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From Editor’s Desk Editors- Subhadeep Chakraborty, Amir Hossain Hello fellow readers, The campus is buzzing with energetic newcomers while few at home, anxious for their chance to visit the campus. With the summer placement season kicking in, it’s definitely a good time to brush up your basics while also focus on some insightful content that should give you an edge over the rest in the race to be industry ready. This edition as usual covers the relevant topics covering the basics of finance applied to some niche sectors of the market. Additionally, this time we have come up with an interesting read on “Treynor-Black Model” which educates on how you can take advantage of good information to benefit from larger risk-adjusted returns than Capital Asset Pricing Model. For all the trading enthusiasts, who wish to gain a fair understanding of the stock market can read the article “Who said market is Overvalued?” where it talks about an episode when Sensex and Nifty touched alltime high records, delighting bull investors and making it a landmark day for the Capital Market. Learn about how economic surveys provide a signal on what is really happening in the private sector economy, by tracking key variables through the article “Purchase Managers’ Index (PMI)”. This article talks about how PMI is calculated and the importance of PMI in economic decisions. We also have an article on FDI inflows in India, its effect and potential, which talks about the different models that are instrumental in the growth of an economy. If you are curious to explore the psychology behind Financial Decisions, do checkout the article “Behavioural Finance: Psychology Behind Financial Decisions”. This article provides an overview of how financial decisions made by an individual are driven by the emotions, habits, biases and influence of others. While covering the topics around the world in the Sofia Times, we have not left the occasion of starting our blog for finance snippets bi-monthly. SOFIA is a firm believer of imparting finance knowledge combined with fun. Finance is not all about credit and debit, it’s more about drawing inferences of credit and debit and having a valuable insight of the business. Driven by Society of Finance, this edition is not only for finance enthusiasts, but also for the people who would like to keep themselves updated with the current happenings in the business world. As usual we hope that this edition will enrich your knowledge benefit in your academic and career decision. Happy Reading!
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PURCHASE MANAGERS’ INDEX (PMI) WHAT IS PMI? PMI is a series of monthly economic surveys of carefully selected companies. They provide an advanced signal of what is really happening in the private sector economy, by tracking variables such as output, new orders, employment and prices across key sectors. For India this data is published by IHS Markit Limited. It covers the manufacturing and services sector of the Indian economy and provide 3 types of indices: 1. Manufacturing Purchase Managers’ Index – It is based upon the growth variables (subindices) related to the manufacturing sector of the economy. 2. Services Purchase Managers’ Index – It is based upon the growth variables (sub-indices) related to the services sector of the economy. 3. Composite Purchase Managers’ Index – It is basically a weighted average of the manufacturing and services PMI variables. The weights are provided basis the percentage of contribution in the nations’ GDP by the sector. HOW PMI IS CALCULATED? Calculating PMI of a sector (Manufacturing or Services) is a 4-step process shown below: 1. Panel Building and Management – To calculate PMI of a sector, a panel of members is recruited to accurately represent the underlying structure of the sector as provided by the Gross Value Added and company size data by the respective authorities of a country. 2. Data Collection – The members recruited are the purchase managers or people at responsible positions at any of the organisations within the sector. Data through a qualitative questionnaire is collected on a monthly basis from the panel members. The questions can be as simple as: “Is the level of output at your unit (in volume terms) is higher, same or lower than one month ago”? In response the panel members or the purchase mangers of the respective organizations have to answer in terms of: • • •
Yes, ‘It is higher than the previous month’ ‘It was same as the previous month’ ‘It was lower than the previous month’
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The panel members can also provide the reasons behind their response, however it is not mandatory to do so. 3. Data Calculation: The responses from the panel members are weighted according to the Gross Value Added and contribution in the sector by their respective companies. Based upon the responses the value of the sub-indices is calculated: Sub-Indices’ Value = (% reporting ‘UP’) * 1 + (0.5*(% reporting ‘THE SAME’)) + (0.0 *(% reporting ‘DOWN’))
4. Publishing the PMI – After calculating the index values of all sub-indices, they are weighted to calculate the PMI of the sector. PMI of a sector = Weighted Average of all the sub-indices in that sector. For example, the sub-indices of manufacturing sector and their weights are shown below: • • • • •
New orders – 30% weight applied Output – 25% weight applied Employment – 20% weight allowed Suppliers’ Delivery times – 15% weight applied Stock of Purchases – 10% weight applied.
HOW PMI IS INTERPRETED? The PMI of a sector is monthly published in points, which ranges from 0 to 100. If the score is 50, it is interpreted as no change in growth from the previous month levels. If the score is above 50, it is interpreted as an expansion in growth over previous month. If the score is below 50, it is interpreted as contraction of growth when compared to previous month.
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For example: if the PMI output sub-index is measured as 56 for January and 53 for February, it will mean that the output has expanded in both the months, but the rate of expansion of output was less in February when compared to the rate of expansion of output in January. SEASONAL ADJUSTMENT The PMI data published by HIS Markit is seasonally adjusted to accommodate all the seasonal fluctuations observed in an economy. These adjustments are made on the basis of historical data collected and observations made over the functioning of an economy by the publishing authority as well as the valuable feedback provided by the panel members or the purchase managers.
IMPORTANCE OF PMI IN INDIAN ECONOMY
India's Composite PMI over the years 70 60
50 40 30 20 10 0
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The composite PMI data in above graph clearly indicates the impact of COVID-19 over the manufacturing and services sector of the Indian economy. It reached as low as 7.2 in the month of April 20 when the first wave of coronavirus hit our country. Thereafter we have observed a VShaped recovery as soon as the global economies reopened. In Sept20 it once again touched the benchmark of 50 points and remined above it till April21, after which it slipped back to below 50 levels when 2nd wave impacted Indian Economy. IMPORTANCE OF PMI IN ECONOMIC DECISIONS PMI data is always published monthly and ahead of the economic data published by the responsible authorities of the nation. Trends have shown that the PMI data always moves hand in hand with the GDP growth rate of the country. Since PMI is published in advance of the official data it gives a clear picture of the growth estimated in the Manufacturing and Services sectors of the economy. In the country like India where the GDP growth rate is published quarterly, PMI helps economists and academicians track the progress of the sectors and country as a whole on monthly basis and take well informed economic decisions in the absence of official data.
Article submitted by- Sourabh Basandani (BIFS) Back to Table of contents
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Treynor-Black Model: An active strategy beating CAPM returns Treynor-Black Model aims to give larger risk-adjusted returns than Capital Asset Pricing Model (CAPM) returns by taking advantage of good information available in the market. But first, let’s do a recap of CAPM to get a better understanding of Treynor-Black Model. The CAPM pricing relationship is used to determine an expected fair (arbitrage-free) price of an asset in equilibrium under certain assumptions. These assumptions include investors being rational (i.e., they maximise their utility only on the basis of mean return and standard deviation of risk), unlimited amounts available to be loaned or borrowed at risk-free rate (Rf), perfect and frictionless markets, and investors having homogenous beliefs regarding future returns (information symmetry exists). For a given level of standard deviation of risk, the efficient set of returns is given by the Markowitz’s Portfolio Frontier (points on the downward concave curve), from which investors can pick a portfolio along the curve which maximises their utility (risk-averseness). However, an investor can construct a better set of optimal portfolios by holding risk-free asset (Rf) and the tangent passive market portfolio (Portfolio M consisting of risky assets which aims to replicate a market index) in such weight composition that it satisfies their utility, this is known as Two-Fund Separation and is the basis of CAPM. This linear relationship is plotted as Capital Market Line (CML) in a return-risk graph plane and under the given assumptions, it is the best possible set of returns an investor can earn (given a particular level of risk). According to CAPM, the expected fair return of a portfolio ‘P’ is given by E(Rp) = Rf + βp[E(Rm) - Rf]. So, the return has a market risk premium over and above the risk-free rate as compensation for investing in a risky asset, and the market sensitivity of this asset is captured by the beta, which is the asset’s risk with respect to the market risk [defined as βp = [Cov (Rp, Rm) / Var (Rp)]. CAPM’s stringent assumptions lead to choosing Beta as the risk factor instead of total risk variance as rational investors would always diversify the 8
idiosyncratic risk and get returns only based on the undiversifiable (systematic) risk. CAPM can be graphically depicted as Security Market Line (SML), where if return of an asset is not on the line, the asset is mispriced (valuation needs to be corrected). CAPM is widely used for pricing risky assets as investors set this best theoretical return (for that given level of risk) as the benchmark for other assets with same β. However, in reality we see how information asymmetry and imperfect markets give ample arbitrage opportunities and hence returns superior to that of CAPM returns. One popular measure to record the degree of mispricing that exists in markets is Jensen’s alpha, which is given by: αp = 𝑅̅ p - {Rf + βp[E(Rm)-Rf]}, where 𝑅̅ p is the historical estimated average return of portfolio ‘P’, and this is subtracted from CAPM return to check for mispricing. After identifying
the mispriced assets with positive alpha (undervalued), we can create an active portfolio. The total risk of any asset (σ2i) can be divided into its systematic/ market-specific risk (β2iσ2M) and idiosyncratic risk component (σ2εi). Since, investing solely in this active portfolio would not only give attractive returns, but also a higher idiosyncratic risk (loss of diversification benefits), Treynor-Black model’s objective is to incorporate this active portfolio into a diversified mixed portfolio to have a better risk-adjusted return valuation. To incorporate only the active portfolio benefits, we need to create a new diversified portfolio (consisting of this active portfolio ‘A’ and the passive market portfolio ‘M’) and assign weights such that this portfolio lies on Point Q (same level of risk as Portfolio M). When the investor mixes the risk-free asset with this diversified portfolio, it gives the best optimal portfolio (in which weights of Portfolio Q and risk-free asset 9
can be altered to maximise utility) as a new Capital Allocation Line (CAL) is formed (which is higher than the CML). Now, using this new mixed diversified portfolio, the investor earns superior riskadjusted returns, provided the investor is better experienced than the novice rational investors in identifying positive alpha assets. With the popularity of active mutual funds (funds managed by professional portfolio managers), novice investors can get a clearer picture about the expected return given the level of risk of the fund. Treynor-Black model renders CAPM unrealistic as CAPM has a strong market efficiency assumption. Although it is a better alternative, Treynor-Black model still suffers from some of CAPM deficiencies like Roll’s Critique (which states that the perfect market portfolio is unobservable in real world and hence different proxy indices are used). Treynor-Black model introduces its own disadvantages as well like ability of experienced analysts to successfully forecast the alpha value, and novice investors prohibiting their portfolio managers to short sell overpriced assets (i.e., borrowing overpriced asset to sell it off and then buying the asset back to return when the valuation corrects itself at a cheaper price) as it seems risky. Treynor-Black model is still far away from predicting asset returns accurately consistently when compared to other portfolio optimisation software, but as the world gets more experienced in financial markets, there is a need of a better parsimonious model than CAPM for return forecasting.
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Article submitted by -Ayush Patri (PGDM BIFS 2021-23)
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Behavioural Finance: Psychology Behind Financial Decisions Finance is a dynamic area and has evolved with time into different sub-areas and new concepts. Behavioural Finance is one such concept. Generally, finance revolves around formulae, tools, techniques and practical application of the same, whether it is a personal finance or business finance or corporate finance. But Behavioural finance is relatively a new school of thought that addresses and provides insight into the theories of human behaviour from the fields of psychology and decision-making to characterize some prevalent features of irrational behaviour in financial markets as well as providing new grounds based on which it questions conventional methods of modelling investor behaviour. What is Behavioural Finance? Behavioural finance studies how financial decisions of a person are driven by the emotions, habits, biases and influence of others. It focuses on the fact that investors are not always rational, have limits to their self-control, and are influenced by their own biases. It also seeks to provide explanation for people’s economic decisions. Understanding Investor Behaviour Traditionally, economics and finance have focused on models that assume rationality. It is not necessary that decisions made by the people are always rational. They may or may not be. All of us have innate psychological biases, pre-conceived notions or apprehensions that can lead to predictable “errors” in how we make important financial decisions. Some of the biases are easy to break once you know they exist while others are just so hard wired into our conscience that even though we know that they are there but we can’t break them. And that’s where behavioural finance comes into picture. What all are the biases? Which ones can we removed? What changes can we make in our behaviour, so the decisions that we taking are more optimal for us as investors. It catalogues those errors and helps us to anticipate, and hopefully avoid, these decision-making “traps.” Behavioural finance examines typical errors made by financial market participants as a result of behavioural biases, and examine the extent to which irrationality can affect financial markets at the aggregate level ("bubbles"), how long irrationality may persist, and what factors will eventually cause these bubbles to burst. So, what are those decision-making errors and biases? Some of the common biases revealed by Behavioural Finance are: 1. Self-attribution Bias Self-attribution defines the readiness of humans to make selection based on overconfidence in his/her understanding or expertise. When we mistakenly think we 11
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know more than we actually do, we tend to miss information that we need to make an informed decision. Confirmation Biases Confirmation bias is the inclination of people towards accepting information that confirms their belief and ignore information that contradicts it. We form our views first, and then spend the rest of the day looking for information that makes us look right. Herding Effect “Are you lemming?” is a popular term used for herding effect. It states that people foster the tendency to do things just because everyone else is doing them. And this irrational herd mentality led to a massive bubble formation. Anchoring Bias Anchoring bias occurs when people rely too much on pre-existing information or the first information they find when making decisions. A specific piece of information becomes the building block for investor’s crucial decision. Loss Aversion Loss aversion showcase the tendency of investors where they are so scared of losses that they always focus on trying to avoid a loss more so than on making gains. In research it has found that investors feel the pain of a loss more than twice as strongly as they feel the joy of making a profit.
Conclusion Because of the many flaws of accepted economic theory, behavioural finance serves as a good complement. The assumptions of perfectly rational individuals and perfect information seem to work in some situations. Behavioural finance then gives explanations as to why the market behaves as it does. People in the world of investments commonly talk about the role that greed and fear play in driving stock markets. Behavioural finance extends this analysis to the role of biases and emotions in decision making. By understanding how and when people diverge from rational expectations, behavioural finance bring forth a blueprint to help us make better, more rational decisions when it comes to financial matters. Behavioural finance is growing very fast, in explaining not only how people make financial decisions and how markets functions, but also how to improve them.
Article submitted by- Shraddha Gupta (PGDM BIFS: 20-22) Back to Table of contents
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Who said market is Overvalued? Indian Equity market reached a milestone when Both Sensex and Nifty created history by reaching 56,000 and 16,000 points on August 3, 2021. Both touched all-time high records, delighting bull investors and making it a landmark day for the Capital Market. This was all over the news, but in order to comprehend it, we must first grasp what Nifty and Sensex are. Sensex and Nifty are stock market indices. “A stock market index is a statistical representation of the market's real-time fluctuations”. A stock market index is formed by putting together comparable types of stocks from a market or exchange. The Bombay Stock Exchange's stock market index, known as the Sensex, stands for "Stock Exchange Sensitive Index." It calculates the BSE movement. The Nifty is the National Stock Exchange's index and stands for 'National Stock Exchange Fifty.'
This surge in the market was backed up by many strong performing stocks from the industry leaders including HDFC, TCS and Infosys. Companies like Britannia, UBL and Marico also contributed in the rise. Big pharma counters like Sun Pharma and Lupin are also trading extremely strongly that day, which added to the bullishness of the market.
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Fears about high valuations, other global cues linked to Covid, China's crackdown on IT and education enterprises, and concerns about the impact of the lockdown scenario on businesses have kept the Nifty and Sensex locked in a range for the past six to eight weeks.
What Exactly caused this rally? India’s vaccination pace along with the government support and a surge in foreign investment aided this increase in points. In the intraday session on August 3, robust purchasing in most sectors drove domestic stocks to new highs. Along with that, the Sensex reached a new all-time high of 53,887.98, while the Nifty reached a new high of 16,146.90. The BSE midcap and small cap indices, like the benchmarks, reached new highs of 23,443 and 27,232, respectively. The Sensex eventually closed at 53,823.36, up 873 points, or 1.65%, while the Nifty concluded at 16,130.75, up 246 points, or 1.55 percent The BSE Midcap index ended the day at 23,374, up 0.19 percent, while the BSE Small cap index closed at 27,134, up 0.23 percent. The week following this jump Nifty closed at around 16,700. Market experts believes that rally was also a result of improving Covid situation leading to better investor sentiments. Tech Mahindra Ltd. was the Nifty 50's best performer after the Indian software company's results for the three months ended June surpassed analysts' expectations, thanks to robust growth across all business verticals and contract wins. The company's $815 million in quarterly contract wins give it hope for double-digit revenue growth this fiscal year. On the other hand, Dr. Reddy's Laboratories Ltd., one of India's major drugmakers, became the country's worst-performing stock after a US investigation and disappointing first-quarter results.
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According to market sources, there was a significant increase in the purchase of Puts, which are derivative products used to make pessimistic wagers. Mid/small-caps and cross-sector equities, on the other hand, saw purchasing activity and appreciated. “The situation is such that many individuals are simply purchasing Puts at every high level when they should be looking towards 17,000 levels for the Nifty in less than two years. Puts worth between 5,000 and 10,000 crores were added on Tuesday. Markets are really rising as a result of this... Markets will continue to strengthen in terms of forward profits; therefore, the rise will continue,” a financial analyst opined. Nifty50 finished the week on a bullish note, one notch above its consolidation zone, having surpassed lifetime highs. For the time being, the general picture has turned optimistic, as the market has adopted a definite upward trajectory following some sideways consolidation. Thanks to an infrastructure boom, cash influx, and tech-driven supply chain efficiency, the Nifty managed to produce decadal high profits growth in FY21, which supported the rise. Given the level of deleveraging we're seeing and the cash that corporations are hoarding, there will be capex and reinvestment in the coming years, driving profits growth through future themes like ethanol blending, green energy, electric vehicles, and the like. Rather of waiting for a downturn, investors should focus on researching the wealth compounders of the future and investing in them whenever there is a drop.
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The stock market's value will ultimately be determined by GDP growth, liquidity, and the government's policy framework. All of the prerequisites for a stock market boom are currently in place. There are still companies like ITC, SBI, NTPC, and others that are appealing and have a low-risk element. However, some stocks may be purchased during this strong market, such as WIPRO - The stock has had a tremendous uptrend in the past, and it is continuing to rise steadily on the upward side. The higher top and lower bottom chart series point to a bullish continuation pattern that should continue in the next sessions, and the increase in volume activity over the previous several days suggests that the counter is bullish. TATA STEEL- The stock has been in a spectacular uptrend, consistently generating bullish continuation chart patterns; however, a brief break in the momentum is now seen owing to some profit booking; nonetheless, a powerful reversal from the support of the retracement zone is quite possible for additional upward. Regardless, while dealing in stocks, one should do their own analysis and make their own financial decisions.
Article submitted by - Apurva Agarwal (BIFS) Back to Table of contents
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FDI inflows in India, its effect and potential
Data Source: ThePrint There are mainly two way a country gets foreign investments, one by FDI and other FPI.FPI are Foreign Portfolio investments that are made into stocks in share markets, they are also called hot money as they are mostly invested for short term. FDI-foreign direct investments are on other hand done for long term. It is important to understand how a country can grow its economy. There are different models that different countries have implemented, take example of China which has grown it's economy by exports. They have brought the cost of goods down by applying economies of scale. They manufacture at large scale which helps them to lower the fixed cost and makes them more competitive. There is also some grey area in this model as we have seen these are state owned companies hugely subsidized by govt which gives them huge competition advantage. Then there is Saudi Arabia and some other middle Eastern countries that are totally dependent on export of crude oil. They are facing uncertain future because of new advances in battery technologies and cost of solar energy going down and raising concern of climate change. But for countries like India who neither have huge manufacturing base nor natural resources like oil to export, they can only grow their economy by using their human resources and constant innovation. For increasing manufacturing contribution to GDP, we will require huge 17
investments in infrastructure, HR and Research and development. Govt can spend on infrastructure but we have seen governments are quite inefficient when it comes to innovation. Govt structures are about maintaining status quo and innovation is about disruption, they demolish status quo. So, it's easy to say if Indian economy has to grow at higher pace, it needs private investments and that too foreign investments. Because we know foreign direct investment are most productive way of generating growth. India receives FDI in different sector be it in retail sector or technology sector. We have seen huge investment in Indian start-ups by companies like Tiger Global Management. Tiger global management has invested in some of most successful Indian start-ups like Zomato or Unacademy. Good thing about this investment is that they want more profits when they invest because risk associated with investing in start-ups are quite high so, only way to achieve this is to scale up the business, which is of extreme importance for India because we have such huge population that for everyone to have chance of using reasonably good services, everything has to be scaled up which results in reduction of costs and it becomes affordable for everyone. FDI is one of main instruments which can help India unlock its potential. We have already started to see this. Earlier this year google made a investment of $4.5 billion in Jio platform, this will be used to make affordable 4G android mobile phones which will increase mobile and internet penetrability in India. Currently India has 448.2 million mobile internet users with more affordable 4g phones this number is only going to go up. It can lead to drastic changes in education, healthcare facilities in remotest of areas. Especially for youth of rural areas, this will give them ways to compete and be on equal footing with others. Government also knows importance of FDI and they want to attract as much as they can. We have seen govt focussing on ease of doing business, regulatory clearance. They have increased and allowed FDI in Insurance sector from 49% to 74%, same has also been done in defence sector which was a big point of contentions earlier but now it is done. Government is trying for big infrastructure push and for that they are trying to get as much FDI as they can get. Over the past years we have seen huge FDI in IT sector which has led to its rapid growth of around 9% YOY. According to Mukesh Aghi, president of US India Strategic and Partnership Forum, if India wants to achieve $5 trillion dollar economy mark till 2025, they need to attract $100 billion worth FDI every year. Highest ever FDI that India received was $81 billion in FY21. We still need to increase this by 25% to reach mark of $100 billion, this is an uphill task. Most of this FDI will come from USA. In below graph we can see USA inflows over the years and GDP growth of India.
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Data Source: World Bank Govt will play critical role in this as foreign investor look for certainty in policies and political stability for long term investments. India has the advantage of political stability as this and previous Modi govt are majority govt and everybody knows who is calling the shots. But still, we have to go long way to go in policy making. They have not been able to implement policies, take example of farm laws which has huge potential to attract FDI, it has been put on hold because of protests and nobody knows whether it will ever be implemented. Look how govt is not able to meet targets of disinvestment. Disinvestment is more than just govt selling PSUs, it also helps to make market competitive as most PSUs are big monopolies. India has many things going for it like large young workforce, substantial number of English speakers who can interact with north America and European clients, huge potential as for many products market penetration is still very low. Most Indians are still to buy their first vehicle, first house so it's a huge opportunity for foreign players. India has one of the fastest growing middle class, India has scale going for it. It just needs proper policy making to attract FDI and faster growth will follow. We can see world acknowledging India’s potential, below graph show historical high FDI inflows during covid time.
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Data Source: The Print
Overall picture looks good for India but still lot of things are needed to be done in policy making side, structural reforms are needed in various sector for attracting more FDI so that we can achieve high growth figures and sustain it. Article submitted by - Ashutosh (PGDM 2023)
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Walmart: The Largest Online Retail store With the advancements in data analytics and data science in recent years, we have seen the amount of data available is exploding and the companies are handling copious amount of data with the help of big data and machine learning algorithms. The whole world has completely metamorphosed into a digital era and Walmart being the gargantuan retailer has utilised this opportunity to learn and understand the customer’s need and wants by deploying machine learning and artificial intelligence. As the coronavirus outbreak has disrupted the supply chain and ruptured the way the companies were operating previously, the world is moving rapidly and completely towards digitizing its functioning and capture the market by competing their rivals with better and more advanced technologies so as to satisfy the customer’s demand as the customer’s behaviour has profoundly changed towards online shopping experience. This paper is a move to gain knowledge about how the Walmart has taken the advantage of big data and artificial intelligence to flourish in the competitive world. 1. Factors responsible for these changes in recent years • Changes in customer’s expectations • Demand for speed and efficiency • Rise of the e-commerce giant Amazon • Sky rocketing innovation in technologies • Recent global pandemic 2. Machine Learning and Deep Learning Machine learning is an area of Artificial Intelligence with a concept that a computer program can learn and adapt to new data without human intervention. These days Machine learning are widely used by the big retailers in order to learn the customer’s behavioural patterns, their logic behind purchasing any particular product or availing any service and their preferences from the interaction of the machine learning and computer and various computer software applications. To provide better services to their customers and to enhance their shopping experience, Walmart continuously upgrade its technologies to understand customer’s needs. It has broadened the scope of Marketing and business process optimization in the retail sector. Here is how Walmart makes an efficient use of Machine Learning technology: • • • •
tracking the search history of customer and recommending Ads on different websites based on their search history. By offering fast delivery based upon past shopping experience. Predicting customer’s need based upon their previous behaviour data. Market segmentation by grouping customer’s according to their past behavioural history. 21
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Utilising customer’s buying details, Walmart uses these data to make better plans and strategies to have better deployment of resources.
2.1 How Machine Learning algorithms are applied by Walmart for different Problem Statements: 2.1.1 Inventory Based on Demand forecasting By reviewing the information obtained from Inventory data of Warehouse, the analysts of the company look for the data structure and consistency if any, then they look for the patterns and the factors which are affecting demand of a particular product. For forecasting the future demand, there are various tools and techniques of Machine Learning which can be applied. for example, to find the correlation between variables to predict the outcomes of a response variable. with the help of Association rule mining, Analysts determine which pair of products are more in demand. And through recommender system, they identify consumer’s preferences by analysing their previous behaviour and accurately suggest them relevant items. 2.1.2 Marketing Mix Analysis With the help of customer data, analysts in Walmart easily do market segmentation and so divide the market on the basis of similarity or uniqueness in customer behaviour and other aspects. So, in this case, they generally apply K Means clustering to form various groups according to the different characteristics like demographics, geographical, propensity, media habits, behavioural etc.
Figure1. K- Means Clustering graph 2.1.3 Customer Acquisition By investing in targeted ads, and through association rule mining and network analysis, company can easily reach out to the new customers, introducing their products to them and getting to know about their preferences and accordingly recommending them products and 22
services. And with the help of Bayesian regression, analysts predict the next relevant product to offer to the existing customers on the basis of the what they have purchased before and also by providing them with more relevant information, product and services, they win customer’s trust and retain them for future transactions. 2.1.4 Faster Delivery Chain Going through with the customer’s feedback and delivery data report, analysts apply prescriptive analysis here to prescribe what action we can take so as to mitigate the problem faced by customers for delay in delivery of their product and on the basis of that, they continuously, improve their delivery service. Also, by looking at the historical data, they get to know what was the reason for the late delivery of the product whether it was traffic problem or something else. So, in this case, they use Simple or Multiple regression to find out the correlation between the factors which are affecting the delivery chain.
Article submitted by -Shristi Vaish (PGDM) Back to Table of contents
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