The Real Value of B2C ecommerce market

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The Real Value of B2C ecommerce market

The Real Value of B2C ecommerce market will be $3.2 trillion by 2020.

The global ecommerce market is expected to grow at a compounded annual growth rate of 17% from $1.3 trillion in 2014 to $2.5
trillion by the end of 2018. In the United States in the third quarter
of 2015, ecommerce generated $87.5 billion and accounted for 7.4%
of all retail sales (Rogers 2015). Ecommerce has been growing annually on average between 14% and 15% quarterly since 2014, while
retail growth has remained less 3%. comScore estimated that
U.S. consumers spent more than $57 billion online from November
I through December 31, 2015, up 6% from 2014. On Cyber Mon
day, U.S. consumers spent more than $2 billion online (comScore
2016). Alibaba, in China, reported more than $14.92 billion in goods
transacted” on one popular shopping day (Denale 2015). Amazon
Q3 2015 ecommerce revenue grew 23% from 2014 to more than $25
billion (SEC 2015). Frost and Sullivan predict that by 2020, the business to consumer (B2C) ecommerce market will be $3.2 trillion and
the business to business (B2B) ecommerce market nearly twice as
large at $6.7 trillion. Nearly $10 trillion in ecommerce revenue will
occur by 2020. Globally, the United State and China are the largest
ecommerce markets, accounting for more than 55% of ecommerce
sales in 2015. eMarketer estimates that by 2018, China’s ecommerce
marketing annually will be more than $1 trillion, with the U.S. likely
reaching $500 billion, followed by the U.K. at $124 billion and Japan
at $106 billion (Rogers 2015). Clearly, huge amounts of goods and
services are being transacted between businesses to consumers and
between businesses and other businesses globally.
Ecommerce analysis will continue to be an important activity for
generating such growth and new levels of revenue. Puma Gener
a 7% conversion lift using analytics. PBS increased conversions and
visits by 30% using analytics to track customer events in the funnel
WBC cited a 2% boost in conversion rate through customer segment
tation. Watchfinder claimed a 1,300x increase in ROI remarketing
based on analytics. Marketo claims a 10x higher conversion rate for
personalized campaigns using analytics. BT used conversion testing to
increase form completions by more than 60%. Amari Hotels increased
online bookings and sales by 44% by using analytics to optimize online
advertising (Google 2015). Companies that do ecommerce analysis
increase their business performance.
Ecommerce is transacted on pure-play B2C ecommerce sites
that have no physical storefront, such as Zulily, eBags, and Wayfair,
and by omnichannel B2C retailers, such as Walmart and Staples, that
have physical stores. Even pure-play ecommerce sites, like Amazon
and Warby Parker, are opening stores. As a result, existing companies
that already sell goods and services are now selling online and vice
versa. New companies are almost required by the market to have an
online presence. Although some companies that sell physical prod
ucts use an online presence only for branding to drive offline sales,
that’s increasingly rare. Even luxury brands are selling their goods
directly to consumers on retail sites. B2B ecommerce is even larger
than B2C ecommerce. Major global companies execute ecommerce,
such as Ford, GM, Coca-Cola, Chevron, IBM, General Mills, Kraft
Heinz, Exxon Mobil, General Electric, and Microsoft. The largest 300
B2B ecommerce companies were projected by eMarketer to grow
13.3% this year to $547 billion (from $483 billion in 2014)-figures
that easily eclipse the U.S. B2C ecommerce market.

Ecommerce isn’t just about the site anymore. The most popu
lar ecommerce sites have a mobile experience, whether mobile web
or mobile app. 30% of U.S. ecommerce sales in 2015 were gener
ated on a mobile device (Brohan 2015). Many ecommerce sites also
have physical stores. And in the future, ecommerce will be embed
ded into things and pervasive in Internet connected devices with
mobile payments just a touch away both online and in-store. Internet
Retailer predicts that in 2015 the U.S. mobile commerce sales will
total $104.05 billion, which is up 38.7% from $75.03 billion in 2014.
They estimate that mobile commerce in 2015 will now 2.5 times
faster than desktop ecommerce sudes, which they predict will grow
15% this year to an estimate $350,64 billion globally. Note that 109
of mobile customers leave an ecommerce site when it is not optimized
for mobile (Dorian 2015)
Ecommerce is an extremely competitive space. It takes huge
amounts of capital to even try to compete with the major commerce
players. This competition can create piczor the margin or revenue,
that can be driven primarily through discounting, Ecommerce can be
considered a zero-sum game. Thus companies are competing by ere
ating digital experiences that enable person to quality and easily find
and buy. Whether on a desktop, tablet, or mobile device, the compa
nies that are winning in ecommerce make it ensy and frictionless to
find the product or service desired understand how it fits the need
and buy it. Then these sites can compel their.customers to come back
again and again to buy more online and in-store. To do so, commerce
companies use marketing and advertising that is tightly coupled with
user experience that ladders up to a prospect or customer’s notion of
the brand and works to meet their intent, People come to commerce
experiences with certain goals in mind: to learn more about a product
by reading product information and social reviews, to compare prices
and promotions, and to purchase products. Ecommerce sites that win
at this zero-sum game cup match that intent to product and create
Commerce
Leading ecommerce companies use data and analytics to com
pete and they use a lot of different data to do so. Data is collected
and analyzed about who visits an ecommerce site, when they visit
what pages they view, and what site or source they came from the
referrer or marketing channel). Other information is also collected
about user behavior, such as user interactions and events on the site
data related to products viewed promotions used, pages visited time
spent, the different paths and clickstreams on the site, the queries
entered in search, and many other data points, such as the order
value, the price of products. the shipping method used, and the pay
ment information. Customer data may be captured or inferred, such
as who the customers are or could be, where they live, what they like
and their preferences or propensities, what they’ve bought and other
demographic and psychographic information
The idea of conversion”_where a prospect transitions to a pay
ing customer is embedded into the analytical DNA of the world
leading ecommerce companies. They staff entire teams and rum.com
prehensive programs for conversion testing and optimization. Mar
marketing channels and sources of traffic, such as organic and paid search
and various types of online advertising, are measured and tracked.
Higher-order consumer research around brand awareness, favor
ability, and consideration is performed. Customer data is analyzed
segmented, grouped into cohorts. modeled and understood using
financial measures, like the cost of customer acquisition and customer
lifetime value. Customer loyalty, retention, satisfaction and churn are
known and optimized. Merchandise products, orders, and transac
tions are analyzed from the site to the warehouse through to shipping
and fulfillment.
All this different quantitative and qualitative data about the entire
ecommerce experience and operations can be captured, measured.
and analyzed to improve business performance and make better deci
sions. Although tracking, measuring and analyzing all of this data may
sOund challenging and it is-it is possible to do Of course, doing so
isn’t easy. It requires investment in people, first and foremost, who
understand business, technology, and the process of doing analytics.
It also requires investment into different types of analytical tools and
technologies, including ecommerce platforms, business intelligence
tools, analytical platforms, and data science sandboxes. It might even
require the collection of new first-party data, the usage of second
party data, or the purchase of third-party data,
All of this data, the people and teams who work with and analyze it, and the technology supporting it represent powerful assets for
ecommerce companies to use to help run their business. But the data
must be collected and analyzed effectively and accurately for com
panies to use it to create better experiences, make better decisions.
drive conversion, satisfy and retain customers, and thus increase revenue
nue, growth, profitability, and value. The effective use of ecommerce
data and related data requires investing in the analytics value chain
from the technology to the people to the processes, governance
and change management necessary. Doing so can provide a mate
rial return on investment from analytics by converting more users
to customers and providing insights that can be used to improve the
customer experience. The return from analytical investment can also
come from improving marketing operations and tracking the cost and
return of marketing and advertising. The impact of merchandising
programs can be attributed to sales and other financial metrics. The
details of transactions, the metrics around products, and the key per
formance indicators related to the shopping cart can be understood
benchmarked, and targeted with goals. These methods for competing
with ecommerce data are entirely possible if you know how to succeed with ecommerce analytics.
Ecommerce analytics is the phrase used to describe business
and technical activities for systematically analyzing data in order to
improve business outcomes of companies that sell online. This broad
definition incorporates business activities such as the gathering of
business requirements, the execution of analytical programs and proj
ects, the delivery and socialization of business analysis, and the ongo
ing management of the demand and supply of analysis. The range of
business stakeholders demanding service in ecommerce companies
will run the gamut from the C-suite to the leaders of merchandising
buying, planning, marketing, finance, user experience, design. cns-
tomer service, inventory, warehousing, fulfillment and more
Ecommerce analytics also involves working with IT and engi
neering teams in the appropriate software and Internet development
lifecycle. It requires the analytical team to participate and possi
bly lead technical activities that are required to deliver or support
analysis, such as data collection, extraction, loading, transformation.
governance, security, and privacy. Ecommerce analytics can include
understanding and doing dimensional data modeling, working with
databases, handling data processing, creating and executing querying.
determining data lineage, participating in data governance commit
tees, acting as a data steward, working with and defining metadata,
and using tools to analyze data, create data visualizations, and do data
science and advanced analytics. All of this work occurs within a corpo
ate organization with its own culture and ways of working into which
the analytics team must integrate and learn to support and guide to
drive data informed business outcomes. Successful analytics often
requires rethinking and reorganizing the way a company is structured,
including new roles in the C-suite, such as chief analytics officers.
chief data officers, and chief data scientists.
Companies that are successful and effective with ecommerce ana
lyties ask business questions that can be answered with data, and then
they employ analytical teams that can collect and acquire data, govern
and operate analytical systems, manage analytical teams, and gender
ate analysis and data science that inform stakeholders. These compa
nies create value by asking questions, answering them with data, and
changing the way they take action as a result.
Many other questions can be asked to help guide and drive
business performance; ecommerce analytics leads to asking a lot of
questions. The analysis of ecommerce is complex not only because
it crosses both business and technology, but also because it is on the
forefront of digital experience and innovation. The site, mobile, and
connected ecommerce experiences online in 2016 are innovative,
fast, personalized contextual and powerful for guiding us to the right
product, at an appealing price, and then leading us through a pur
chasing process that is easy and frictionless. But in certain cases, the
opposite is true. Ecommerce sites and experiences have many oppor
tunities to improve. They may be hard to navigate or may make it dif
ficult to find product information. The trustworthiness of the site may
be in question. The experience of selecting products, adding them
to the eart, and stepping through the shipping and payment pages
may be problematic, confusing, or in the worst case dysfunctional for
the device or browser the person is using. In addition, the people
working at ecommerce companies may largely be unaware of these
problems because they aren’t getting timely, complete, and relevant
data and analysis to help improve the experience and increase conver
sion. Or ecommerce stakeholders might be suspicious of experiential
or customer issues but can’t prove them using data. Or there’s the
worst case, where no set of unified resources, technologies, or analyst
ics team exists to help stakeholders. What’s missing at these compa
nies that aren’t taking full advantage of the information and insights
in their data is solid, focused ecommerce analysis that helps business
stakeholders do their jobs better. Whether that job is to merchan
dise the site, improve the user experience. drive customer acquisition,
increase conversion, manage orders and fulfillment, or maximize cus
tomer profitability and shareholder value, ecommerce analytics can
be a successful competitive advantage.
This article was written to help both new and experienced ana
lysts succeed with ecommerce analytics. It was also written with the
understanding that people who work at ecommerce companies in
non-analytical roles, or who are simply interested in the topic, may
read this article. Thus it is structured to guide the reader into the topic
by first reviewing ways to think about doing ecommerce analytics
as part of what I call a “value chain.” Methods and techniques for
doing analysis are discussed in detail for both the new reader and
experienced analyst. Reporting, dashboarding. and data visualization
for ecommerce are explored. Data modeling is reviewed, including
a discussion about dimensions, facts, and metrics. Several chapters
are dedicated to detailing the what, why, and how of useful types of
ecommerce analysis executed for marketing, advertising, behavior,
customers, merchandising, orders, and products. The sciences of
conversion optimization and attribution are discussed. Guidance on
building effective and high-performing teams is provided. Data gov
ernance, security, and privacy of ecommerce data and what the future
holds for ecommerce analytics are deliberated. The comprehensive
scope of this article offers an experienced practitioner’s perspective
and viewpoint into ecommerce analytics across multiple dimensions
business, management, technology, analytics, data science, and the
ecommerce domain. Although more content and detail can always
be added in future volumes, the broad and ambitious subject matter
discussed is unprecedented. This article offers a view into ecommerce
analytics that hasn’t before been consolidated nor unified into one
source. Whether you read this article as a standalone text or in combination with my other articles, you will develop and enhance your
understanding of ecommerce analytics, the business and competitive
opportunities it enables, and how to use analytics to take advantage
of them.

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