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 than 3%. comScore estimated that U.S. consumers spent more than $57 billion online from November 1 through December 31, 2015, up 6% from 2014. On Cyber Monday, 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’s 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 generated 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 12% boost in conversion rate through customer segmentation. 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 products 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, ExxonMobil, 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 popular ecommerce sites have a mobile experience, whether mobile web or mobile app. 30% of U.S. ecommerce sales in 2015 were generated on a mobile device (Brohan 2015). Many ecommerce sites also have physical stores. And in the future, ecommerce will be embedded 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 grow 2.58 times faster than desktop ecommerce sales, which they predict will grow 15% this year to an estimated $350.64 billion globally. Note that 30% 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 ecommerce players. This competition can create razor-thin margins or revenue that can be driven primarily through discounting. Ecommerce can be considered a zero-sum game. Thus companies are competing by creating digital experiences that enable a person to quickly and easily find and buy. Whether on a desktop, tablet, or mobile device, the companies that are winning in ecommerce make it easy 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, ecommerce companies use marketing and advertising that is tightly coupled with a user experience that ladders up to a prospect or customer’s notion of the brand and works to meet their intent. People come to ecommerce 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 can match that intent to a product and create commerce.
Leading ecommerce companies use data and analytics to compete—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 payment 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 paying customer—is embedded into the analytical DNA of the world’s leading ecommerce companies. They staff entire teams and run comprehensive programs for conversion testing and optimization. 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, favorability, 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 transactions 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 decisions. 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 companies to use it to create better experiences, make better decisions, drive conversion, satisfy and retain customers, and thus increase revenue, 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 material 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 performance 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 projects, the delivery and socialization of business analysis, and the ongoing 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, customer service, inventory, warehousing, fulfillment, and more.
Ecommerce analytics also involves working with IT and engineering teams in the appropriate software and Internet development lifecycle. It requires the analytical team to participate and possibly 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 committees, 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 corporate 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 analytics 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 generate analysis and data science that inform stakeholders. These companies create value by asking questions, answering them with data, and changing the way they take action as a result. Business questions for ecommerce can include the following:
• Customers: What are the characteristics of my most loyal customers? Least loyal?
• Marketing: How do customers perceive our company and products?
• Categories and products: Which products drive the most sales or highest gross margin? Which products are frequently purchased together?
• Price and promotions: What impact do discounts and promotions have on overall sales?
• Omnichannel sales: How are my channels performing and how do they complement each other on the path to purchase? How does this differ from attribution?
• Prospects and customers: Which prospects should I target to convert into loyal customers? What products or offers would be most effective?
• Optimization and prediction: What parts of my site should I test? What products should I order now to match sales forecasts?
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 purchasing process that is easy and frictionless. But in certain cases, the opposite is true. Ecommerce sites and experiences have many opportunities to improve. They may be hard to navigate or may make it difficult to find product information. The trustworthiness of the site may be in question. The experience of selecting products, adding them to the cart, 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 conversion. 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 analytics team exists to help stakeholders. What’s missing at these companies 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 merchandise the site, improve the user experience, drive customer acquisition, increase conversion, manage orders and fulfillment, or maximize customer profitability and shareholder value, ecommerce analytics can be a successful competitive advantage.
This book was written to help both new and experienced analysts 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 book. 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 governance, security, and privacy of ecommerce data and what the future holds for ecommerce analytics are deliberated. The comprehensive scope of this book 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 book offers a view into ecommerce analytics that hasn’t before been consolidated nor unified into one source. Whether you read this book as a standalone text or in combination with my other books, Building a Digital Analytics Organization and Digital Analytics Primer, 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.