Free Leveraging Big Data To Analyze Consumer Behavior In Digital AND Retail Marketing Research Proposal Sample

A Research Thesis Proposal Submitted to Dr

Abstract
In 2010, it was confirmed that the world has crossed the mark of one zettabyte data. This large amount of data is processed using completely fast computers hence easier reach of information that was formerly presumed to be beyond reach. This sums up the whole idea of big data. With the current media transition trends from traditional to digital, big data is a mega opportunity for organizations to optimize consumer and digital markets. The huge amounts of data in conjunction with the availability of advanced technology provides an open opportunity for organizations to utilize big data to their benefit. Big data can be leveraged, especially through technological means, to optimize digital markets and consumer footprints.
1. Introduction

Background

In the modern world, consumers become sensitive to their levels of satisfaction as days unfold. Organizations, on the other hand, continue improving their production and marketing to ensure that their products meet consumer requirements. In so doing, knowledge regarding consumer requirements turns out to be considerably important in ensuring that consumers are satisfied and determining how to target them (Weinman, 2015). Big data is an important and efficient tool for organizations in ensuring complete optimization of digital consumer marketing. From the very many definitions of big data, the following can be best suited for the purpose of this research proposal: Big data is a collection of unstructured, semi-structured and structured data that is too large to process conveniently or analyze using traditional data analysis techniques. Beyond merely being large, big data is considered new and emerging and with the ability to make businesses agiler (IBM n.d.).
It is through big data analytics that organizations can make meaningful conclusions that are used to make future decisions relating to consumer (Hemann & Burbary 2014).
As per IBM (2014), retailers may also use big data to measure online conversion rate along with information from individual stores and departments to have improved consumer experience and in result better marketing strategies for engaging outcomes.
1.2 Problem Statement
The increase in the use of ecommerce and online shopping whether it is for grocery, clothes or travel packages has increased the quantity of often referred to as BIGDATA available in the digital world. The dilemma with that data is how it can be segmented and utilized effectively to analyze consumer footprints and their behavior in the digital environment. This is necessary in developing future marketing strategies for the company/organization to have a determined marketing approach to cater to different consumers according to their behavioral pattern recognized through analysis of data. Failure to define a sustainable solution for this dilemma attracts the use of traditional marketing that, among other limitations, is expensive and time consuming. Digital marketing, on the other hand, is dynamic, time saving, less expensive and fast reaching.

How to use BIGDATA and other subjective source of data’s if available to analyze consumer behavior in digital marketing.

What is the predicted pattern of a consumer and how does it affect the buying behavior?
How to examine those data to segment consumers and create marketing strategies based on those segments which work best for the brand/company.
The research aims at investigating how big data can be leveraged to optimize digital marketing and scrutinize consumer behavior. It provides with a better understanding or view of how the traditional/digital media transition has facilitated the use of big data analytics to make meaningful decisions because traditional marketing is expensive, slow and complex as compared to digital marketing which can be analyzed real time and yield refined outcome with dynamic perspective that too at a reduced cost (Business Zone, 2013).
2. Proposal
Digital Market and the use of internet is growing heaps and bound every second and so it the way consumer use internet on a regular basis. With the number of hours spent digitally getting bigger also has increased the chances of people shopping online. The dilemma with so much data is how to effectively utilize it without getting bemused in the numbers. Also what is important is to do intensive literature review on what type of data can useful to study consumer behavior rather than having no clue about how to deal with Gigabytes or exabytes.
In my view, the central parameter that determines whether big data is useful for an organization in marketing or not is the extent to which big data analytics is conducted. While some organizations conduct mere descriptive big data analytics, other go to the extent of prescriptive big data analytics. The only organizations that enjoy full benefits from big data are those that complete the whole analytics cycle (Schmarzo 2013). Organizations can only optimize digital and consumer markets if big data analytics does not end at the descriptive level but continues through the four stages up to the final stage of prescriptive analytics where it is used to make meaningful decisions which are related to consumer behavior, their interests and loyalty along with purchase behavior.
With the emergence of digital marketing and online consumer patterns, big data turns out to be considerably important in identifying and targeting consumers and understanding how the internet footprint has expanded over the years. Organizations use big data to determine consumer demand patterns and thereafter develop marketing strategies to satisfy such patterns. If big data analytics can be conducted to the prescriptive stage and the results used to make decisions regarding digital and consumer markets, such markets can easily be optimized (Artun & Levin 2015).
Among the previous researches relating to the power of big data for digital market and consumer behavior optimization include Ziyad Saleh’s (2015) who proposed that big data can be used to uncover many life and organizational issues and how it can be used to maximize the benefits. Koen van Antwerpen (2014) research discussed how marketing successes can be achieved with big data analytics. My research is, however, different from the previously mentioned researches in that it focuses solely on how complete analysis of big data can be used to leverage digital with the help of consumer activity and their online footprints.

Big data can be categorized in 5 sections namely; Value, Volume, Veracity, Velocity and Variety (IBM, 2015).

Value stands for the power the data can have for analysis and not be just a huge number of data. If the data is huge but does not have any importance in terms of what analysis can be made and if that analysis be of any help to the company and its strategies. If not, it would rather be a condition where you have lesser data but it can be segmented and analyzed to create focused strategies for marketing. Volume is the scale of data available which keep on increasing every second as the use of internet increases around the world. Veracity can be defined the uncertainty the data holds. Users might not be feeding in the right information and biases while filling in data or shopping behavior. Velocity is the amount of data. It can vary from thousands of data to gigabyte and exabytes. Finally variety can be defined as the places those data is being taken from, was it from online sources like Facebook, Google and Yahoo or was it taken from healthcare databases (IBM, 2015).
Having a clear understanding of all these potential forms of data and their characteristics, knowing which data to be used and analyzed and what to be considered waste and analyzing them for segmented marketing and targeted strategy building is the key for this research.
Foursquare, a local search and discovery application, is a good example of how data influences the market and enhances its strategies (Wikipedia, 2016). The application is used to locate nearby events, persons, organizations among provision of other useful information depending on the geographical location of the user. It uses data from all users and consumer online experiences to suggest activities and places which may interest users. This is an intelligent app useful and successful because of its data and location accuracy (Business Insider Inc, 2016).
Industries which can be analyzed for Big Data can be fashion, tourism, medical - and many more that deal with humongous amount of records both online and offline.
3. Research Design
In an attempt to ensure a better understanding of the subject, research on the availability of big data to people who can benefit from it (both natural and artificial) will be conducted (Ayanso & Lertwachara 2014). Research on the strategies put in place by organizations to ensure that there is enough data for analytical procedures will be evaluated. Moreover, research on the extent to which such data is used to make meaningful decisions shall as well be conducted. The merits and demerits of using such data shall be weighed out, and the challenges encountered enquired for. Beyond the mere assessment of data mining techniques, the research shall get to the extent of evaluating organizational data clustering techniques.
Questionnaires will be prepared for the managements as well as other stakeholders regarding the extent to which they think successful big data analytics contributes to marketing. The research will compare organizations that use big data and big data analytics in making decisions relating to consumers in digital marketing with those that do not and will be measured on the basis of how these companies use the results and outcome from big data analysis and produce marketing strategies in a focused manner and does it help or improve the business and in what manner.
4. Expected Results

The research is expected to yield the following deliverables:

A detailed narrative on methods developed to determine the extents to which organizations collect and use big data for marketing.
The most appropriate big data analytics to understand digital environment and consumer market optimization through measurement of marketing activities and segmentation of business and consumers.
5. Management Approach
5.1 Research Plan
This research has been divided into four major tasks that include project management, research, model development, and final deliverables. The initial activities of the research such as problem definition and progress presentations are contained in the project management that is a preliminary stage. The research stage is comprised of exploration of various means through which organization collect, store and use or plan to use big data (Close et al., 2014). Data analysis and model developments shall involve comprehensive assessment and evaluation of the data collected at the research stage. The final stage of the thesis will involve producing results in a manner that can be easily understood and implemented by the organizations.
5.2 Risks Involved
There is the risk that data may be complex to understand at the first stage due to its technical nature. Having a good knowledge of IT and some programming background to decode the data and analyze them would be an advantage for me. There is a risk that big data may require super expertise. Liason with IT experts is a good strategy to control the possibility of occurrence of loss.

Conclusion

It is my belief that this is the first proposal to leverage big data in a bid to optimize digital and consumer markets. It aims at enlightening marketing professionals from global and local environment and giving them a better understanding of how big data and big data analytics can be leveraged to ensure success in digital and consumer marketing (Isson & Harriott 2013). The research unveils the fact that mere possession of big data is not enough to facilitate digital and consumer market optimization. Organizations which are global and have huge consumer data ( online and offline) must be ready to conduct complete big data analytics processes if at all they are to benefit from their big data possessions.

Reference List

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