E-Commerce Negotiations: Advancements, Challenges, Data Considerations

E-commerce Negotiations: Progress, Challenges and Data Considerations

E Commerce is a way to buy and sell products in retail. While some businesses utilize e-commerce as the only means of selling their goods, others incorporate it into a bigger marketing plan that also includes physical stores and distribution methods.

E Commerce is a great way for startups, shopping cart development ,small businesses, and larger companies to reach customers around the globe.


What is an Ecommerce Website?

A digital storefront is an ecommerce site. It facilitates a transaction between buyer and seller. This is a virtual space in which you can showcase your products and where online customers can make their selections. Your website is the virtual store, salesperson, and cashier of your online business.

Businesses can create their own commerce website on a dedicated domain or build a branded experience on an online store such as Amazon.


How does Ecommerce Work?

E Commerce is a system that connects buyers and sellers through electronic channels. You need, for example, a channel such as a social media platform or website to allow customers to find and purchase products. A payment processor then facilitates the exchange of goods or services. The customer will receive a confirmation via email or SMS and a printable receipt once the transaction is successful.

If it is a transaction for goods, the seller will ship the item and send the customer the tracking number by email or SMS. If it is a transaction for a service, then the service provider will contact you to schedule the service and complete it.


What Are the Different Types of E-Commerce?

There are as many ways to shop online as there are ecommerce. Some of the most common business models in e-commerce include:

  1. B2C: Businesses sell directly to consumers. This is the most common model, with many variations.
  2. Businesses sell to each other: The buyer often resells the product to the consumer.
  3. C2B: consumers selling to businesses. Businesses that allow C2B transactions let customers sell to other businesses.
  4. C2C: Consumers selling to other consumers. Businesses create online marketplaces to connect consumers.
  5. B2G: Businesses that sell to government or government agencies.
  6. C2G: Consumers selling to government or government agencies.
  7. G2B: Governments and government agencies selling to businesses.
  8. G2C: Governments and government agencies selling to consumers.

The Following Is A Brief Introduction To The Topic:

The Following Is A Brief Introduction To The Topic:

In order to negotiate between buyer and seller, this blog focuses on the employment of intelligent agents in B2C commerce employing big data analytics.

The developed model is used for conducting negotiations on behalf of prospective buyers and vendors, using analytics to improve the negotiations in order to meet practical requirements.

This blog explores the possibilities of using business analytics and big data for negotiations, whereby big data analytics could be used to create opportunities for bidding.

Sellers can learn how to predict buyers negotiation strategies and adopt the best tactics for achieving their goals. A design experiment is used to collect data that can be used in the negotiation process. This approach will enhance the quality of both parties negotiation decisions.

The affordability of smart mobiles with permanent connections, social networks, and live conversation streams has significantly changed B2C online commerce.

If we were talking about negotiations a few years ago, where the parties involved had no or little knowledge of attributes, values, and other relevant information, today, such information is available from a variety of online sources.

Intelligent automation can be a great help in many areas of e-commerce, including auctions, contracting, scheduling and other activities.

Negotiations are a way of interacting between parties who have conflicting goals and want to work together to reach a solution that benefits all parties. This can be a time-consuming process.

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What Does E-Commerce Negotiation Mean?

What Does E-Commerce Negotiation Mean?

The e-commerce negotiation process is a way to make decisions that will allow two or more parties to reach an agreement electronically, even if they have limited information or differing preferences.

E-commerce negotiation is a decision-making process that seeks to find an electronic agreement, which will satisfy the requirements of two or more parties in presence of limited information and conflicting preferences. Buyers and sellers are free to assess the utility of each e-commerce solution.

Negotiation is about finding a solution which maximizes utility value to both parties.

A result of the recent technological advancements mentioned above, all B2C organizations are now forced to develop and improve their existing services in order to attract and retain customers.

E-commerce businesses are also negotiating with customers to get better deals. They do this to retain their clients, build lasting relationships and increase customer satisfaction.

It is important to pay attention to this topic, given the increasing importance of B2C negotiations. Big data analytics can be a great help in negotiations.

Analytics will help businesses reduce the time and effort required for negotiations. It will also help customers who lack negotiation knowledge and skills.

The volume of data and information provided and the way they are used for optimizing the negotiation operations will determine the success of eNegotiation.

The volume of data can be used to extract valuable information that could determine the success or failure of a company. A seller can use big data analytics to learn how to predict a buyers strategy for negotiation and then develop and implement the best tactics to get results in his favor.

B2C negotiations are a success when the seller can manage and transform data to useful information, and use it to differentiate himself.

B2C negotiations must take advantage of the large volume of consumer data available due to the Internet, social networks, mobile phone applications, RFID, sensor applications and other new technologies. The size of the data is increasing exponentially. The collected data is mainly unstructured, but contains valuable information about customers opinions and behavior. Big data analytics is a set of integrated technologies, practices, methods, and applications for analyzing critical business information.
Data can help an organization understand their business better and make more informed decisions.

There hasnt been enough research in academia on how to effectively leverage big data for meaningful information to be used during e-commerce negotiation.

The proposed model allows for simultaneous multi-party negotiations. This agent-based electronic negotiation system incorporates big data analytics to conduct goal-driven multiparty negotiations on multiple issues at the same.

SA can accurately predict profitability using different variables. The variables are original price, quantity available, delivery time, and others.

SA will then calculate the best price to ask for and the price at which the product can be sold on the spot. This is done in order to maximize profits.

This work develops a framework architecture for e-commerce negotiations application as one of the B2C services that B2C companies can provide to increase online and mobile customer satisfaction.

In order to achieve this goal, the author integrates intelligent agent technology and big data analytics into an intelligent negotiation model.


History of E-Commerce Negotiations

History of E-Commerce Negotiations

As more and more business activities become electronic, the number of these processes will increase. It has become a normal part of life.

E-commerce has many advantages that anyone can understand. It changes our business model and simplifies life. Some business areas are resistant to change due to their particularities.

The majority of business negotiations is one such area.

Traditional or partially automated systems cannot keep up with the increasing frequency of electronic trading. Automated business negotiations will increase efficiency, reduce costs, and promote the further custom extension development of custom extensions in eCommerce.

In the past two decades, negotiations have been extensively studied. Artificial intelligence (AI) is one of the most common methods used in ecommerce negotiations.

AI has been used for various purposes, including research, education, and training.

The majority of earlier negotiation models were built on fixed assumptions, which often led to mismatches. They are therefore inappropriate for real-life electronic negotiations.

These models require complex computations and a large amount of memory, especially when multiple attributes are involved. Online negotiation applications are being developed.

Most of these applications are one-site negotiation support systems that require human involvement. Intelligent agent technology was used in a variety of existing eMarketplaces to implement e-negotiation applications.

Market agents trade only on price. In the real world, however, negotiation is not limited to price alone, but involves multiple factors (e.g., price, quantity, and product quality).

Read More: Select the right enterprise e-commerce solution that suits your business


B2C E-Commerce Negotiation

When two or more parties are attempting to reach an agreement that is acceptable to all involved parties, this is called negotiation.

Decentralized decision-making is used to reach a compromise when there are conflicting preferences and incomplete information. The e-commerce market allows access to a much larger group of buyers and vendors, which can lead to better deals for both customers and businesses.

The proposed agent-based model uses big data analytics to determine the best initial offer, and to adopt multiple criteria decisions in the utility function.

Following assumptions have been made:

  1. Messages sent between two parties to convey an offer or concession.
  2. The messages are encrypted in order to protect the privacy and confidentiality of the parties involved.
  3. Transparency is required in all negotiations, which means that they must be carried out electronically.
    negotiation process.
  4. Online/offline negotiation applications are available
    mode.

If the application is disconnected, the execution resumes from the same point. The proposed system aims to help parties negotiate by determining initial asking prices, concessions, developing effective strategies, minimizing mistakes, etc.

This is especially true for those who lack knowledge about negotiation processes.

To resolve these problems, the proposed system uses software agents. Data about the buyer, including price, quality, and delivery time are stored in the profile of a mediator site.

An interface agent enters the data and stores it in the profile of the buyer on a mediators site. At that time, any restrictions on these attributes can be added. Mobile agents carry the buyers request to a mediator located at a fixed place.

A mediator can retrieve missing information about some attributes from DW. A negotiation is a combination of several attribute negotiation processes.

The attributes that are negotiable may vary from buyer to buyer and include: price, quality, delivery time and guarantee period, as well as other features important to the buyer. All attributes are assumed to be negotiable.

The exchange of counter offers is an iterative procedure that leads step-by-step to a compromise that both parties can accept.

Both parties private information, including negotiation strategies and negotiable attribute constraints, are kept secret. Opponents negotiation of a sequence of concessions can reveal a strategy.

A big data analytics system is used for the initial offer. Negotiator agent (NA), delivers the generated offer. The offer received by the negotiating party is evaluated, a new counteroffer generated and sent back.

This process continues until a successful negotiation is achieved. The negotiation process ends when the mobile agent for the buyer accepts a price that the mobile agent for the seller has offered.

Agents then return to their origins, where they evaluate the data.
The best counter-offer is selected and presented to potential buyers in an appropriate form. Negotiations are completed if the initiator accepts the counteroffer. The user has two options if the negotiation is not successful: quit or restart the process with the attributes re-adjusted.


E-Commerce Negotiation Architecture

E-Commerce Negotiation Architecture

A negotiation system consisting of multiple agents: an interface agent, an agent server, a presentation agent, mobile agents for the buyer and mediator (fixed and wireless), and agents for both buyer and seller. Below are the functions of each agent. The design of the electronic negotiation system is based on this. It will help buyers find potential counterparts and negotiate terms.
Our previous works have developed the architecture.

S-agents represent the interests of the seller. M-agents are usually installed on the desktops of buyer and seller devices, or servers.

They act as a middleman between Bagents and S-agents. To build the system, an agent framework and development environment is required. Infrastructure is needed to support interactions between agents who may be geographically distributed on the Internet.

The 3-tier architecture consists of the mobile and fixed devices for buyers, a mediator and seller negotiations systems. The first tier consists of a buyers device (wireless or fixed), equipped with an interface.
Intelligent mobile agents installed on buyer devices to assist in communication with the system, and to act as personal assistants for the buyers.

A mediator searches for sellers to facilitate wireless buyers access. Buyers usually make the final decision based on offers recommended by their negotiation agent.

The second level consists of an intermediary site, whose main functions are:

  1. The agent of the client can collect data about the buyer.
  2. Filling out the buyers profile
  3. Making an offer
  4. Generate mobile agents for each mobile
    client
  5. Selecting the best offers and evaluating them
    Continue to negotiate with the seller who made this best
    offer.
  6. Content adaptation

It controls the adoption process for the mobile device, which can have different capabilities and limitations. The server also supports wireless technologies and users with diverse wireless devices.

It is responsible for content delivery.

The server contains stationary agents (administrator, presentation agent), user profiles and a device specifications database.

When a user first requests a service, a profile is created. It contains device specifications for the client. The presentation agent determines the type of presentation that is best suited to each user based on a set of predefined rules.

So, every buyer will receive a content which is both compatible with their mobile device or wireless technology and adapted to his preferences.

Third tier: a system of seller negotiations whose main functions are as follows:

  1. The weighting of each negotiation attribute
  2. The selection of the concession strategy that will be used
  3. Evaluation of the Buyers Offer
  4. Create a counteroffer

This system architecture uses mobile agents to communicate between tiers and manages the distribution of resources.

Read More: Develop Your Custom Ecommerce Extension 2023


A. Seller Module

The Seller Negotiation Module consists of knowledge bases (KBs) that specify rules for generating advice for negotiators and different negotiation mechanisms.

It also includes a Big data system consisting of eMarket data warehouses and analytical tools integrating text mining.

These tools analyze all types of marketing data by using sophisticated quantitative methods, such as data mining, statistics, forecasting, and visualization.

These data sources are also available to customers, giving businesses the opportunity to influence their opinions and behaviors, as well as understand whether customers will spend money on a particular product category.

A seller can use big data analytics to learn how to predict a buyers negotiation strategies and adopt the best tactics to achieve results in his favor.

The data warehouse stores information on previous sales situations. The classification analytics tool then selects the instance with the most similarity to the current selling situation.

A price can be offered based on price information associated with the instance.

The DW system contains data about specific negotiation circumstances, negotiating party profiles, results of negotiations (success or failure and the terms of agreements reached), negotiation strategy, and more.

In other words, any information that could be used to determine a sequence of concessions by both negotiating sides, and so forth.


Seller Negotiation System

The Seller Negotiation System retrieves and analyzes big data in order to provide advice on the calculation of offers. These analytical results are used to build the negotiating agents behavior. Each agent uses an inference system based on the rules-based system in the sellers information base. For more complex
Powerful analytics tools can be used for knowledge processing. Agents cooperation allows for the detection of various offer conditions, which in turn helps decision makers with their negotiation process.


B. Buyer Module


The Interface For The Buyer Module And Mobile Agents

Interface Agent: The buyer provides the necessary information to an interface agent. These data will be saved in the profile of the buyer and include information such as price, quality delivery time, warranty period, etc.

If a user does not have any information about certain attributes, they can either perform the search themselves or delegate the task to an agent server. The mobile agent of a buyer will deliver recorded preferences to a mediator.

2) Buyers mobile agent: represents the buyers interests and sends the negotiation request of the buyer to the server where it will be processed.

Buyers can be at a fixed or mobile location.


C. Mediator Module

The following components make up the architecture of Mediator Negotiation System:

1) Agent Server (AS): distributed, intelligent. Its functions include providing a standard interface for other agents and managing resources to satisfy the requests of the buyer agent.

The main functions of an agent server are:

  1. Create and maintain an execution environment, and protect and regulate agents.
  2. Facilitating the migration of agents code
  3. Monitoring the actions of agents
  4. Allowing agents to communicate and co-exist with each other while working on the same negotiations;

The following sources of data are available for Analytics Tools:

  1. Avoid direct communication and any other form of communication between agents of different buyers to avoid the sharing of confidential information about negotiation strategies, constraints or negotiation status.
  2. Communication with other servers, and the ability to access services via them.

Presentation Agent (PA). With the advent of heterogeneous devices, content adaptation has become a necessity.
unavoidable. Its primary goal is to allow the presentation of digital contents on different mobile devices. Device context is information used to identify a mobile device. The device context includes some of mobile devices main characteristics, including device type and screen resolution. Mobile devices are connected to the Internet today via a variety of wireless technologies. Each device has a different rate of data transfer.

We must specify what type of wireless technology the user will use to connect to his device.
Device to the Internet It is possible that the layout, font size and image size are not compatible with portable devices. A presentat

ion agent creates dynamically new images from the original. This agent creates new content using different media conversion tools, such as text and image.
Mobile device types (such as PDAs, smartphones, and notebook computers) can be recognized.
Phones; types of operating systems (Apple OS X Blackberry OS Windows Mobile Palm OS etc.); type of format; web browsers, network type, upload and download speed. Type of format, web browsers, network type and upload/download speed of mobile devices.

The Mobile Agent of the Mediator can be moved from one system into another. The mobile agents are created dynamically in the execution.

They can be reconfigured dynamically in response to changes in the services. The users preferences are used to build an offer that will be presented to a negotiation system. This offer is then accepted by the interface agent, and passed on to an agent server.

Agent server creates a mobile negotiator agent whose task is to deliver an offer to potential buyers. The main contents of a negotiators agent are an offer that is to be delivered and an address.

This can either be specified by the client or found through a search.

A preliminary agreement is established or negotiations are unsuccessful when each agent engages in bilateral negotiations, exchanges offers and counteroffers with other parties, and so on.

In both situations, a mediator informs the agent about the outcome. When the agent learns more information about its counterpart, it can make better decisions.

The agents reasoning strategy may change as they gain more knowledge during the course of the negotiation. The agent will select the best possible outcome and present it to the buyer.

The negotiation process is completed if the buyer accepts and signs the final agreement. Negotiation is considered unsuccessful if the buyer does not accept it.


Model Of Negotiation Proposed

Model Of Negotiation Proposed

Each attribute (i) has three different values: for a seller, a maximum value (Air max), which is the asking or starting point; for a buyer, s lowest acceptable value (Ai min); and the best expectation value of the negotiation.

Each attribute (i), has three values: for the seller, the maximum value is Ai max which is their starting point or asking price; for the buyer, the lowest acceptable value is Ai min and the best expected value is Ai s.

This early prediction is based on the context and situation. The values of these attributes will be used to calculate relative concession rates.

The same attributes cannot be negotiated. Each attribute has a weight (wi), which represents the importance of that negotiation attribute. The weight of each attribute is decided by both the buyer and seller according to their preferences for each negotiation.

The Fact-Based E Negotiation Model is described as follows: Initially, the buyer and seller choose their concession strategy and assign the weight to each attribute.

They then submit these choices to their negotiation agent. The other side is unaware of the concession strategies or attribute weights assigned by each party. The value of negotiation attributes is delivered to the appropriate opponent agent.

It is important to note that the goal of e-negotiation should be to maximize utility functions. The worst case scenario shouldnt make utility functions lower than predefined values. Negotiation will not proceed if the process is interrupted.
Should be terminated. The SA will forecast the acceptance probability of the buyer in each negotiation round.

The sellers agent will calculate their own evaluation function and determine the actions they want to take. They then refresh these parameters and move on to the next round.

The negotiation agent (either the buyers or the sellers) checks the offer of the opponent to see if it meets their expectations, and then decides whether or not to accept, refuse or continue negotiations. If the negotiation is to continue, the side that wants to continue will change its offer to demonstrate a willingness to compromise and then negotiate with the other party.

It is the bidder who evaluates and accepts the offer of the other party. The opponent can reject the proposal and adjust the attribute values, create a counter-proposal and return it to the bidder.

The process continues until both sides agree on the proposal or the attribute values are balanced to the point where they accept it.

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Conclusions And Future Words

This work presents a description on the B2C electronic commerce negotiation model. This models primary task is to negotiate on behalf of potential buyers and sellers. This model uses multiple software agents to represent different functionalities of the system. It also applies to big data.
analytics. Analytics results allow agents to take proactive and/or reactive actions in negotiation based on their analytics. They may be able to improve their ability to select and achieve goals, and take correct actions based on the analytics knowledge.

The user interface is customizable. The information entered by the buyer is stored in their profile and will be used to generate the original offer.

To speed up the process, multiple negotiator agent servers are used to conduct negotiations with different organizations simultaneously. The best counter-offer will be selected by the server and presented by the agent to the buyer. Future research will focus on the ecommerce development of a fact-based, secure e-commerce agent-based negotiation system.


References

  1. 🔗 Google scholar
  2. 🔗 Wikipedia
  3. 🔗 NyTimes