Talent Management with the help of people analytics

The most valuable and essential resource for any organization is its people. They are the heart and drivers of success in any organization. An organization needs to invest in its people by providing them the right environment to grow professionally and personally. Talent development is one area that is well sought. Now and again, the human resource department comes with innovative ideas for talent management and development. The process of talent development starts right from selecting suitable people for a particular profile to their retention. The global work environment is changing every day. More and more companies are changing the way they were looking at talent development. Companies used to think that if they recruit highly skilled people, they need not worry about their progress for several years. They were expecting highly skilled people to build a skilled workforce on their own. But not now, since the skill set required to perform a job is changing rapidly.

Talent development and management are also going through a period of change. In the organization, an employee continuously looks for a flexible work environment and diverse profile. They have to be reskilled many times during their span. An innovative work environment has become a basic need. The creative person will look for challenging work and expect their employers to provide the same. Data-driven decision-making has become essential to cater to this need, and using Analytics is the best way. Data Analytics is already helping organizations for their customer experience and product management, so using their internal workforce is not a new concept. Using HR Analytics, which specializes in people’s data, is the fresh hot cake for human resource and talent management. In the present time, if you are not utilizing your data to generate valuable insights, then you are missing something important. Analyzing people’s data for accessing their development needs can add value. So let us see how analytics can help the organization in its talent development programs. The process of talent development starts with hiring the right talent. The analytics system inside the organization helps in storing all information related to the employees. This information is not limited to their demographic and skill set but about their behavioral habits shown during the hiring process too. One can store the recording of the employee’s conversation during the interview process, which can be analyzed using big data methodologies for depth understating while onboarding of the employee. The combination of human and machine intelligence is shifting the thinking process within the HR spectrum. Machine learning algorithms help analyze people’s data and provide the required input for skill measurement at any point in time. Analytics tools are managing the talent scorecard, skill matrix, workforce planning, talent pool development, analysis, access to resources, etc. Several tools can quickly build a useful talent dashboard, which can easily help senior management view the current talent pool. Analytics helps in all significant talent development areas, such as recruiting, development, and retention of talent.

These days’ organizations are collecting multiple data points about their employees. Later these data points help in the development of training programs. Bringing an agile methodology with the use of analytics is making talent development programs friendlier. Predictive analytics techniques help HR managers segment employees based on their training needs, building different retention strategies for another segment. By using statistical modeling techniques, cost management has become more comfortable. Predictive analytics is assisting hiring managers in hiring the right talent based on the data. For example, an HR manager can get multiple inputs from several available sources on the internet using analytics tools. Be it a social media platform where a prospective employee is posting his view. Therefore, the origins of information about the employees are not limited to their resume anymore. After hiring, their skill set development, a program they are part of, and their platform are being analyzed by these analytics tools to get better insights about the employee.

Overall, data analytics using machine learning, artificial intelligence, and big-data methodology is becoming a future for talent development. Therefore, all the organizations recommend bringing workforce planning as close as possible to HR analytics. Let data-driven decision becomes a culture so every employee, no matter what level he is in the system, believes in data analytics’s capability. There are several models exist which help in building data-driven culture across the organization. An organization needs to select one of them based on their business areas and living culture. Transformation may not be natural, but when done, the future will look different.

(The above article is published by ISTD Delhi chapter in their Souvenir 2020 which was released in National Conclave on 5th and 6th September 2020)

Application of Data Science in Marketing

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The consumer is changing. The mindset of buyers does not have a specific way. Every day, Companies are trying to read the consumer’s mind and are always eager to explore what is going inside the mind of a consumer while making a purchase decision. The market place has changed now. The conventional marketing techniques are becoming obsolete. The first set of changes in product marketing came when the culture of supermarkets emerged. Many traditional shopkeepers had rejected the supermarket concept then, but when reality hit them hard, then the corner grocery stores also allowed customers to choose and pick the product on their own. The same thing repeated when the e-commerce portal started selling the products online. No one had ever imagined that one could buy anything on a click while sitting in the comfort of his or her own or while moving.

Data Science is one of the areas, which is helping the marketing department of every company. Whether one is selling the product or services, both are getting tremendous help in every decision they make from data science techniques. The companies are collecting and storing the data every second. This data is producing valuable information using data science techniques and tools. From product inception to product decline, everything is changing with the help of data science. Marketing research and consulting firms who are the most crucial supplier of consumer data to large organizations are generating insights from these data sets. The data is present in many formats and broadly classified in two types: structured and unstructured data. Nowadays, there are many techniques and tools available to analyze both types of data and provide real-time information for decision-making. We have highlighted some of the primary applications of data science techniques in the critical area of marketing decision making.

Customer Segmentation – If, as a company, you know who is buying your product, then you will never have to worry about your bottom line. Understanding the different segments of buyers can help in many ways. You can create a customized product for different types of consumers. Your marketing strategy can be shaped and reach to the relevant consumer. Data Science can help create a segment of your consumer based on their buying patterns and demographic information. Customer Relationship Management systems can provide secondary data and online methodology to collect primary data about the customers, which can help data scientists build the segmentation model. Cluster Analysis techniques are data science techniques that help define the segment of consumers for any products. Behavioural data plays an important role apart from demographic information while creating a segment of consumers.

Pricing Strategy – Data Science helps in defining the appropriate price of a particular product. It is essential to know how much a consumer will pay for a specific product. One of the crucial techniques is conjoint analysis. In this analysis, a consumer is shown a different combination of attributes of a product, and then they select which one he will prefer to buy and on what price. Post that, the data science algorithm analyzes the data to provide the optimal amount chosen by a set of consumers.

Competition Benchmarking– Market is a battleground where there are so many opponents who want to attract consumers. There is no shortage of options for any product. The age of monopoly is almost gone. Therefore, it is essential to know whom you are fighting. The completion benchmarking can help the company to win this. A company compares themselves with their competition by this process. There are several methods available under data science that can help analyze the performance of the product, representation of channel, and reach. Social Media Analytics is highly used these days for improving the benchmarking process.

Supply Chain Management – This is one of the critical areas for any business, especially for a product-based company. Nowadays, the whole process of Supply chain management is automated using data science algorithms and tools. Some companies have built a zero manual intervention model for their supply chain management. In the past, the supply chain function was an operation process purely, but with the help of the latest data science techniques, blockchain, artificial intelligence, it is an emerging strategic advantage. Demand forecasting, procurement, distribution is using various modelling techniques and real-time data.

Target Marketing – The dimension of marketing has completely changed now. In the past, a banner or posters were placed on various locations where maximum people can see, and the company used to believe that the product information has reached the right consumer that is not true anymore. Nowadays no one cares what is displayed until it is essential information for him or her. The predictive modelling techniques in data science is helping companies to create target based marketing campaign. The online advertisement is fully algorithm-driven. The data science algorithm running behind knows whom to show what. These automated techniques provide a maximum reach of any product to the right consumers.

The above mention examples show that it is essential for every marketing person to know about data science. The decision based on gut filling may not always help, but the decision based on the right data will still help. There are many resources available to educate in the field of data science. The prerequisite for enhancing the knowledge in the file of data science is basic mathematics and statistics, along with computer science. A marketer who has expertise in his domain and basic understating of data science can generate gold for any organization. Remember that in the upcoming time, only data will be the truth rest everything will be an illusion.

(The Above article has been published in the Second Edition of PHD-Chamber Journal of Ideas Innovations)

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Text Analytics for understating the emotion in the written text

In the present day, every company has access to a large volume of unstructured data, which can help in strategic business decisions. One of the essential data types is textual data. For example, a business must know what customers feel about their products or services. Similarly, in research, it is essential to understand what a respondent is saying on his own. When we prepare a structured questionnaire to collect responses, we ask close-ended questions that give us direct answers, and we have open-end or free answers questions in which the respondent writes sentences. These two types of questions provide two kinds of data one in a structured format and another in the unstructured format as a text format. The whole ecosystem of the data is of the two forms only. In an Unstructured format, there is further addition of Audio, video, and images.

Text Analytics is one area by which the text or responses collected through different media are analyzed using tools and algorithms. Previously, the text data was investigated by the manual reading of each sentence. With the advancement and availability of the latest tools, the text analytics process is moving towards automation. Several methods and tools available can quantify the text data to provide patterns, trends, and insights. Natural Language Processing (NLP) is one of the widely used concepts that help in analyzing text.

The input for text analytics or text mining comes from online reviews, twitter feeds, Facebook posts, emails, survey questions, and customer feedback. Due to the boost of social media platforms, everyone has the power to write content related to anything. These written contents are a gold mine for respective stakeholders. Once the input is in place, the software or tools perform further analysis. To provide the full context, let us consider the process followed in the R software.

Firstly, in the R environment, a corpus is built using the input data that is simply a collection of unstructured text. Once the corpus is ready, then the data cleaning is done by removing the numbers, punctuation, stop words, whitespace, etc. After that, the tool generates a Document Term Matrix. The document term matrix is a matrix in which the words are present as columns with their count as rows. This matrix is used for further analysis to get valuable insights from the text data.

The text analytics results come in the form of word count, frequencies, word clouds, word association, correlations, and clusters. Apart from these analyses, the tool also provides the facility to conduct sentiment analysis of text data. The sentimental analysis is the process by which one can interpret and classify the data into three emotions: positive, negative, and neutral. With the help of the content analysis, a business or research scholar can identify respondent sentiment towards the company, brands, products, or research scenarios. If a business can know what a customer feels, then they can improve their offerings.

Artificial Intelligence is helping the text analytics domain in a big way. The infrastructure for holding a massive amount of data is available these days. So many cloud service providers give services for storing and mining unstructured data. The future is looking promising for text analytics.

(The above article was published as an Abstract at ICMIT2020 conference)

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The changing face of research

Technology has bought research from the world of pen and paper surveys to the digitally recorded and machine analyzed. Right from collecting data to the presentation of findings, everything is quick, concise, and available at a click. Everyday new tools are emerging and replacing the traditional method of conducting research.

While in traditional research, customers and prospects used to be the target, and data collection happens using face-to-face Interviews and surveys. In the current digital world, buying, watching, search habits, etc. is defining the persona of a person which is a target. How we behave digitally has become more important than what we answer in a particular survey question. Data retrieved from the apps installed on our smartphones tell more about us which usually we don’t admit or say. The devices that we use – be it handheld or laptop, fitness gadgets, etc. examine our habits’ patterns and provide input to the researchers. Access and uses of significant data sources are widening with the help of the latest tools and technologies.

These new-age methods of identifying suitable consumers not only are more reliant but also are timesaving. Marketers today need real-time availability of insights for quick decision-making. The analysis of the information collected is not limited to regular findings using survey analysis tools. It is now also based on Machine-learning models, Natural language processing, etc through which the results are both faster and quicker.

The final delivery of information is also changing face with earlier being only as a report presentation using PowerPoint, Word, or Excel sheets to live data streaming dashboards. These customized dashboards are available on mobile devices too. The right time research is evolving as real-time research.

With all these advancements, there are also some inherent challenges in the transition. Lack of required talent is one of them – there is no formal training available to train on modern research techniques. The only source is the internet, and the availability of humongous information on the internet is scary and confusing for learning the advanced and upcoming tools or technologies. Validation of information present on the internet becomes difficult for the researcher. Another critical challenge is the selection of the right tools from multiple new-age options. Every means and method has its pros and cons. It is essential to understand any particular tools or method’s strengths and weaknesses before using it. Some specific techniques might be useful for one type of research but it can be disastrous for another.

Thus, while educating the industry about the new techniques and equipping the current researchers with the latest tools and right skills is the hour’s need.

(The above article was published as an Abstract at ICMIT2019 conference)

Shaping the world through Business Intelligence tools and Analytics

Data is the new trending word, and getting insights from the data is the only need of the hour. In the current era, where an immense amount of data is generated at every second, the only thing we cannot ignore is the data. Business Intelligence, Analytics, Big Data, Machine Learning, Data Science, etc. are the most searched and discussed terms everywhere. Data is not coming from one source, and in one form, it is occurring in all possible types and stored at different places. The challenges companies face to store and analyze these data that come in the form of numbers, texts, images, audios, or videos. The data can be structured or unstructured. Sometimes the source is known and sometimes it is unknown. Data is collected from so many sources such as surveys, reviews, online clickstreams, social media feeds, internal servers, etc. which required a verity of hardware inside the company or on the cloud in the form of distributed computing for storing, maintaining and accessing information.

Therefore, the need for Business Intelligence tools that can generate required insights from these data using analytics has become essential. Digital technology is connecting people, sensors, and devices to the number of people every day. It is creating a new social ecosystem for generating new businesses for the organization. The leading companies such as Google, Facebook, Amazon, Microsoft, and Apple are coming with the latest tools and designing algorithms for data mining and business intelligence and providing infrastructure for every type of user.

Effective use of business intelligence and Analytics changes how an organization can create values and generate the Return on Investment for its stakeholders from the customers. The new business models are transforming into the data-oriented model, and they are driven by analytics. Data visualization provided by new Business Intelligence tools can show real-time data, and machine-learning algorithms generated real-time insights for business decisions.

(The above article was published as an Abstract at ICMIT2018 Conference in April 2018)

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