CHAPTER ONE

1.0 INTRODUCTION

1.1 BACKGROUND OF STUDY

There have been new advances in the business environment with advancement in the level of technology; most business owners tend to market their products both online and offline. It is evident that the online business is taking the lead in the business world and thereby creating series of competition among business owners in the same niche.

As the level of competition continue to grow, the prices of most commodities becomes unstable which in turns brings about the need for improved dynamic pricing model and other strategies for e-commerce system.

In dynamic pricing products prices always respond to the fluctuation of the market and hence the prices keep on changing with the tick of a clock. Every seller wants to set the selling price of their products so that their revenue is maximized. Most sellers usually have the problem of sustaining the prices of products in the market and this become a problem to them. The purpose of the dynamic pricing problem is to determine the selling prices such that sellers receive maximum revenue. Usually, a customer before buying a product selects a store/seller for the purchase. The selection may be done under multiple attributes (preferences), such as best price offered, after-sale services, product quality, delivery time, sellers’ reputation etc. Therefore, the sellers have to provide a competitive price for a product in response to variation in the market parameters such as competitors’ prices and consumers purchase preferences. There exist intelligent agents, called pricebots, which enable online sellers to dynamically calculate a competitive price for a product. According to Dasgupta et al. (2004),”these intelligent agents provide a convenient mechanism for implementing automated dynamic pricing algorithms for the sellers in an online economy”. However, some intelligent agents use a number of assumptions for the dynamic pricing in online markets. Some intelligent agents assume that sellers are provided with complete knowledge of market parameters, while some other agents consider product price as the only attribute that determines consumers’ purchase decision. it is to this regard that the researcher wishes to carry out a dynamic data driven algorithm to improve price model/ strategies for e-commerce systems using Konga.com as the case study.

1.2 HISTORICAL BACKGROUND OF KONGA.COM

Konga was launched in June 2012 by Sim Shagaya with 10 workers. In March 2013, Naspers acquired a 50 percent stake in Konga. Konga received funds from Investment AB Kinnevik, a Swedish investment company. Konga received over $15 million in funding from investors including Naspers and Kinnivik. Multichoice Nigeria entered a partnership with Konga to increase its marketing distribution platform.  In October 2013, Konga announced that it was moving into a new 120,000 sq ft fulfillment centre.

On 29 November 2013, Konga.com crashed and remained offline for 45 minutes. The site had unprecedented traffic as a result of its Black Friday promotions. Sales on the day were the same as the total amount of sales made in the whole of December 2012. Konga increased its server capacity to prevent downtimes in the future. In December 2013, HumanIPO selected Konga as one of the African technology startups of 2013. In May 2014, Konga reached one million likes on Facebook, overtaking its competitor Jumia. Konga’s MartketPlace platform, which allows for local and international resellers and vendors to upload their products (anything from Fashion, diapers, and toys, to mobile phones, electronics, accessories and more) was launched and released in February 2014 with the goal to become the Nigeria (and future African) Alibaba marketplace and retailer. 

During the 2014 Black Friday termed Konga Yakata sales, ₦50 Million worth of order were processed every hour with more sales generated from Konga’s mobile app.

1.3 STATEMENT OF PROBLEM

What really instigated the study was due to high level of inconsistency in the fluctuation of price and the inability of the old system to regulate prices effectively and accurately. Several models have been created to improve price of the e-commerce system but most of them failed to regulate the prices with respect to the market price fluctuation.

The most well known applications are goods with a limited availability and a set utility date, such as airplane or theatre tickets. However, some big retailers like Jumia have also experimented with dynamic pricing. The common denominator for all applications is that they involve major players that have big IT development budgets. This allows for tailor made solutions to their specific pricing needs. In order to make this functionality available to a wide range of web shops, a flexible dynamic pricing solution that can be integrated into existing web shops is needed.

1.4 AIMS AND OBJECTIVES OF STUDY

The main aim of the research work is to derive a dynamic data driven algorithm to improve pricing model/strategies for e-commerce system. Other specific objectives of the study include:

1. to derive a dynamic data driven algorithm that will save customers’ time for online payment gateways
2. to generate a pricing strategies for online retailers that sell non-perishable goods in plenty supply
3. to investigate on the factors affecting the efficiency of data driven algorithm for e-commerce system
4. to have an improved payment gateways for konga.com so as to enhance sales and patronage

1.5 RESEARCH QUESTIONS

The study came up with research questions so as to be able to ascertain the above stated objectives. The research questions for the study include:

1. How to create dynamic data driven algorithm that will save customers’ time for online payment gateways?
2. What are other pricing strategies for online retailers that sell non-perishable goods in plenty supply?
3. What are the factors affecting the efficiency of data driven algorithm for e-commerce system?
4. What are the needs to add improved payment gateways for konga.com?

1.6 SIGNIFICANCE OF STUDY

The study on a dynamic data driven algorithm to improve pricing model/strategies for e-commerce system will be of immense benefit to konga.com and other online websites that are in similar niche as konga.com and jumia.com as it will derive a dynamic data driven algorithm that will save customers’ time for online payment gateways, the study will also generate a pricing strategies for online retailers that sell non-perishable goods in plenty supply.

1.7 SCOPE OF STUDY

The study on a dynamic data driven algorithm to improve pricing model/strategies for e-commerce system will be limited to konga.com; the study will cover the dynamic data driven algorithm that will save customers’ time for online payment gateways

1.8 DEFINITION OF TERMS

DYNAMIC DATA: refers to an organization or collection of data in memory that has the flexibility to grow or shrink in size, enabling a programmer to control exactly how much memory is utilized.

MODEL: an algorithm used as an example to follow or imitate

ALGORITHM: a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer.

E-COMMERCE: commercial transactions conducted electronically on the Internet

SYSTEM: a set of things working together as parts of a mechanism or an interconnecting network; a complex whole

DATA-DRIVEN MODELS: The approach we advocate is one we call data-driven modeling. With data-driven modeling, a client uses detailed measurement of a disk (or RAID) to build up a working portrait of its behavior. The client can then use this model to better predict or understand how the disk will behave and thus enable the client to extract its full performance.