Real Disruption in BPO industry through Robotics Process Automation by Anjuum Khanna

Automation is a powerful force that is constantly simplifying the way the world functions.  Automation helps remove redundancy and leads to job satisfaction by eliminating repetitive tasks. With the help of robotic machines, industries have been able to achieve what was humanly impossible. Several industries have been revolutionizing their processes using robotics, including the BPO/KPO industry.

The application of robotic machines in the BPO/KPO industry where we perform repetitive tasks can drastically reduce the amount of time taken to perform these tasks as compared to several human counterparts. This reduces the TAT to offer deliverables in an outsourcing industry and leads to an exponential growth.

The Robots/Algo can be programmed to perform the tasks with high precision, thereby improving the quality of the deliverables. The programs that are used to run these processes are based on algorithms that can be improvised constantly to accentuate Process Efficiencies. This kind of customization leads to increase in CSAT and thereby increases the customer loyalty and stickiness to the Brand. The robots are capable of performing end to end tasks needing minimal human monitoring in the completion. This reduces errors and improves performance.

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Nowadays, robots are not only capable of performing repetitive precision tasks but also the kind of tasks that need decision making based on the available information. This is now possible with the help of AI (artificial intelligence). Combining artificial intelligence with robotics has created a new generation of smart robots that are capable of thinking, feeling, understanding and performing the tasks with their cognizance. These robots are capable of handling complex and challenging tasks in a constantly changing business environment. These advanced analytical skills are speedily closing the gap between the physical and digital world thereby creating a better Phygital world.

This new wave called Robotic Process Automation also known as RPA has swept away the back office tasks and revolutionized BPO industries worldwide. With the combination of robotics, big data and artificial intelligence, the world of business are transforming at a fast pace in terms of efficiency, effectiveness, and profitability. The application of robotic process automation, several BPO services such as regular chat and email jobs have been replaced with Chat Bots and self-help applications that constantly monitor and improve customer relationship with the service provider.

In this new world, the developing countries that were improving the economy by providing services are affected by the digital wave. The RPA has led to a severe reduction in headcount and in talent acquisition. The only way to survive in the digital age is to master the latest technologies and tools to drive them. In the future, BPO/BPMS industries will have fewer people and more robots in their structure. This need not mean to hurt the human workforce but can be taken as an upgrade that simplifies the lives of many and improves the ways of the world.

Anjuum Khanna

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Integrating Innovation and the Customer Experience by Anjuum Khanna

Customer behavior patterns continue to change. Complicating matters, customer loyalty and lifetime value continue to dip. Folk might wonder why but the answer is that customers simply have several many options before them and desire the best experience. In this scenario, it is imperative to ensure stackability or the loyalty of a customer.

Key to innovation

The rising demands of the customer could mean that they are looking for ease, comfort, or a wide variety of things. Understanding your customer segmentation and attaching them to the appropriate services is vitally important.

The key to innovation is slicing and dicing data to gain a better understanding of your customer and their journey. You have to know which customer base to sell that product to and where that product sells.

For instance, while working for a top DTH service provider, we decided to launch a line of products. In collaborated with Nielsen and after extensive market surveys, we found out the kind of content customers wanted to consume.

The majority of customers told us they would love to see acting or dancing and the actors and actresses they preferred. We now had a segmented customer base. We created a range of products with a human angle that the customer could buy as a package and learn from.

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Collaboration

Where does innovation come from? It comes from Outside-In thinking. Human-Centered Design (HCD) allows products to be designed in collaboration with the customer to resonate more deeply with the audience. HCD tools ensure that the customer remains part of the entire product lifecycle or decision making life cycle.

Once the customer angle is brought in, that is when differentiation is possible. Be it airlines, automotive or consumer durables, when we say innovation, it means a customer demand for a product that needs to be seen to.

Customer Journey

When approaching the design of the product, it is vital that the customer be part of the entire product lifecycle. Questions need to be asked, surveys conducted and focus groups need to be conducted to check if the product is on the right or wrong path.

Look at the evolution of BFSI products. Previously, it would take a week or even two for a loan to be disbursed. Someone thought about it and realized it was a very primitive way of working. Enter Fintech. Today, all a customer need do is log in to a Fintech company, upload their documents and, if their loan is approved, receive the money in minutes. That is how innovation has changed the life of a regular banking process.

Prioritising Innovation

Understand the design thinking process to solving problems, where the entire leadership of an organization sits and understands the nuances of the customer. You need to be proactive. When coming up with a product, you need to see which pain points are an impediment to the NPS or CSAT scores.

When you put your heads together, innovative ideas come about to address any deterrent to a smooth customer experience. Xiaomi is a great example of a positive customer experience. Despite their minimal marketing expenses, they are able to innovate and collaborate with the customers in their product range. Conversely, Meru Cabs, a previous incumbent taxi service, lost their first-mover advantage and market share when they failed to innovate as Ola and Uber stepped in.

The Big Winner

Whichever way innovation leads us, it will be the customer who ends up on top because, ultimately, everything we do is for the ease of the end user. Innovation will continue to remove biases from our minds and provides greater earning opportunities but the opportunities might not all be what you expect. When banks innovate, they might not want to hire more accountants. Instead, they might hire an algorithmic trader or creative genius. The future is bright and the possibilities endless as innovation continues to ensure our lives become more comfortable than they are right now.

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Author: Anjuum Khanna

4 Ways to Improve Customer Experience by Anjuum Khanna

In 2017, United Airlines learned a hard and expensive lesson in customer experience. The airline lost over $1 billion dollars in value because a passengers’ customer experience went viral on social media.

In this digital age, customer experience is more important than ever. With the presence of social media and the consequent ability of a customers’ experience(s) going viral in a matter of minutes, more companies and organizations have recognized that the way a customer feels about their interaction with the brand can make or mar the business.

In view of the importance of customer experience, how can you improve this vital aspect of your business?

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  1. Respond

Everyone wants to be heard and appreciated. Your customers are no different. Hence, listening and responding to the concerns of your customers is one of the most important steps in improving your customer’s experience. It might be hard work, but endeavor to respond to all the feedback you get from your customers. Doing this can work wonders for your customer retention.

  1. Be Proactive

According to a report in the Harvard Business Review, your customers are left satisfied and develop a sense of loyalty when they spend as little time and effort as possible in getting their problems resolved. Thus, to improve your customer experience, ensure ease of access to help. Actively seek ways to reduce the time taken to receive help. Constantly be on the lookout for product and services that can make their lives easier. Your customer feedback is a great means of identifying new needs and generating more ideas.

  1. Personalize

Customers appreciate customized experiences. Although technology is a great tool in business and service delivery, customized service build relationships that last. Your customers want to feel like they are your only customers; they want to feel valued and special. Hence, bulk, imprecise emails will not help. Give them personalized content and watch your bond grow stronger.

  1. Reward loyalty

Rewarding loyal customers is a great way of improving customer experience. You can show that you appreciate their loyalty in various ways from personalized giveaways to sending greeting card to promotions and discount offers. If your customers feel that you are saying ‘thank you for your service’ this increases the chances of retention. Moreover, rewarded customers often become advocates for your business and can attract more customers.

Customer experience is an important facet of your brand. Use the tips above to improve it and enjoy the immense benefits it can bring!

Reach out to me or read more on CX, Automation & Digital Transformation on my blogs and website.

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Machine Learning – Post By Anjuum Khanna

I, Anjuum Khanna, would give some insights on what Machine Learning. In simpler words machine learning is an application of artificial intelligence that enables the system to learn automatically and the best part of this application is you don’t need to programme explicitly. Anjum Khanna also defines it in a different way that it focuses on the development of computer programs that can access relevant data and use it learn for themselves.

This learning process starts with data or observations (stated and observed), which we can provide in terms of examples or instruction. This learning can also be gathered through direct experience. Primary aim of machine learning is to allow computers to learn automatically without any human intervention or assistance. Machines should also adjust their actions accordingly.

As per author’s definition we can also say that machine learning is subfield of AI. We can see many examples of machine learning such as Siri, Netflix, Google maps, Uber etc. This can tell that how machine learning has upgraded our living.

We can learn more about it by knowing more about machine learning methods. As per Anjum Khanna below methods can tell better about machine learning:-

a) Supervised machine learning algorithms: – it is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher is supervising learning process. We know the correct answers, the algorithm makes predictions on the basis of training data and it gets corrected by the teacher. Learning stops when the algorithm achieves an acceptable level in terms of performance. In Anjum Khanna’s words we can further learn this by few real life examples as

b) Unsupervised machine learning algorithms: – This type of machine learning is more closely aligned with artificial intelligence. On further analysis we can say that in this kind of machine learning a computer can learn to identify complex processes and patterns without a human guidance. Although unsupervised learning is prohibitively complex for some simpler enterprise use cases, but it is more effective while we need to solve problems that humans normally find difficult to tackle. In Anjum Khanna’s words we can further learn this by few real life examples as

I, Anjum Khanna has a very positive vibes that machine learning will do wonders in future. There are few predictions which will be true in future for machine learning:-

  1. Usage in applications: – In next few years, machine learning will become part of almost every software application. Soon all of our devices will be embed with capabilities of machine learning. After that our personal device will become personalised device.
  2. Usage in service industry: – As machine learning becomes increasingly valuable and the technology matures, more businesses will start using the cloud to offer machine learning as a service. This will allow a wider range of organizations to take advantage of machine learning without making large hardware investments or training their own algorithms. As in cutting throat competition personalised service is required in service industry and machine learning will resolve that issue.

So these are only few examples, there will be a great revolution with machine learning. But one trend is consistent across all of these predictions. As this technology advances, more businesses will embrace the AI revolution.

All the best for disruptive future

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Why Artificial Intelligence Matters – Post By Anjuum Khanna

Hi, this is Anjuum Khanna, and today we will talk about most commonly used technology disruptor about which we heard a lot. But I always mention in my blogs that we hear about what technology has done to our world, in the same we should also look forward to unfold future for more opportunities.

So, in Anjuum Khanna’s simple words let’s define AI. As the name speaks it is known as “artificial intelligence” or “machine intelligence”. So Artificial intelligence (AI) is a special feature of machines, in comparison to the natural intelligence displayed by humans and other animals. In computer science, AI research is defined as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. So when a machine is involved in a function like “problem-solving” or “learning” it is also known as artificial intelligence.

As intelligence is a step above the common task so a task which is common is not intelligence. As per me, this word is a word which is full of disputes. So intelligence requires frequent innovation. Let us understand this with a small example.  As optical character recognition is frequently excluded from “artificial intelligence”, has become a routine technology. At one point in time, this was the part of Artificial Intelligence. Right now these technologies are defined as artificial intelligence understanding human speech, competing at the highest level in strategic game systems (such as chess), autonomous cars, intelligent routing in the content delivery network and military simulations.

After this explanation let’s go to history, where we will see how and when AI was defined. Back in the 1950s, the fathers of the field Minsky and McCarthy described artificial intelligence as any task performed by a program or a machine that, if a human carried out the same activity, we would say the human had to apply intelligence to accomplish the task. This is a very simple definition which in Anjuum Khanna’s words communicate that any task which is done with intelligence by the human being is performed by machine can be called as artificial intelligence. So after many disputes in history, we have settled on few criteria like planning, learning, reasoning, problem-solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity which undoubtedly belong to AI.

Type of AI:-

As per me (Anjuum Khanna), segregation is required to see the development stages of any product or technology. We can easily define AI into two categories:-

  • Narrow AI is what we see all around us in computers today. Intelligent systems that have been taught or learned how to carry out specific tasks without being explicitly programmed how to do so.

Let me explain through few examples this type of machine intelligence is evident in the speech and language recognition of the Siri virtual assistant on the Apple iPhone, in the vision-recognition systems on self-driving cars, in the recommendation engines that suggest products you might like based on what you bought in the past. Unlike humans, these systems can only learn or be taught how to do specific tasks, which is why they are called narrow AI.

  • Artificial general intelligence is a futuristic intelligence and is the type of adaptable intellect found in humans, a flexible form of intelligence capable of learning how to carry out vastly different tasks, anything from haircutting to building spreadsheets, or to reason about a wide variety of topics based on its accumulated experience. This is the sort of AI more commonly seen in movies, but this technology doesn’t exist today.

As per the survey conducted by AI developers in between 2040 & 2050 this technology will start developing and by 2075 will achieve 90% of development. However few groups are still confused about its development as till the time we don’t have the hold on the functionality of the human brain we can’t even start with general intelligence.

For the better understanding of Artificial intelligence as per me (Anjuum Khanna) we should understand few basic technologies of this concept.

Machine Learning: –

In Anjuum Khanna’s definition, machine learning is a computer system which can feed large amounts of data, which is then used by the machine to learn how to carry out a specific task, such as understanding speech or captioning a photograph.

Neural networks:-

These are brain-inspired networks of interconnected layers of algorithms, called neurons, that feed data into each other, and which can be trained to carry out specific tasks by modifying the importance attributed to input data as it passes between the layers.

These are two supplementary topics which need to understand with artificial intelligence. One more of AI research is evolutionary computation. This is basically natural selection and sees genetic algorithms undergo random mutations and combinations between generations in an attempt to evolve the optimal solution to a given problem. This approach has even been used to help design AI models, effectively using AI to help build AI.

The most important question that comes to our mind is how AI will change this world. And I m (Anjuum Khanna) having my own thought process on the same. So let’s understand this with an example.

All of the major cloud platforms such as Amazon Web Services, Microsoft Azure and Google Cloud Platform provide access to GPU arrays for training and running machine learning models, with Google also gearing up to let users use its Tensor Processing Units — custom chips whose design is optimized for training and running machine-learning models.

All of the necessary associated infrastructure and services are available from the big three, the cloud-based data stores, capable of holding the vast amount of data needed to train machine-learning models, services to transform data to prepare it for analysis, visualization tools to display the results clearly, and software that simplifies the building of models.

These cloud platforms are even simplifying the creation of custom machine-learning models, with Google recently revealing a service that automates the creation of AI models, called Cloud AutoML. This drag-and-drop service builds custom image-recognition models and requires the user to have no machine-learning expertise.

Cloud-based, machine-learning services are constantly evolving, and at the start of 2018, Amazon revealed a host of new AWS offerings designed to streamline the process of training up machine-learning models.

For those firms that don’t want to build their own machine learning models but instead want to consume AI-powered, on-demand services — such as voice, vision, and language recognition — Microsoft Azure stands out for the breadth of services on offer, closely followed by Google Cloud Platform and then AWS. Meanwhile, IBM, alongside its more general on-demand offerings, is also attempting to sell sector-specific AI services aimed at everything from health care to retail, grouping these offerings together under its IBM Watson umbrella — and recently investing $2bn in buying The Weather Channel to unlock a trove of data to augment its AI services.

AI applications:-

To know more about AI we need to learn through examples. Here are some examples to see its impact on all major industries.

AI in healthcare: – This is the most critical industry as it requires precision and accuracy. The biggest bets are on improving patient outcomes and reducing costs. Companies are applying machine learning to make better and faster diagnoses than humans. One of the best-known healthcare technologies is IBM Watson. It understands natural language and is capable of responding to questions asked of it. The system mines patient data and other available data sources to form a hypothesis, which it then presents with a confidence scoring schema.

AI in business: – Robotic process automation is being applied to highly repetitive tasks normally performed by humans. Machine learning algorithms are being integrated into analytics and CRM platforms to uncover information on how to better serve customers. Chatbots have been incorporated into websites to provide immediate service to customers.

AI in education: – AI can automate grading, giving educators more time. AI can assess students and adapt to their needs, helping them work at their own pace. AI tutors can provide additional support to students, ensuring they stay on track. AI could change where and how students learn, perhaps even replacing some teachers. It can find out the gaps and help in resolving them.

AI in finance: – AI in personal finance applications, such as Mint or Turbo Tax, is disrupting financial institutions. Applications such as these collect personal data and provide financial advice. Other programs, such as IBM Watson, have been applied to the process of buying a home. Today, the software performs much of the trading on Wall Street.

AI in law:- The discovery process, sifting through documents, in law is often overwhelming for humans. Automating this process is a more efficient use of time. Startups are also building question-and-answer computer assistants that can sift programmed-to-answer questions by examining the taxonomy and ontology associated with a database.

AI in manufacturing: – This is an area that has been at the forefront of incorporating robots into the workflow. Industrial robots used to perform single tasks and were separated from human workers, but as the technology advanced that changed.

Here we have seen many directions in which AI has worked and has improved deliverables. This technology is growing day by day and showing improvement in many fields.

 

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