What is data science?
Data science is the practice and theory of using the right data points/processes for extracting hidden insight from given data in a manner that enables businesses to make better decisions. It filters out the unwanted data, efficiently collects useful data, and processes raw numbers to perform better.
The primary objective of Data research is to pose inquiries and find a potential path to study for definite answers and more accentuation set on finding the correct investigation to pose. This can be achieved by anticipating possible patterns, investigating different and separated information sources, and discovering better approaches to dissect data.
What is data analytics?
Data analytics is the practice of analyzing raw data to make conclusions over that information. Its techniques and processes are automated into mechanical methods and algorithms to work over raw data. This information is used to optimize processes to increase the efficiency of the business ultimately.
Implementing data analytics in the business model helps enterprises optimize their performances and reduce costs by identifying efficient ways of doing business that involves storing massive data. There are four basic types of data analytics, i.e., descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.
What is the relation between data science and analytics?
Analytics is one of the practices in data science used to represent raw data in a more natural form to view, understand, compare, and extract insights.
Data analytics is a practice of removing ideas by analyzing data. Data analysts used to carry out this process manually, but now, it has been changing due to automation, machine learning, and artificial intelligence. In today’s world, data analytics is shared among human analysts and algorithms. Algorithms are advantageous in being able to process data on a massive scale, while humans have the cognitive advantage of being able to think beyond the confines of those algorithms.
Data science has the power to change the insights into business by data visualization by exploring and examining data from multiple detached sources. In contrast, data analytics is used to solve the questions given by the company.
Data science deals with formulating issues whose solutions are likely to benefit the business, also, to have hands-on machine learning experience or build statistical models.
How does data science work?
Modern businesses face problems due to the overwhelming of data where most of which are unwanted. The first crucial step is to identify the data that holds value to the targets and putting the right processes in place to collect and handle that data with the help of algorithms and automation. This kind of data science is used to build Google’s deep-learning algorithms, driverless cars, and other AI tech innovations.
In the current marketing environment, data science involves a series of steps to ensure the delivery of right and apt data. The process involves:
- Identifying the data problems and opportunities for improvement.
- Determining the correct datasets and variables.
- Recognizing the sources and collect suitable data.
- Validation of data to ensure accuracy and uniformity.
- Creating data models and algorithms to organize the data.
- Analysis of data to identify patterns.
- Finally, test, optimize, and debug the data processes.
Why is data science important?
The data science technology has rapidly evolved over the past ten years. Now, we don’t need huge teams of data scientists, collectors and analysts to get these insights. With today’s automation tools, the latest advances in machine learning, and artificial intelligence, small businesses can now afford data science in a similar way in which previously only the most prominent organizations could do. Data science has leveled the playing field in terms of businesses to improve performance and growth. This can become an opportunity to beat the competition and lead the market.
How can data science help my business?
Data science helps improve various areas in business where data is relevant and available. It is flexible for marketing as well, i.e., information is used to test whether or not overtime is helping to scale up the business, to find out the optimum wage to pay the staff, or to trials for flexible working patterns.
Ultimately, the goal of data science is to help improve business results. That means being more productive, efficient, or testing various methods. These may be the results for reliable data, whereas, if the information is in large volumes, to handle it, we would need data science or the latest technologies related to it, which are automation and machine learning.
What can data science do?
In today’s world, companies that effectively use data science and analytics to optimize business outcomes will outperform companies that do not. Data science is capable of combining the best of problem-solving approaches with advanced AI/ML techniques to deliver tangible business impact to clients. It helps to maximize customer outcomes, optimize core business processes, and to build next-generation data and insights infrastructure. To encounter data science deliverables beyond expectations is the ability to do pattern recognition across industries and use that to develop innovative approaches to problem-solving. Data science-based platforms also drive the velocity of execution and deploy scalable solutions.
What is AI, ML, & DL?
Artificial intelligence (AI) is a broader concept of knowledge demonstrated by machines. It is mimicking the information or behavioral pattern of humans or any other living entity.
Machine learning is a subset of AI. It defines the systems or algorithms designed to learn structures to predict future outcomes. It is the technique by which a computer can learn from data over time without human intervention/using a complex set of different rules.
Deep learning is the subcategory of machine learning suitable for self-training algorithms and feature extraction. It is a technique to achieve complex AI by performing machine learning inspired by human’s brain and its network of neurons.
What is the difference between Machine learning and Artificial Intelligence?
Artificial intelligence is the concept of training the computer to perform activities that, at present, humans can do better, adding the capabilities to a machine that human contains.
Machine learning is an application of artificial intelligence (AI) that uses algorithmic models to identify patterns in datasets that analyze volumes of data and derive conclusions.
Artificial Intelligence is an ability to acquire and apply knowledge aiming to increase the chance of success, but not accuracy. It works like a computer program that does smart work whose goal is to simulate natural intelligence to solve a complex problem. AI, as a decision making leads to develop a system to mimic human, responding to behave in a certain manner in a circumstance. AI will go for finding the optimal solution and leads to intelligence or wisdom.
Machine learning is an acquisition of knowledge or skill aiming is to increase accuracy, not caring about success. Here, the machine takes data and learns from data on a particular task to maximize the performance of the device on that task. ML directs the system to learn new things from data involving in building self-learning algorithms. ML will not consider the optimal solution and go for the only solution, leading to knowledge.
How is data science related to AI/ML?
AI is a subset of data science. Practically, AI is a blend of theories and practices crafted within data science. AI is the goal for technology to mimic or improve upon the human process of cognitive thinking and decision making. Any application of artificial relies entirely upon data and a system’s ability to interpret this data.
Similarly, machine learning is a subset of AI. Hence, ML, as a technology, is a part of data science too.
What is marketing analytics?
Marketing analytics is the analysis of marketing data and various marketing factor representation that tells how marketing campaign fares and its effectiveness.
It is essential in digital marketing. With robust and objective data helping to inform the marketing decisions, marketing analytics can reap the benefits of increased visibility and directly targeted sales. Call tracking is a best-employed solution. Out of all marketing channels, these days online seems to be the best way, and most transactions are closed via the telephone.
Finding out what works with online marketing and how it affects telephone sales is an excellent source of information for any marketing agency or business that has an in-house marketing team. When finding a resource that bridges the gap between online and offline, a call tracking solution is one of the best treasures of information.
What does marketing analytics do?
Marketing analytics optimizes the company’s return on investment through the analysis of marketing data. Marketing analytics has a wide range of jobs in today’s world with a career path that can lead the marketing analytics function of a company.
Marketers, along with your long task list, are conversant with every new marketing medium that is introduced. But, knowing the efforts that are making a difference is where it all starts. Back up for every activity with facts and figures is feasible. Marketing analytics reports each action a visitor takes on digital properties or even on social media. It helps you understand the marketing activities that are bringing in revenue. An accurate map of the marketing analytics reveals an approachable practice and is essential for growing a business of any size.
There are five Marketing Analytics that Help business:
- Understanding the target audience in more significant detail
- Identify where competitors are investing their efforts
- Measure the performance of marketing campaigns
- Monitoring current trends and predicting future trends
- Use of data to decide the next course of action
Why is marketing analytics important?
Marketing analytics allows an understanding of trends by focusing on every single detail, including customer and market trends, that is of real importance in today’s time. Aids by providing a clear picture of the efforts and the returns, it allows us to easily depict that which programs worked with the reasons why it failed or even succeeded.
The market study is an integral part of the business. Marketing analytics allows monitoring of trends over time and understanding the return on investment by providing a clear picture of the working and the reports of each program. By efficiently helping to study the market trends, marketing analytics facilitates to forecast future results proficiently. The one with data analysis skills, marketing, and product sense could work in marketing analytics.
What is the customer lifecycle?
Consumer appeal is probably a significant factor of success in business, and it is essential to dedicate ample time and resources to ensure to be equipped with the right knowledge and technological arsenal to meet the demand in the market. Often, It is said that successful businesses have the most loyal customers.
The customer lifecycle describes the different stages a consumer goes through before, during, and after they complete a transaction. The phases a customer goes through while maintaining a relationship with the brand varies for different cases. The five primary stages of a customer lifecycle are:
- Reach: the product needs to be in places where the consumer will find it. (to develops awareness)
- Acquire: Reaching out to potential customers without offering a suitable product is a waste. (direct contact with the right customers improves customer retention)
- Develop/nurture: After the first purchase is made, the business needs to keep in touch with the customer by developing a relationship with the buyer. (customer feedback)
- Retention: when customer communication is held high, by texting about business to the customer, the chances of them returning and make another purchase are more elevated. (satisfying customer needs and retaining them)
- Advocacy: Making customers a brand advocate for the business. Their satisfaction results – they recommend products or services to friends and family. (Spreading awareness)
The customer lifecycle is a nonlinear, cyclical pattern where customer retention is the end goal in achieving strong brand loyalty. Yet, the business needs to continue to offer relevant and timely messaging to prior customers; else, the top-of-mind awareness will quickly fade. The customer lifecycle helps the company to maximize revenue potential for its client, who purchases the website. When a customer has become a brand advocate, the potential for upselling rises as a result. Another great way to progress consumers through the lifecycle is by launching new product features, releases, or exclusive offers.
What is customer lifecycle marketing?
Customer lifecycle marketing is the study of the whole customer journey in an integrated way, i.e., from the first contact to customer relationship management rather than narrowly focusing on a given channel, campaign, or metric. Look at the customer’s entire journey and use an always-on approach to the market instead of focusing on separate campaigns across all devices and channels, optimizing the messaging to align with the various touchpoints along that journey. Since customer lifecycle marketing works across platforms, it is the key for different teams that run their aspects of marketing. These aspects collaborate and pull everything in the same direction when applying a customer lifecycle marketing strategy for everyone to be on the same page and understand the entailing of customer lifecycle marketing.
What is customer lifecycle management?
Customer lifecycle management is the process of assigning various stages of the customer lifecycle to different measurable metrics. All companies have the opportunity to control and guide the customer journey. Customers follow a set of steps when it comes to choosing a brand’s product or service and brand loyalty. This can be used to put out the kinds of content for which consumers are already searching. Providing them with value, we can prove to customers that we are a reputable, transparent brand that has visitors’ and customers’ best intentions at heart.
The customer lifecycle outlines some steps taken by a customer as they progress through the sales and marketing funnel. It gives marketing, sales, and customer service teams a complete picture of the customer’s journey and highlights areas for improvement.
- Discovery – customer searching for a product
- Education – addressing customer’s queries and satisfy the customer’s needs
- Purchase – customer making a purchasing decision
- Post-Purchase Engagement – customer experience
- Advocacy – the importance of customer recognized
What is the importance of customer lifecycle management?
Customer lifecycle management is more than just a buzzphrase in B2B sales and marketing. Businesses feel that it allows them to gain valuable insight into their customers’ state of mind, except for only the purchase decisions that might be on their radar. Customer lifecycle management is a practice that can make or break the ability to upsell and cross-sell. It also helps to grow an existing customer relationship to helps companies cultivate brand loyalty by spotting opportunities to add value to the customer equation at some critical points in time.
It is easier to expand the relationship with an existing customer than to land a new one. While communicating with customers, marketers know that the message is more likely to be heard if it is in sync with the product and service needs. On the same note, customers tend to block out cookie-cutter sales and marketing messages that are not close in addressing the points of pain facing by customers. Hence, it is a waste of time if there is no valuable information conveyed in emails and other customer communications.
Today, most successful marketing-driven companies have the key to successful marketing by taking a more proactive and personalized approach to reaching out to customers at the right time, for the right reason, and with the right message. That is where product and service customer lifecycle management comes in, and it is essential to take the time to analyze various data sources that exist within the organization, i.e., from CRM platforms and order the processing systems to ERP and discover what top of mind for the customers is.
Customer Lifetime value
What is customer lifetime value?
Customer lifetime value is an important metric to measure at a growing company. Measuring CLTV with the cost of customer acquisition, companies can measure the time to regain the investment required to earn a new customer like the cost of sales and marketing.
Customer Lifetime represents the customer’s value to the company over a while. It is the metric that indicates the total revenue that a business can expect from a single customer. It is considering a customer’s revenue value and comparing that number to the company’s predicted customer lifespan. Businesses use this metric to identify relevant customer segments that are the most valuable to the company.
The formula gives the calculation of Customer Lifetime Value model for the company:
(average annual customer profit) * (average duration of customer retention)
CLTV tells companies about their revenue expectations from one customer to generate throughout the business relationship. The lifetime value is higher if the customer continues to purchase from a company for a long time. By this, the customer support and success teams have direct influence during the customer’s journey. Customer support representatives and customer success managers play vital roles in solving problems and offering recommendations that influence customers to churn or stay loyal to a company.
How to calculate customer lifetime value?
The calculation of customer lifetime value includes – calculation of average purchase value, that multiply by the average purchase frequency rate, which determines customer value. The calculated average customer lifespan is multiplied by customer value to get customer lifetime value.
Customer Lifetime calculations give value Model in the following steps:
- Average purchase value: (Company’s total revenue)/ (number of orders)
- Average purchase frequency rate: (name of purchases)/ (number of unique customers)
- Customer value: (Average purchase value)/ (average purchase frequency rate)
- Average customer lifespan: Averaging the number of years a customer continues purchasing from the company, i.e. (sum of customer lifespans)/ (number of customers)
- Calculate CLTV: (Customer value)* (average customer lifespan).
This calculation gives the expected revenue by an average customer to generate for the company over the course of their relationship.
What is the importance of customer lifetime value?
Customers are worth more than just the amount of money they spend on businesses today, i.e., they have future value if the company can retain them as customers. Higher the CLTV measurement, the greater the profits. One has to spend money to acquire new customers and to retain existing ones, but the former costs five times as much. Once the businessman knows his/her customer lifetime value, they use it to improve it. Working on retaining the existing customers through email marketing, SMS marketing, social media marketing, and other methods are feasible to increase the CLTV number. Wanting to gain new customers, does not mean to forget about the old ones.
The Value of Knowing own CLV:
Calculation of CLV for different customers helps in many ways, regarding business decision-making. By knowing the CLV, we can determine and significantly boost the business profitability
- The amount spent to acquire a similar customer and yet have a profitable relationship
- The kinds of products that customers with the strongest CLV desire for
- The products which have the highest profitability
- The most beneficial types of clients
What is the use of customer lifetime value?
The optimal use of customer lifetime value can be obtained by:
- Having all the customer data in one accessible place
- Predictive analytics capability
- The main dashboard for communicating with customers on all channels
- Personnel can monitor the data streams and communication
There other numerous actionable uses for the metric besides these. CLV can be effectively used to improve customer acquisition and customer retention, prevent churn, help to plan a marketing budget, measure the performance of the ads in more detail
Here are a few other ways to use CLV:
- Acquiring Higher Valued Customers (to know how much to spend on acquisition)
- Securing Future VIPs (use predictive analytics to get a firm idea of which new customers will likely be future VIPs)
- Practicing Value-Tier Segmentation (how much of the total revenue is provided by just this 5% of your customer base?)
- Preventing churn (send the right offer to the right person at the right time)
- Find Your Weak Point – Then Strengthen it (able to see which area you most need to invest your time and money into)
- Plan Your Yearly Advertising Budget (determine how much is the need to spend on advertising much more precisely)
- Measure Ad Performance (get the full picture of which ads are most effective for the business)
What Is Omnichannel Marketing?
Omnichannel marketing is described as a cross-channel marketing discipline that aligns the delivery of content across various marketing channels to provide consistent and seamless content experiences across the buyer’s journey and beyond. It addresses the fact that the modern customer is no longer confined to a single platform or channel for interaction with a brand by delivering a unified brand experience to an existing customer or potential buyer. Omnichannel marketing also focuses on providing a consistent shopping experience across various marketing avenues, which is highly personalized based on the unique tastes and preferences of each user.
Omni-channel marketing campaign revolves around six significant metrics:
- Customer centrism
- Target audience segmentation
- Customer data
- Tools and technology
- Metrics and analytics
What is omnichannel campaign management?
Omni-channel Campaign Management is a unified database marketing solution for planning, monitoring, and executing permission-based omnichannel marketing campaigns across various touchpoints. It enables nurturing the leads to becoming customers. It builds brand loyalty with a scalable, comprehensive, and easy-to-use solution that allows taking action from any device with real-time results.
The deliverables of Omni-Channel Campaign Management are:
- Advanced campaign workflows
- Campaign segmentation tools
- Sophisticated email marketing
- Precise customer targeting
- Powerful customer insights
What is risk analytics?
Risk analytics is the study of the underlying uncertainty of a given course of action. It often works in tandem with forecasting professionals to minimize future adverse unforeseen effects. The possibility of forecasting future cash flows streams, statistical analysis to determine the probability of a project’s success or failure, the variance of portfolio/stock returns, and possible future economic states, these all fall under risk analytics.
These risks can be mitigated by clearly defining, understanding, and managing tolerance for and exposure to risk. The advanced analytics capabilities enable more unobstructed visibility into the challenges related to managing the types of uncertainty in key areas such as operations, supply chain, regulatory compliance, finance, credit, and eCommerce. Use of analytics to quantify, measure, and predict risk, leaders can rely less on intuition and create a consistent methodology that is steeped in data-driven insights.
Proper risk analysis often includes mathematical and statistical software programs. In the information technology industry, a risk analysis is used to align technology-related objectives with the company’s business objectives. A risk analysis report can be either quantitative or qualitative. The application of qualitative or quantitative techniques reduces the uncertainty of the outcomes and associated costs, liabilities, or losses.
What are the risk analytics steps?
Risk analysis there to protect and conduct properly, and it is the real value added to the projects. The reason to utilize it is to remove or reduce the risks which threaten the success of the project. There are four steps of risk analysis. They are:
1. Risk Identification
The right people who can aid us in this are technical experts, customers, and project managers. Then, it needs to use a mix of one on one meetings, brainstorming meetings, and review of previous project risk and issue registers. During this step, it is better to categorize the risks into specific buckets such as safety, technical, commercial, and so on
2. Risk Analysis
This step is to analyze the categorized levels and get a detailed understanding of what its occurrence would mean. It requires to ensure that all the right people are present to conduct this in a meaningful way
3. Risk Response Plan
A plan built to either reduce the impact or eliminate the opportunity for the risk to occur. This is a realm of managing risks and developing a risk response. Mainly, four strategies are used in this step – the 4 T’s:
- Terminate (Avoiding the risk)
- Transfer (danger passed to another party)
- Treat (Mitigating the risk)
- Tolerate (benefits with the risk)
4. Risk Monitoring and Control
This step requires the project manager to diligently monitor the status of the risk and residue score by reassessing risk at critical junctures with the risk state, whether it is static increasing or declining. This depends on the trend, and significance may need a renewed effort by the project team to ensure that identified risks are dealt with appropriately.
Operational risk management
What is operational risk management?
Operational Risk Management is a methodology designed for the organizations looking to put the real oversight and strategy in place when it comes to managing risks. Each business that faces circumstances or fundamental changes in their situation can be seen as presenting varying levels of risk, evolving from minor inconveniences to potentially putting its very existence in jeopardy.
The risks need to be managed with a sophisticated approach to risk management. Hence there would be more chance that the business would thrive and grow. Operational Risk Management benefits the company in the following ways:
- Improve the reliability of the business operations
- Improve the effectiveness of the risk management operations
- Strengthen the risk involving decision-making process
- Reduction in losses due to poorly-identified risks
- Early identification of unlawful activities
- Lower compliance costs
- Reduction in potential damage from future risks
Marketing mix modeling
What is marketing mix modeling?
Marketing mix modeling is a technique that helps to quantify the impact of many marketing inputs on market share or sales. Its usage purpose is to understand how much to spend on each marketing input, how various marketing activities drive the business metrics of a product, and how much each marketing input contributes to sales. It is also used to optimize the budget of spending over different marketing inputs.
Brands used the marketing mix model as a decision-making tool to estimate the effectiveness of various marketing initiatives in maximizing Return on Investment (RoI). An accurate marketing mix model can be defined by the difference between the success or failure of a product. Marketing Mix modeling offers essential benefits that include better allocation of marketing budgets, better execution of ad campaigns, and business scenario testing.
What is customer propensity modeling?
Customer propensity modeling necessarily correlates customer characteristics with anticipated behaviors/propensities. It tracks buying habits and other actions such as a customer’s propensity to open a marketing email, participate in feedback surveys, or sign up for a loyalty program. The quality of customer data and the effectivity of segmentation is provided by sign up to a loyalty program. To give an example of a national retailer with physical stores and online channels; There are three customer segments defined by their spending and shopping frequency. They are frequent shoppers, slow-and-steady customers, and at-risk customers.
The application of propensity modeling predictive tool to each of these customer segments will allow developing a successful, long-term sales strategy which response to growth opportunities with proactive and timely upselling and cross-selling campaigns.
What is a propensity model?
Propensity modeling attempts to predict the likelihood that visitors, leads, and customers will perform individual actions. It is a statistical approach that accounts for all the independent and confounding variables that affect the particular behavior. The properties of a useful propensity model are:
- Evolve with trends
- Data pipeline and feedback
- Deliver predictions
- Evaluate performance
- 3. Demonstrate ROI
- Optimize sales funnel
- Drive efficiency over time
- 4. Scalable
- Adapt to large data sets
- Process data in real-time
For instance, propensity modeling can help a marketing team predicting its likelihood that a lead will convert to a customer, a customer will churn, or even an email recipient might unsubscribe. Here, the propensity score is the probability that the visitor, lead, or customer will perform a specific action.
What is a propensity model in marketing?
Propensity Models trace the past behaviors of the customers to make predictions about them. This method is different and complementary to segmentation. In segmenting, the cluster customers are based on shared traits or behaviors. In marketing, propensity modeling takes a step ahead of segmentation, focusing on likely behavior or action. Segmentation provides insight into customer behavior, while propensity modeling provides foresight. It allows targeting customers based on their same reaction, opposite to past practice.
There are three main types of models:
- Propensity to Buy model – looks at customers who are ready to purchase and those who need a little more incentive to complete the purchase.
- Propensity to Churn model – looks for at-risk customers
- Propensity to Unsubscribe model – seems for the customers who have been over-saturated by the marketing efforts and are on the verge of unsubscribing
Supply chain analytics
What is supply chain analytics?
Supply chain analytics is viewed as an application of statistics, mathematics, predictive modeling, and machine-learning techniques to find meaningful patterns and knowledge for shipment and transactional and sensor data. Supply Chain Analytics improves operational efficiency and effectiveness by enabling data-driven decisions at strategic, operational, and tactical levels. With the help of it, all the data that is produced by the different arms of a supply chain starts making sense. This data is collected and displayed in a visually digestible manner, in the form of graphs or charts, and is used to plan and make decisions sequentially. Supply Chain Analytics brings data-driven intelligence to the business, reducing the overall cost to serve and improving service levels.
Even after the evolution of supply chain technology, the commonly used system for supply chain planning is a spreadsheet. Companies cannot model the trade-offs of growth, profitability, supply chain cycles effectively, such as business operations complexity on a spreadsheet, and procure to pay and inventory turns. As that complexity increases, most companies are not able to use supply chain analytics to improve operating margin and inventory cycles.
The supply chain is the best place to use analytic tools in case of competitive advantage, because of its complexity and prominent role that supply chain plays in a company’s cost structure and profitability.
Why is supply chain analytics used?
Relying on traditional supply chain execution systems has become increasingly more complicated, with a mix of global operating systems, pricing pressures, and ever building customer expectations. Some recent economic impacts such as rising fuel costs, the global recession, supplier bases have shrunk or moved off-shore, and the increased competition from low-cost outsourcers. All these challenges potentially create waste in the supply chain, and that is where data analytics comes in.
Supply chain analytics has an important goal, which is to improve forecasting and efficiency and be more responsive to customer needs. For instance, predictive analytics on point-of-sale terminal data stored in a demand signal repository will help a business anticipate consumer demand, which in turn leads to cost-saving adjustments to inventory and faster delivery.
Supply chain analytics helps an organization make quicker smarter, and more efficient decisions with benefits include the ability to gain a significant return on investment, better understand risks, increase accuracy in planning, achieve the lean supply chain, and prepare for the future.
What is supply chain data analytics?
Supply chain analytics uses data and quantitative methods to improve decision making for all activities across the supply chain. In particular, it does two new things. First, it expands the dataset for analysis beyond the traditional internal data held on Enterprise Resource Planning (ERP) and supply chain management (SCM) systems. Second, it applies powerful statistical methods to both new and existing data sources. This creates new insights that help improve supply chain decision-making, all the way from the improvement of front-line operations to strategic choices, such as the selection of the right supply chain operating models.
Big Data is gaining prominence across the global Supply Chain and Logistics Industry, which is a transaction-based process and generates a large amount of data every day. The massive deployment of connected devices such as mobile devices, trucks, RFID readers, sensor networks, and webcams adds a vast volume of independent data sources. Companies already own a lot of information. Supply chain data analytics helps companies to achieve the skill set attitude, and technology required to become a data factory and create additional value from their information resources. Big Data can deliver the digital transformation of the Supply Chain business across the operational excellence and customer experience.
Credit risk modeling
What is credit risk modeling?
Credit risk is the chance that a borrower will be unable to make his/her payments on time and default on the debt. It refers to the risk that a lender may not receive his/her interest due or the principal lent on time. Credit risk modeling is how lenders understand how likely a particular loan is to get repaid. It is a tool to understand the credit risk of a borrower. This is important because the credit risk profile keeps changing with time and circumstances. As a result, there is an interruption in the cash flows of the lender and maximizes the cost of collection. In extreme cases, some parts of the loan or the entire loan may have to be written off, resulting in a loss for the lender.
Credit risk modelling takes the key factors in some decisions, including, payment history, Debt service Coverage, EBITDA, management experience, Liquidity, years in business, Leverage ration and assign a weight to each of the factors and then score each factor based on the type of credit you are granting.
What is a credit risk model validation?
The model validation is used to assess if a model is performing in the way it is intended and acceptable for use. Credit risk rating models means comparing the risk ratings given by the model with actual outcomes. However, abiding by the fact that no model will ever be perfect, the goal is to limit the underestimation or overestimation of the likelihood of default.
In case, a model predicts a lower risk of default than it occurs, the bank risks loss of principal, interest, and fees, and higher recovery costs with an overstatement of the fair value of the portfolio. Other than that, predicting a higher risk of default could lead to loss of potential profits, noncompetitive bidding, and an understatement of fair value.
The activities of the model validation process should be designed to understand the accuracy and appropriately capture borrower risk. The validation process should be applied to all models, whether internally developed or purchased from a third-party provider. A practical model validation program is generally demonstrated by some essential characteristics that include – independence, comprehensive, and ongoing.
What is meant by the implementation of agile methodology?
Agile implementation is a form of project management that works in small increments and well suited to projects that could become irrelevant once delivered, especially useful in software development. The key to the flexible plan is that it provides flexibility for changes to the product as it continues to be developed.
There should typically be at least four roles to the agile team:
- Developers – responsible for the actual technical design, programming, and testing
- Product manager – the subject matter expert who monitors the expectations of the stakeholders
- Project facilitator (often called a ‘scrum master’) – works with the development team to keep a consistent flow and hold to deadlines
- Stakeholders- internal or external customers who are nonparticipating yet impacted by the final solution.
Communication among the roles is often daily, excluding the stakeholders, where it may only meet at the end of each sprint release. It is helpful to have representatives in the team experienced with agile implementation.
What is advanced analytics?
Advanced Analytics refers to the autonomous or semi-autonomous examination of content or data using sophisticated techniques and tools, that is typically beyond those of traditional business intelligence (BI), to make predictions, discover more in-depth insights, or generate recommendations. It is an umbrella term for several sub-fields of analytics that work together in its predictive capabilities
Advanced analytics is a part of data science that uses high-level tools and methods to focus on projecting future trends, events, and behaviors. By this, organizations gain the ability to perform advanced statistical models such as various future-proof aspects, as well as ‘what-if’ calculations of their operations.
Digital and analytics
Why is digital analytics important?
Digital analytics is the qualitative and quantitative data analysis of a business and the competition to drive continuous improvement of the experience that the potential customers have, which translates to desired outcomes, both online and offline. It encompasses the collection, analysis, measurement, visualization, and interpretation of digital data briefing about user behavior on mobile sites, websites, and mobile applications.
Digital intelligence is an essential component of digital analytics that enables brands and website owners to understand the usage of their sites and apps. Using digital analytics data, companies can optimize the customer experience on their websites, mobile sites, and mobile apps, and also maximize their marketing ROI, content offerings, and overall business performance.
Digital analytics is the process of analyzing digital data from different sources like mobile applications, websites, among others. It gives a clear vision to the organization on the behavior of users/customers. Digital analytics provides companies insight into the areas of improvement. Organizations use digital analytics as a tool for collecting, measuring, and analyzing qualitative and quantitative data. It helps companies to provide a better online experience to their clients and potential customers, which gradually results in the achievement of desired goals.
What is an ML model?
A machine learning model is generated by providing training data to a machine learning algorithm for the model to learn from it and can further process additional data to make predictions. Different types of models have been used and researched for machine learning systems. It can be a mathematical representation of a real-world process.
What is a machine learning model deployment?
Deployment is the method to integrate a machine learning model into an existing production environment and make practical business decisions based on data. This is one of the cumbersome and the last stages in the machine learning life cycle. Often, an organization’s IT systems are not compatible with traditional model-building languages, forcing data scientists and programmers to spend brainpower and valuable time rewriting them.