage space for referring friends for growth.

Hyper-personalization: Use of browsing histories, past purchases, social media data, and internal customer data to provide more personalized and targeted products, services, and content. It is about being relevant at the right place and the right time to the right person.

Internet of things: All the businesses are aiming at reaching their Customers anywhere, anytime, any platform with any device.  All such smart devices or physical objects that are connected to the internet, are continuously emitting data and communicating with each other. IoT based real-time predictive maintenance in manufacturing is becoming important to save costs and downtime.

Journey Maps: Creating customer journey maps is the prime and initial milestone for digital transformation where you understand the pitfalls and pain points with every touch point you have with customers. Once they are in place then plan with most impacting problems.

KNN: K nearest neighbor algorithm in Machine Learning used for classification problems based on distance or similarity between data points.

Leadership Support: Without actual drive from CEO and Board members, digital will not move further as it involves cultural business transformation.

Mobile First: Today we live in “always on, always connected” world, and use mobile for almost everything. We spend more time on mobile than any other digital channel. Start your digital implementation with mobile first in mind. More than half of the world population is on mobile now and

NFC: Near Field Communication used for a contactless, WiFi-style technology that could already be in your smartphone. A few years from now your credit cards, bus pass, train tickets, loyalty cards for high street coffee shops will be gone due to digital transformation and you only carry your phone.

Omni-Channel: It is about true continuity of customer’s experience even when they flip channels to complete a single transaction. Disney, Virgin Atlantic, Starbucks are great examples of Omni-Channel digital transformation.

Prioritize: Spend where you’ll make the biggest difference for the business. Small and quick successes will be drivers for digital transformation.

Quality: Quality of data collected by all the channels is extremely important in digital transformation for generating meaningful insights. Garbage in, Garbage Out still remains valid in the digital age.

Robotics: Robots are machines with programmed movements that allow them to move in certain directions or sequences. Artificial intelligence has given robots more ability to process information and to learn.

Sentiment Analytics: Sentiment analysis is all about helping companies gain better insights into their customers, and helping them to bridge the gap between insight and action by analyzing positive and negative sentiments.

Talent: To deliver on the digital transformation, organization have to get the right talent. As Jim Collins said in his book “Good to Great”, get the right people in the bus put them in right sits and then decide where the bus will go.

User Experience: This is extremely important considering the focus on the customer. Use of responsive web designs to adjust to any screen sizes with ease, make the website accessible on any platform, intuitive and pleasant to use is the key.

Virtual Reality: Virtual Reality is making a lot of impact on the world we live in today. Everything that we know about our reality comes by way of our 5 senses – Sight, Sound, Smell, Touch & Taste. Today Real Estate, Travel companies are coming up with VR gadgets to give the feel of virtual walks of houses or destinations like Eiffel tower / or Grand Canyon experience on your living room couch.

Wearables: They come in various forms, like smart watches, health trackers, Google Glass, interactive clothing, gesture controllers and the list goes on. Sooner or later, all of us are identified by the data we generate, and wearables represent a quantum leap in the type and quantity of data collected — which is both an interesting and a scary proposition

Xamarin: A modern programming language for iOS, Android & Windows mobile platforms.

Yarn: The earlier map-reduce has undergone a complete overhaul and now called yarn that allows multiple data processing engines such as interactive SQL, real-time streaming, data science and batch processing to handle data stored in a single platform.

ZooKeeper: ZooKeeper is a Hadoop centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services.

Digital Transformation brings changes in product creation and delivery, changes in IT infrastructure, changes in the consumption and payment methods to ‘go digital’.