Overview of E-commerce Technologies, Data Analysis Capabilities and Marketing Knowledge

ting provides the network platform, the infrastructure with high performance also as services.

Moreover, collected data are stored and performed 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 environ-

ment. 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 Market-

place which focus on the BtoB context (Schmid & Lindemann, 1999; 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 require-

ments 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 suitabil-

ity of a product to customer needs. From the business view, it is important to attract consumers.

This phase includes marketing and catalog management. 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 fulfillment, the de-

livery 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 transac-

tion process.

Technologies deployed in the Information phase.

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

er’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.

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 (anonymously) 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 rele-

vant 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 tech-

nologies 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, coopera-

tive multi-agent systems and intelligent agents are of increasing concern within software engi-

neering of large scale distributed systems (Bodendorf et al. 2007). Agents are programs to

which a user can delegate one or more tasks. They operate on behalf of a user. Agent technolo-

gies 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 commu-

nication 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 sys-

tems, 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 ser-

vices (time delivery, quality of delivery, ..) because it allows for remotely controlling the loca-

tion and conditions of shipments and products. We cite as an example perishable products

(Verdouw, 2018). Many companies call for 3rd party delivery services.

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 communicate 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 Intelli-

gence. 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 customers 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. Barilliance’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 cur-

rent 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 addition, promotion manage-

ment 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, Smith and Lancioni, 2013; Lindman and 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 tech-

nologies are implemented to support the business strategy processes.

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

ing reliability, flexibility, being user-friendly and response time (DeLone and McLean, 1992).

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

faction) on profitability (Khlif and Jallouli, 2014).

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

tive CRM and analytical CRM (Teo and al, 2006). Operational CRM focuses on the daily man-

agement of a relationship with the client through the contact points (customer service, call cen-

ter, sales force ...). The collaborative CRM covers all the communication and interaction chan-

nels 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 infor-

mation 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 defines the main EC technologies and classify them according to the stag-

es of the website development, the transactional process and customer relationship manage-

ment. This paragraph analyses the importance of data analysis capabilities to unlock customer

insights and orient in time marketing decisions based on data collected with EC technologies.

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 participa-

tion of customers in social media to comment or rank a product, a brand or a company, the pro-

portion 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 cog-

nitive 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 timeli-

ness of using such as different frequency of index statistics, Ad-hoc query, and self-service data

exploration (Song et al, 2018).

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 in-

corporate insights on customers, marketing programs and related functions.

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

cal 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 deci-

sions 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, Perceptual 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 quanti-

ties 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 (Weiss & Indurkhya,

1998). 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, 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 in-

tegrating 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 re-

search priorities, the recent topics in Marketing management 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 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, management" and diffu-

sion 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, allocation, and compensation), Channel manage-

ment (strategy, design, and monitoring), Customer/market selection (Targeting decisions), Rela-

tionship 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, Figure 1

provides an overview of the main EC technologies implemented in different stages and the con-

tribution of these technologies to guide marketing decisions.

The first stage of website development relies mainly on CMS and Web development tools,

Web services, Web design tools, Catalog design tools, Database applications, Hosting infra-

structure 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 following

technologies: cookies, Email campaigns, Ads channels, Email, instant messaging, chat bots,

Feedback ratings, social media, Agent-mediated platforms, the shopping cart application, re-

turns management application, contracting tools, GPS tracking, IoT and Tracking delivery ap-

plications. These technologies and applications provide valuable source of structured and un-

structured data. The shopping card applications, contracting tools and GPS tracking and Track-

ing delivery applications are sources of high value demographic, psychological and geograph-

ical characteristics of prospects and customers. Agents, cookies and social media provide struc-

tured 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, collaborative and analyt-

ical 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 seg-

ments, the performance improvement of the sales force and the customization of the firm prod-

ucts and services.

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 enterprises

(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 cus-

tomers, 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 stag-

es (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 relationship 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 market-

ers.

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