E-commerce Technologies, Data Analysis Capabilities and Marketing Knowledge

Optimizing websites involves techniques and tools with SEO, SEA and SMO.

Recently, companies use cloud computing as the technology that stores and performs

the collected data. Cloud computing provides the network platform, the infrastructure

with high performance also as services. Moreover, collected data are stored and per-

formed with cloud solutions.

2.2 EC Technologies Deployed in the Transactional Process

EC includes promotion, selling and distribution of products and services in an online

environment. In this section, we detail first the electronic commerce transaction process

in phases, then we present the technologies deployed in each phase.

The Electronic Commerce Transaction Process

Different models of electronic transaction process are defined in litterature, especially

the consumer buying behavior for BtoC context and the Electronic Reference Model

for Marketplace which focus on the BtoB context (Giovanoli et al. 2014).

According to these models, we describe an electronic transaction process in three

phases: an information phase, an agreement phase and a settlement phase.

The Information Phase is the phase of information gathering or evaluation phase of

requirements and products from various sellers. In this phase, the consumer explores

many sites in order to identify what to buy and from whom. It includes all analysis

done to check the suitability of a product to customer needs. From the business view, it

is important to attract consumers. This phase includes marketing and catalog man-

agement. The information phase ends when the product(s) and the seller are chosen.

The agreement phase may take place when the seller enables the negotiation. In this

phase the conditions, pricing and other delivery related issues are negotiated. The

agreement phase ends with submitting an order or the signing of a legal contract

between the customer and the supplier.

The settlement phase focuses on issues related to the order processing and fulfill-

ment, the delivery and payment of the final goods according to the agreement. It includes

also the post – purchase services that any e-seller should takes into consideration.

We depict e-commerce technologies that can be deployed in each stage of an

electronic transaction process.

Technologies Deployed in the Information Phase

Companies start collecting data from the browsing and surfing through web pages.

Consumer’s data are also collected when filling the registration form, the cart, the stage

of payment until the stage of fill in the feedback ratings. The online site presents

various application forms to the surfer (Registration forms, …). Whereas, even without

applying these forms the user behavior is traced via cookies. The navigation process

and other information are usually traced in logs. The activities on the site are often

reported on information called Key Performance Indicators (KPI). This section

describes mainly cookies and solutions based on cookies.

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Cookie

Cookie is a text file (piece of javascript of code) created by the web server and stored in

the user’s web browser either temporarily or permanently. Cookies are used (anony-

mously) when the visitor is surfing through the different pages of a website. Later,

when the cookied visitors browse the Web, the cookie will let the retargeting provider

know when to serve ads, ensuring that ads are served to only to people who have

previously visited your site. There are different uses of cookies. Cookies provide a way

for the website to recognize the client and keep track of his preferences. Web servers

use also cookies to personalize content, serve visitors with relevant ads, and to analyze

traffic. The client can block or allow cookies in its browser. There are many cookie-

based technologies (for branding and conversion optimization tool). These technolo-

gies are effective when it is a part of a larger digital strategy. Retargeting tool relies on

collected data. We consider it as a marketing decision based on collected data.

Software Agents

Software agents are widely deployed in ecommerce context. Since the early 1990s,

cooperative multi-agent systems and intelligent agents are of increasing concern within

software engineering of large scale distributed systems (Wyai et al. 2018). Agents are

programs to which a user can delegate one or more tasks. They operate on behalf of a

user. Agent technologies can be applied to any of these areas where a personalized,

continuously running, semi-autonomous behavior is desirable. We cite agents of

interest, agent of search, negotiation agent..

Technologies Associated to the Agreement Phase

To provide negotiation services, the shopping site can implement various technologies

to communicate with consumers. The negotiation can be held with negotiation support

systems based on software agents. In the following, we focus on technologies used to

carry out communication between customers and the online shopping site.

We cite email, Chat, Forums, Chatbots, assistant robots in stores. Chat bots are

increasingly deployed in order to communicate with customers in collecting requests

and responding.

Technologies Associated to Settlement Phase

Payment Stage

Companies can offer to their consumers many payments options: electronic payment

systems, online credit cards, electronic wallets, etc. Electronic payment system needs to

be secure and fast. Digital payments are increasingly used as a tool by government to

create transparency and legitimacy. Mobile payment platforms and the payments

options may influence consumers on the selection of the seller.

Delivery Stage

In order to monitor product delivery, many technologies are deployed such as GPS and

IoT. In plus, various web-based solutions and mobile solutions are developed for

tracking and shipping (FedEx 2019).

The Internet of Things (IoT) could contribute significantly to improve product

delivery services (time delivery, quality of delivery, ..) because it allows for remotely

controlling the location and conditions of shipments and products. We cite as an

example perishable products (Verdouw et al. 2018). Many companies call for 3rd party

delivery services.

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Experience Evaluation

Various Post-purchase services can be provided,

for experience evaluation.

E-commerce sites offer various technologies such as feedback ratings, comments and

likes on social media, CRM (Customer Relationship Management) tools to commu-

nicate with the online shopping site, email, instant messaging and chat bots. Chat bots

are used by the online shopping site to enable clients to chat. The answers are made by

robots. They are based on Artificial Intelligence. So, the bot is learning from a

knowledge base. The business company can save costs and reduce time response.

Once the client is traced in the e-commerce site, software agents can inform cus-

tomers about promotions via multiple channels (social media, email address, applications

such as messenger, whatsapp,..). Emails newsletters are also used. In fact, conducting

email campaigns is largely deployed such as product recommendation emails. Baril-

liance’s study shows that there is an increase of 30% in conversion rates after adding

personalized product recommendations to the email newsletters (Barilliance 2018).

2.3 CRM Technologies

From a managerial perspective, EC technologies offer valuable opportunities to the firm

current value chain for enhancing inter-functional collaboration and efficiency in its

relationships with customers, suppliers and the main social economic and governmental

partners

Applications for supply chain management SCM and the customer relationship

management CRM are important for the growth of any e-commerce project. In addi-

tion, promotion management applications can help to plan and carry out promotions to

attract buyers.

Following the emergence of relationship marketing and the development of EC

technologies, this paper focuses on the important role of the CRM data in providing a

valuable source of competitive advantage and producing knowledge that guides

decision makers especially in commercial and marketing processes (Stein et al. 2013;

Lindman et al. 2012).

CRM Systems

CRM can be studied as a process, a strategy or a technology. Lefebure and Venturi

(2001) present CRM as “The management of customer relations combines technologies

and business strategies to provide customers with products and services that they

expect. The management of customer relationships is the ability to identify, acquire and

retain the best customers with the goal of increasing sales and profits.”. According to

this definition, the CRM systems and technologies are implemented to support the

business strategy processes.

The quality of the CRM system refers to the performance characteristics of a

system including reliability, flexibility, being user-friendly and response time. The

quality of the CRM system has a direct and indirect positive influence (via customer

satisfaction) on profitability (Khlif and Jallouli 2014).

The CRM architecture includes three segments namely: operational CRM, col-

laborative CRM and analytical CRM (Teo et al. 2006). Operational CRM focuses on

the daily management of a relationship with the client through the contact points

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(customer service, call center, sales force …). The collaborative CRM covers all the

communication and interaction channels with customers and partners as well as work

technology groups, such as workflow and e-mail. Finally, analytical CRM is the

integration and processing of data to produce useful information for the analysis of

customer relationships and project improvement (Chalmeta 2006).

As a conclusion, the CRM success is influenced by a dual value creation:

– Increasing profits with the identification of

the most profitable segments,

improvement of the performances of the sales force, customization of products and

services.

– Increasing the visibility and the quality of the information to all stakeholders thanks

to the integration of information in a single database (Krasnikov et al. 2009).

3 Data Analysis Capabilities

The previous section identifies the main EC technologies and classifies them according

to the stages of the website development, the transactional process and customer rela-

tionship management. This paragraph analyses the importance of data analysis capa-

bilities to unlock customer insights and orient in time marketing decisions based on data

collected with EC technologies.

Data science in its broadest sense is defined as “a multidisciplinary field that deals

with technologies, processes, and systems to extract knowledge and insight from data

and supports reasoning and decision making under various sources of uncertainty”

(National Academies of Sciences, Engineering, and Medicine 2017).

The data stored in companies and shared in social media is still growing at a high

speed. The challenge for managers is mainly to cope with the high volume, variety and

velocity of data. Core business systems such as marketing, finance and production

produce structured data. However, with the increasing number of audio and video

applications and the large participation of customers in social media to comment or

rank a product, a brand or a company, the proportion of unstructured data has increased

in a significant rate.

Structured and unstructured data have high commercial value. The challenge for

companies is therefore to develop the underlying data infrastructure in order to make it

more robust and agile and to extract consumer insights that enlighten the future

decision in marketing area (HBR Survey 2018).

Data treatment and analysis are based on Algorithm, Visualization, machine

learning or cognitive technologies as examples of tools that could help in extracting

customer knowledge to orient marketing decisions (Kumar 2018).

The application characteristics include the following steps: First, Data should be

queried or in some cases be moved between different platforms. Second, Data needs to

be summarized, grouped and sorted. Natural language processing and video analysis

are techniques that help to convert unstructured Data to structured Data. Data helps to

edit business indicator statistics, predictive analysis, deep data mining and exploration

reports. Finally, there are different timeliness of using such as different frequency of

index statistics, Ad-hoc query, and self-service data exploration (Song et al. 2018).

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Data science is a multidisciplinary field that deals with technologies, processes, and

systems to extract knowledge and insight. Data Analytic techniques include explora-

tory analysis, predictive analysis and prescriptive analysis (Strengthening Data Science

Methods Report 2017). Data sources are all types of data to support decision making

under various sources of uncertainty.

The survey of Harvard Business Review (2018) on real time analytics reveals three

relevant interrelated capabilities that guide consumer experience strategy:

1/Unified customer data platforms: This capability unifies mainly the company’s

customer data from the online and the offline channels.

2/Proactive analytics with machine learning and artificial intelligence: The purpose

is to incorporate insights on customers, marketing programs and related functions.

3/Contextual interactions: This capability integrates real time insights on digital and

physical costumer journeys to draw subsequent actions to pursue in the benefit of the

brand or the company.

The current key tools and approaches used in companies to orient strategic and

tactical decisions are the following: Segmentation tools, Survey-based choice models,

Aggregate marketing mix models, Pre-test market models, Marketing metrics, New

product models, Customer life time value models, Panel-based choice models, Per-

ceptual mapping, Customer satisfaction model, Sales force allocation models, Game

theory models and the Average Perceived Impact (Roberts et al. 2014).

A good example that shows the importance of developing new tools to treat the vast

quantities of panel scanner data and extract customer knowledge is the large use of the

logic modeling to guide responses to changes in the marketing mix (Roberts et al.

2014).

A second example of a trendy data analysis capability is data mining defined as the

process allowing a search, for valuable information, in large volumes of data. This data

search capability uses statistical algorithms, predictive modeling, forecasting and

descriptive modelling techniques and intelligent agent systems to uncover patterns and

correlations and extracts knowledge from corporate data platforms (Liao et al. 2012).

Data mining tools combined with CRM output could be an alternative to the

approaches and models already on offer to improve strategic decision-making and

tactical marketing activities.

The objective is orienting in time and contextual strategic decision either manually

or with the help of artificial intelligence.

4 Marketing Knowledge

Scholar journals in the field of marketing research focus on advancing our knowledge

by integrating new areas and exploring results confirmed in sister disciplines such as

psychology, economics, finance and information systems (Shugan 2004). The role of

EC technologies in the marketing research is growing significantly. Based on the

Marketing Science Institutes research priorities, the recent topics in Marketing man-

agement are the understanding of mobile marketing opportunities, the role of social

media and the harnessing of Big Data (Roberts et al. 2014). The study of the best

sellers’ textbooks of marketing shows the rise of the following topics: Digital and

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mobile communication in terms of access to markets and social networks, branding,

customer management and integrated marketing (Roberts et al. 2014).

The key marketing decision areas are: Brand management (Developing, positioning

and managing brands), New product/service management (Development, manage-

ment” and diffusion of new products), Marketing strategy (Product line, multi-product

and portfolio strategies), Advertising management (Spending, planning and design),

Promotion management, Pricing management, Sales force management (size, alloca-

tion, and compensation), Channel management (strategy, design, and monitoring),

Customer/market selection (Targeting decisions), Relationship management (Customer

value assessment and maximization, acquisition, retention), Managing marketing

investments (Organizing for higher returns and internal marketing) and the Service/

product quality management (Roberts et al. 2014).

Based on the previous sections on EC technologies and data analysis capabilities,

Fig. 1 provides an overview of the main EC technologies implemented in different

stages and the contribution of these technologies to guide marketing decisions.

The first stage of website development relies mainly on CMS and Web develop-

ment tools, Web services, Web design tools, Catalog design tools, Database applica-

tions, Hosting infrastructure and Cloud computing (IaaS, PaaS, SaaS). The key

marketing decision areas that could be guided with these technologies are Brand

management, new product/service management, marketing strategy, Advertising

management, Promotion management, Pricing management, Channel management

(strategy, design, and monitoring) and Customer/market selection.

The second stage concerning the electronic transaction is based mainly on the fol-

lowing technologies: cookies, Email campaigns, Ads channels, Email, instant messag-

ing, chat bots, Feedback ratings, social media, Agent-mediated platforms, the shopping

cart application, returns management application, contracting tools, GPS tracking, IoT

and Tracking delivery applications. These technologies and applications provide valu-

able source of structured and unstructured data. The shopping card applications, con-

tracting tools and GPS tracking and Tracking delivery applications are sources of high

value demographic, psychological and geographical characteristics of prospects and

customers. Agents, cookies and social media provide structured and unstructured data.

The firm needs proactive and contextual analytics to integrate real time insights on digital

and physical costumer journeys and draw subsequent actions related mainly to Brand

Management, New product/service management, Promotion management, Pricing

management, Sales force management, Channel management, Relationship manage-

ment and the Service/product quality management.

Finally, the third group of E-commerce technologies that are studied in this paper is

related to the relationship with customers via CRM technologies. Operational, col-

laborative and analytical CRM systems produce high potential of value creation by

integrating information in a unique database used by all the stakeholders. Data mining,

proactive analytics and contextual interactions are then capabilities that unify the

company’s data from the online and the offline channels to produce Marketing

knowledge via the identification of the most profitable segments, the performance

improvement of the sales force and the customization of the firm products and services.

Overview of E-commerce Technologies, Data Analysis Capabilities

191

Fig. 1. Overview of e-commerce technologies, data analysis capabilities and marketing

knowledge

5 Conclusion

Electronic commerce provides a great opportunity to small and medium-sized enter-

prises (SME) to improve their competitiveness within the global economy. With the

development of information technology, consumer behavior is constantly traced and

studied by researchers and developers. Business challenge is about how well it deploys

technology to build Market-Winning decisions. Therefore, relying on data analysis

capabilities of consumers’ personal data and consumer habits, needs and preferences, it

is possible to accurately grasp the needs of customers, build personalized customer

service systems and lead to higher conversions and long-term customer loyalty.

This paper presents an overview of the main EC technologies implemented in

different stages (Development, transactional, relational) and the contribution of these

technologies to make customer insights and orient marketing decisions. The paper

doesn’t include security solutions and technologies deployed in e-commerce such as

tokenization and blockchain.

As a final recommendation, this overview of e-commerce technologies in rela-

tionship with marketing knowledge highlights the importance of tight collaboration

between researchers from Computer science and Marketing fields to develop more case

studies and research papers that could be useful for a best-contextual data analysis

capabilities. From a teaching perspective also, marketing students need a basic

understanding of the e-commerce technologies and the analytical tools since they will

need to use these approaches throughout their career as marketers.

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