Consumer Behavior Analysis Research Project
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Consumer Behavior Analysis Research Project
The modern consumer behavior perspective is just the same as the traditional consumer behavior perspective.
Our project is consumer behavior analysis. Research has been conducted and presented on the behavior of consumers and how the data obtained is important in solving real-world problems. In analyzing consumer behavior in this paper, we will embrace data mining techniques. Each data mining technique has its pros and cons. For this reason, we will choose the best technique to mine our database.
The main objective is identifying psychological conditions that affect customer’s behavior at the time of purchase and the key data mining tool that is convenient for each method of purchase. Furthermore, there is an association rule that is employed in customer mining from the sales data in the retail industry.
Analyzing consumer behavior is important as the data obtained is converted to a format that is statistical and a technical technique is used to analyses the data (Stoll, 2018). Business enterprises also use the knowledge of consumer behavior in the following ways:
- Determining the psychology of consumers in terms of their feeling, reasoning, and thinking and how best they can choose between the alternatives.
- Businesses also determine how the business environment affects consumers’ mindset.
iii. Businesses can determine the behavior of customers at the time of purchasing their goods and services.
- Companies also find out how customer motivation affects customers’ choice of goods of utmost importance.
- Finally, business finds ways of improving their marketing strategies based on the available data that they will gather.
The modern consumer behavior perspective is just the same as the traditional consumer behavior perspective. The patterns used by consumers in the day to day lives are also applicable in the online context. Koufaris (2002) in his article argues that online consumer behaviors are similar to traditional behaviors.
However, online consumers have additional advantages as besides being customers, they easily access the information about the goods and services they want. The contents of our datasets pertaining the consumer behaviors can be found in Montgomery, li, srinivasan, and liechty (2004.)
In the present world, a normal consumer is regarded as a constant generator whom his or her data is treated in diverse contexts as unstructured, contemporary and behavioral. The volume of the customer data and how rapidly the data is generated affects how the data is generated and interpreted. The large volumes of generated data have caused data scientists to look for a format of making the data easy to be interpreted by common people (roll, 2019).
Large volumes of data are complex and there is a need for sophisticated techniques to aid data management and visualization. Unprecedented volumes of data have caused data to be more complex. Big data analytics is inevitable in the present world to aid in business decision making.
Clickstream data is among the key types of consumer behavioral data. This type of data is collected by the server from the electronic record of the website (nimalendran, 2017). This type of data concerns the number of clicks that customers make when visiting the website. This data gives an insight of the navigated pages. Text data is the second format that caters to the naturalist of the emails, webpages and other posts of social media.
Using online consumer data is through path analysis. This is the navigational path based on website insights. The data analytics in this sense gives the follow-up events up to the point the customer decides to buy the goods and services. The prediction of the customers’ behavior aims, interests and knowledge can be determined in this case. The ability of the business to use customer data in making decisions is explored in the discipline of customer analytics.
In the implementation of the consumer behavior data set, we embrace customer behavior modeling in coming up with the mathematical representation of the common behaviors that we observed from the groups of customers that we observed. These representations are important in that it will give us a prediction of how customer’s behavior will be given the same circumstances.
In the present competitive world, no business can survive without predictive analytics. Since we are living in a competitive world, the survival of the businesses will greatly depend on predictive analytics. The main aim of business analytics is to work on the profitability of the business.
Ml application in data algorithms requires one to understand the type of data he or she will be dealing with. If a data scientist is a novice, there will be a high possibility that the ml model will not succeed. Explanatory data analysis (EDA) is mandatory and it is performed following three visual methods.
The three visual methods of EDA include text analysis, volumetric analysis, and time pattern analysis. In volumetric analysis, customer behavior is categorically done based on the numerical features of data (garvy, 2015).
This visual method is important in the analysis of bi-variate and variate data. Another visual method is the time pattern analysis which is based on the format of time and date. In principle, text analysis is embraced in many companies as it incorporates all text data characteristics. This text analysis is like n-gram and word cloud.
There is a correlation amid the sets of data in this technique. Catalog design and loss leader analysis are the results obtained from the transactional records of this data technique.
Market basket analysis is what a researcher will get from the association of rule mining. This technique is keen on the customer habits of purchase depending on the available items of purchase. This information is important to retailers as it will aid in their ability to coming up with good marketing strategies.
For instance, a trip to the supermarket to buy greens will need that the customer also buys onions and tomatoes as they will go hand in hand. This helps retailers utilize their shelf space well and hence improve their sales.
In the current world, an ordinary shopper is viewed as a steady generator whom their information is treated in differing settings as unstructured, contemporary and conduct. The volume of the client information and how quickly the information is produced influences how the information is created and deciphered. The huge volumes of produced information have made information researchers search for an arrangement of making the information simple to be deciphered by average folks.
Huge volumes of information are perplexing and there is a requirement for modern strategies to help information the executives and representation. Extraordinary volumes of information have made information increasingly mind-boggling. Enormous information investigation is unavoidable in the current world to help in organizations dynamic
Garvy, g. (2015): “rivals and interlopers in the history of the New York security market,” journal of political economy, 52(2), 128-143.
George, t.j., g. Kaul and m. Nimalendran (2017): “estimation of bid-asks spreads and its components: a new approach,” review of financial studies, 4(4), 623-656.
Roll, r. (2019): “a simple implicit measure of the effective bid-ask spread in an efficient market,” journal of finance, 39(4), 1127-1139.
Stoll, h.r. (2018): “inferring the components of the bid-ask spread: theory and empirical tests,” journal of finance, 44(1), 115-134.
Consumer Behavior Analysis Research Project
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