Leadership Analytics (ALY6120)
(August 18, 2018)
Dr. Paul Dooley
Amazon – An E-Commerce Global Giant
Amazon, an American international e-commerce, was first founded in 1994 and was launched in India in June 2013. The company tried its launching in China before its launch in India. This China launching couldn’t match with its competitor Alibaba and failed. As Yi Shi, CEO of Chinese startup Avazu, has said, “For a large market like China, you will need 100 percent localization to compete with local players” (Kola 2016). Localizing turned out to be too difficult and too costly for American e-commerce giant as the concepts of Chinese customers was very different. Localization is a practice of manufacturing a product that adheres to the characteristics of the products of the foreign country in accordance with the cultural, political and legal differences. (“What is localization? definition and meaning”).
So, they decided to go with a different model. Though Amazon had no infrastructure in India, it now dominates the Indian e-commerce market. Amazon decided to enter the Indian market as it was a very large market as compared to American market. It is four times bigger than US market and it is double the European market. And moreover, the internet users are growing in India. Even though there were only 35% of people in India use internet, that itself was still more than entire US market. (“What ‘tech world’ did you grow up in?”)
Analyze the issues and challenges for Amazon in India.
To carry out market research for amazon business models to overcome these challenges and become the leading ecommerce company in India.
To analyze the alternatives and choose optimum strategy.
As the leader of the Amazon, the objective is to have growth as shown in the following figure.
The SWOT analysis of the leader is as follows.
Ability to delegate authority
Commitment to team building Being insecure
Lack of expertise
Significant job role Competition
Support from the top-level management
Knowledge of new tools and technologies
In order to lead Amazon, there is a strong need to overcome these weaknesses mentioned in the SWOT analysis. As a leader, though being insecure about the business model and the position of the company in market is very natural, it should not have any kind of impact on the decisions being taken. Such fear should not come in the way of leading the teams as it might create a negative effect on the minds of the team members. Thus, it is very important to control your emotional states while taking professional decisions. Discussing the decisions and situations of the company with the team members and together with them taking the decisions will help in taking more rational decisions.
Most important of all is meeting the deadlines. This is the core reason of success for any organization as doing the right work at right time is all that is needed to be ahead of the competitors. Team support is the key to get over this weakness.
Micromanaging is the worst weakness when there is lack of expertise in terms of specific technology or software that is used for the data analysis. In order to overcome this, technical leadership is one of the things that needs to be pursued. This will also reduce the micromanaging as you know that your team is going in the right direction with right technical knowledge and also as a technical leader, you will be able to discuss with the team the technical progress in the process of the data analytics.
How Amazon created its business model for Indian market after failing in Chinese market?
Amazon, American based e-commerce giant wanted to enter the Indian market. Here the main business problem is to create a business model which is successful for Indian market. The main reason behind this problem is that it did not make a model before entering the Chinese market and failed. Amazon wanted to become the leading e-commerce giant in India proving wrong the perception of investors because of its failure in Chinese market. Amazon did not carry out a market research before entering the Chinese market. Specific unit was created in order to carry out this research for the Indian Market. This unit required employees who had outstanding analytical skills and leadership skills.
-255270139255500CRISP-DM is one of the business models that helps in determining a structured approach for the data mining project. It stands for cross-industry process for data mining. It helps in planning the project in an organized sequential event which helps in finding the solution to the business problem. The model has very important steps in an orderly fashion, if followed helps find the proper solution to the business problem. Certain times in order to find the optimum solution, some of the steps need to be repeated. But this repetition only leads to finding a more effective solution.
226017618542000-55033360875300An infrastructure Diagram
Before entering the Indian Market, it should prepare data for why it failed in the Chinese market. The data then needs to be prepared considering the same factors for the Indian market. From the study it could be figured that following three factors were the major reasons Amazon failed in the Chinese market.
The people of China were not so supportive for a new company such as Amazon to enter their market and were reluctant to adapt to new ways of purchasing.
Amazon did not research properly about the local competition Alibaba (a local e-commerce company) and thus it did not have strong platforms to fight with the local competitor.
Amazon did not get enough support from the Chinese government, in terms of foreign direct investment and it did not study about it before entering the market.
In order for Amazon to enter the Indian market, apart from other factors, data preparation for these factors was of upmost need. Data preparation is usually the most time-consuming task in all the phases of the CRISP-DM.
Thus Amazon, in order to know about the local competition, decided to acquire few startups such as pets.com, audible.com, Junglee.com, IMBD.com, Zappos.com, Woot etc. This helped them in reducing their cost of outsourcing and also got to know about the technical advancements in the Indian Market. From all these companies, Amazon was able to prepare the dataset as Junglee.com is also an online Indian retail company. These companies provided Amazon the basis required for data preparation.
Also, in order to know more about the preferences of the people of India, it decided to carry out surveys and collected the data. It decided to develop a team which had very technically efficient employees who could make this data more accurate by cleaning the data and making it error free.
It will be using descriptive and predictive analytics to study about the data and construct more accurate data. The data was then formatted in order to know the consumer behavior and trends in order to model a business plan for the Indian Market.
Here all the steps were very important as with this data integration and proper analysis of the data, different factors affecting the Indian market could be figured out. Thus, with data preparation, it is very important that all these steps are followed with the most detailed accuracy as this is the only way based on which the business model will be created.
The next step would be the modeling phase from the CRISP-DM. This phase has basically following four tasks.
Selecting modeling techniques
Here as a leader of the Amazon, we would need to use the data prepared and analyze the data. There are several analytics techniques available. But here first the Amazon will carry out a descriptive analysis.
Of all the tests available such as regression, clustering and segmentation, Amazon decided to carry on with the regression as it had very clear sampling available from the other e-commerce startups that it had acquired and also the surveys it carried. This made it easier for Amazon in terms of building the models as they were able to analyze the trends of purchase and sales and also the needs of the customers. While designing the tests, amazon took into consideration various attributes that might have helped them in analyzing the factors that affect the Indian market.
In order to build the models, Amazon needed to do three things as following
Parameter Settings: It is important to set the parameters in order to build the models. This will make the model not able to adjust to various settings. So, for example, initially Amazon decided to target only middle-class people who could afford to buy the retail products but did not have time to go to stores. Setting this parameter helped in building a model for which the analysis was only made for the needs of the middle-class people.
Model Descriptions: Here in order to decide the business model to choose from the alternatives, Amazon should carry out a linear regression as it has enough sample data that can help in finding the trends for the variables such as customer demands, seasonal demands, mode of payment preferred, acceptance to e-commerce retail.
Models: Here all the possible business models will be derived once the results from the regression is obtained. This will be based on the trends and patterns derived from the data analysis of the sample population. From the alternatives the optimum would be chosen in order to enter the Indian Market.
Model assessment: Amazon for the Indian Market based on the analysis decided to launch cash on delivery as a mode of payment. A brief assessment was required to be done as there is a higher probability for the chances of the fraud and other loopholes.
Revised parameter setting: Thus, Amazon decided to collaborate with the banks and provide more discounts on the card transaction in a way trying to reduce the cash on delivery transactions.
Amazon, in order to enter the Indian market, using the CRISP-DM has been able to prepare the data to the extent that it could understand the customer behavioral patterns and the needs and demands of the Indian Market. Though Amazon did not have any infrastructure in India, initially it decided to use outsourcing and create a set up in the Indian Market. Amazon followed the same strategy for the Chinese market but failed and hence as a leader, I believe that it was a time to change the strategy. Thus, this time while preparing for the data, it was the time to try acquiring the local startups. While analyzing those startups, they seemed more cost efficient and also more technically efficient. Thus, this strategy helped in getting the insider data of the e-commerce retail sector of the Indian market.
In order to study the acquired data, as a leader I would use descriptive analytics to have the knowledge of the past and what patterns and trends the Indian market followed. Descriptive analytics is a preliminary stage of data processing that creates a summary of historical data to yield useful information and possibly prepare the data for further analysis.
So as to decide for the predictive modeling, descriptive analytics was used which in order to have patterns and trends uses data aggregation and data mining methods. These methods are to be used to determine the patterns and trends, as they help in organizing and cleaning the data. This makes it easier in identification leading to more detailed insights with the help of data visualization, querying and reporting.
Data aggregation is any process in which information is gathered and expressed in a summary form, for purposes such as statistical analysis. Data mining is the process of sorting through large data sets to identify patterns and establish relationships to solve problems through data analysis.
Thus, with the descriptive analytics we would be able to get insights into past encouraging us to use the predictive analytics. As a leader of the Amazon, I would first like to try the linear regression for carrying out the predictive analytics. Here, we would take the sales as the dependent variable for the Amazon to carry out the predictive analytics. From descriptive analytics, we could figure out that in India, sales would depend on the pricing of the product, mode of payment and also good returning policies as online retailing was still a new concept for which the customers did not trust the quality. Also, by analyzing we could figure out that online retailing was supposed to be served more as a convenience for the middle-class people who could afford the product but did not have time to go to the physical stores. Thus, the regression model would help by considering these predictive independent variables to determine the sales and relation between all these variables.
I, as a leader along with the team members decided to go for regression mainly because of its three major uses which are (1) determining the strength of predictors, (2) forecasting an effect, and (3) trend forecasting. Amazon by using this regression for predictive analytics decided to come up with Cash on Delivery mode of payment option which is only for the Indian market. This was because of two reasons as determined by the descriptive analytics that people in India do not use mush of any plastic cards for such kind of purchases and also the mindset of Indian people only believed in paying after the service has been provided.
Thus, in this way, as a leader along with the team members, we used the descriptive analytics in order to know the trends and patterns which helped in predicting a successful cash on delivery mode of payment option for Amazon.
This was a final report for the deployment phase has to be created once it has undergone all the other phases to represent it to the top-level management which will be approving the report. It will the deployment stage which would act as a final test for the leader where the final entire report along with final presentation needs to be evaluated and submitted to higher authority for implementing the business decisions and solutions.
Bharti Wadhwa and Anubha Vashisht, Davinder Kaur (2017); BUSINESS MODEL OF AMAZON INDIA A CASE STUDY. Int. J. of Adv. Res. 5 (8). 1426-1433 (ISSN 2320-5407)
What is localization? definition and meaning. (n.d.). Retrieved from http://www.businessdictionary.com/definition/localization.htmlWhat is the CRISP-DM methodology? (n.d.). Retrieved from https://www.sv-europe.com/crisp-dm-methodology/#modeling/Descriptive, Predictive, and Prescriptive Analytics Explained. (2016, August 05). Retrieved from https://halobi.com/blog/descriptive-predictive-and-prescriptive-analytics-explained/Phase 4 of the CRISP-DM Process Model: Modeling. (n.d.). Retrieved from https://www.dummies.com/programming/big-data/phase-4-of-the-crisp-dm-process-model-modeling/What is Linear Regression? (n.d.). Retrieved from http://www.statisticssolutions.com/what-is-linear-regression/What is descriptive analytics? – Definition from WhatIs.com. (n.d.). Retrieved from https://whatis.techtarget.com/definition/descriptive-analytics