Customer data analysis, is the process of collecting and analyzing customer data to gain insights on customer behavior. A recent McKinsey survey found that companies that extensively use customer analytics are reporting +115% higher ROI and +93% higher profits. They also have +112% Sales growth and +115% higher ROI.
Market analysis is a detailed assessment of your business’s target market and competitive landscape within a specific industry.
It includes quantitative data such as the actual size of the market you want to serve, prices consumers are willing to pay, and revenue projections, as well as qualitative data such as consumers’ values, desires, and buying motives.
Following are some benefits of market analysis:
The study of economic systems is known as economic analysis. The goal of the analysis is to identify how well the economy or a component of it is performing.
It could also be applied at a smaller level. For example, a construction firm may explore new construction, renovation of an existing facility, or leasing another building to meet the need for additional office space. The assessment is based on a cost-benefit analysis of discounted costs and benefits over a set period of time. The ratio of total benefits to total costs (benefit-cost ratio) or, equivalently, the total net benefits, can be used to describe alternatives (net present value).
Portfolio risk management strategy is required when risk management is required at the overall enterprise level or at level of a portfolio of many projects. The process of portfolio risk management is as below:
Portfolio risk management, however, does not mean avoiding risky projects entirely to ensure success. It just means how to optimize the the risks. Organizations that proactively manage portfolio risk are better equipped to take on more risk, increase portfolio value, and have a higher rate of successful project delivery. Organizations that ignore portfolio risk management will sub-optimize their project delivery and potentially jeopardize high priority projects. For example during COVID a lot of organizations, who ignored risk management, were wiped out.
What separates the successful lenders from failures? It is its ability to identify credit-worthy borrowers. Today, analysis of almost all kinds of data, from transaction data to social media data, can be used for deciding credit-worthiness. Data analytics can help lenders in five ways:
WHAT?: In continuously changing financial landscape, analysis of intrinsic value of a company is useful to big investment houses and also for other companies (E.g. during merger and acquisitions).
Company analysis refers to the process of evaluating a company’s profitability, profile and products or services. It is also known as “fundamental analysis”. It often also incorporates basic company information, such as the mission statement, goals and values.
HOW?: Company Analysis involves reviewing the history of a company and the events that contributed to shaping the firm. Moreover, it looks into the company’s goods and services. Company analysis studies the products manufactured by the company and analyzes the quality and demand for these products. If the firm is in the service sector, the investor reviews the services offered to the related market.
To evaluate a company, core elements, operations and functions are analyzed. The reports from the analysis of various aspects of the firm put together the big picture of its corporate quality.
Analysts use the SWOT (Strength, Weakness, Opportunity, Threat) approach to determine a firm’s current and probable future position in its respective industry. Although the process was earlier done manually, recently data-driven organizations, armed with latest predictive models and AI have emerged who specialize in the process.
Following are three main applications of real-time data analysis in financial sector.
(1)Money laundering detection and payment fraud detection: Money laundering and Payment Frauds cause significant damage to the respective bank’s corporate image. The corporate identity of a bank is a reflection on its credibility.
Streaming analytics offers comprehensive, real-time anomaly detection mechanisms to help banks and financial institutions to safeguard themselves from fraudulent activities.
With streaming analytics, banks can easily convert their domain knowledge regarding fraudulent behavior to real time rules, use Markov modelling to detect unknown abnormal behavior, and use scoring functions to reduce the number of false alarms. Markov models are generally used to model randomly changing systems, and in the case of fraud detection, it helps to identify rare transaction sequences. This is especially useful in identifying complex fraudulent activity carried out not as one transaction but broken down into a series of smaller transactions by experienced crime rings.
ML enables computers to learn behavioral patterns on their own by referring to large amounts of past data without being explicitly programmed. Algorithms such as Clustering help a computer program to model ‘normal’ behavior by looking at past transaction trends. Therefore, this helps banks to identify new types of fraud by looking for transactions that differ from the normal behavior that the machine learning algorithm has modeled.
(2)Risk Management: In rapidly changing capital markets, it is no longer adequate to measure risk as an end of day process. Trading decisions can significantly alter exposures in a millisecond as traders with exposures to Bear Stearns found out the hard way in March 2008.
In order to assess risks to market portfolios and take corrective measures in real-time, capital markets are now moving towards intraday value at risk computations. With streaming analytics, banks can obtain a low latency, high-performance solution that listens to market prices as well as real-time changes to portfolios and compute value at risk on the fly thus minimizing and managing the risk.
(3)Stock Market Surveillance: Unethical profit gain via artificially inflating or deflating stock prices, exploiting prior knowledge of company proceedings, advance knowledge of impending orders, and insider trading are common forms of stock market manipulation. And to prevent these, a stock exchange can incorporate streaming analytics into their overall surveillance efforts.
Streaming analytics can spot even the mildest form of market manipulation, in real time. Even though stock market manipulation is illegal, identifying suspicious behavior is often rendered cumbersome or impractical due to the volume and velocity at which trading is executed. Thus, a majority of illegal trading activities are not captured as and when they occur.
By joining market data feeds with external data streams, such as company announcements, news feeds, Twitter streams, etc., streaming analytics can instantly identify activities that are possible attempts of market manipulation. By doing so, regulators can be alerted in real time so they can take early action, even before the manipulation takes place.
In today's economy everyone faces the problem of finding the optimal balance between consumption and savings or investments. This problem can be solved by two approaches:
Workflow of decision making for Robo-adviser services is shown below
Some such famous services are: ‘Wealthfront’, ‘Betterment’, ‘WiseBanyan’,'Charles Schwab', 'FutureAdvisor', 'Bloom', 'Vanguard' etc. According to one estimate, they managed assets between USD 2.2 trillion and USD 3.7 trillion in 2020, which,by the year 2025, is expected to rise to over USD 16.0 trillion assets under management. The research indicates that robo-advisory is more suitable for individual investors than institutional investors.
Data science models (mainly Time-series models) based on sound math and research are increasing being used in stock-market to examine past behaviors with the goal of forecasting future outcomes.
A time series is data, which in this case refers to the value of a stock, that is indexed over a period of time. This period of time could be divided hourly, daily, monthly, or even by the minute.
A time series model is created by using ML and/or DL to accumulate the price data. The data needs to be analyzed and then fitted to match the model. This is what makes it possible to predict future stock prices over a set timetable.
A second type of modeling that is used in ML is ‘classification model’. These models are given ratios like P/E, P/BV, Debt/Equity, ROCE, ROE etc. and then they strive to classify or predict if these ratios indicate a sound investment or not.
Price optimization is the practice of analyzing the data of an item’s price points to determine the best price to maximize revenue or profit. In today's competitive and dynamically changing business-environment, every business must adopt the practice to survive and thrive.
Revenue and pricing optimization analysts perform similar duties(even though both roles examine different sets of data).
A revenue analyst job description
Combine program and department budgets into a consolidated budget and estimate future financial needs |
Explain funding requests to stakeholders. |
Help top managers analyze plans and find alternatives if the projected results are unsatisfactory. |
Inform program managers of the status and availability of funds. |
Monitor organizational spending to ensure that it is within budget. |
Review budget proposals and funding requests from managers for completeness, accuracy, and compliance with laws and other regulations |
Work with project managers to develop organization’s budget. |
A pricing analyst job description
Analyze data using statistical software. |
Convert complex data and findings into understandable graphs, tables, and written reports. |
Evaluate and find data collection methods such as opinion polls, questionnaires, and surveys. |
Gather data on consumers, competitors, and market conditions. |
Measure the effectiveness of marketing programs and strategies. |
Monitor and forecast marketing and sales trends. |
Prepare reports and present results to management and clients. |
