IBM Predicts Record Mobile Holiday Shopping as Consumers Browse and Buy on the Go

• Total online sales to increase 15 percent over five-day holiday shopping period
• For the first time, more than half of all Thanksgiving Day online shopping, will come from a mobile device
• Apple iOS to drive twice the mobile traffic of Android, four times the sales

Armonk, NY - 05 Nov 2014: IBM (NYSE: IBM) today released its predictions for the 2014 holiday shopping season based on billions of online and in-store transactions analyzed by the IBM Digital Analytics Benchmark and IBM Quarterly Retail Forecast.

IBM projects another strong shopping season with online sales projected to increase 15 percent over the five-day period between Thanksgiving and Cyber Monday.  The biggest increase in online sales is expected on Cyber Monday, predicted to grow 15.8 percent, followed closely by Thanksgiving with a projected increase of 15.6 percent. Still widely considered the busiest day for in-store shopping, Black Friday online sales are expected to grow 13 percent as consumers find the best deals with their fingers as well as their feet. 

A primary driver of online growth, mobile browsing is expected to account for 48.2 percent of all online traffic over the five-day period, an increase of 23 percent over last year.  Mobile sales are also expected to rise, accounting for 24.4 percent of all online sales, up 9.5 percent year-over-year.  Apple’s dominance in mobile shopping experiences is also expected to continue with iOS device traffic projected to double that of Android devices, and sales expected to quadruple.

Today’s predictions are based on historical and real-time trend data analyzed across hundreds of U.S. retail websites.  The resulting online shopping insight is based on data from IBM Digital Analytics Benchmark which, in its seventh year of holiday reporting, tracks more than 370 performance indicators – helps retailers and marketers benchmark themselves against industry peers while driving more targeted customer engagements. 

Other key predictions for this year’s U.S. holiday shopping season include:

  • Mobile is the New Thanksgiving Tradition1:  For the first time ever, IBM predicts more than half of all online shopping on Thanksgiving, roughly 53 percent, will come from a mobile device, up 23 percent year-over-year. Mobile sales are also expected to grow, reaching 28 percent of all Thanksgiving online sales, an increase of more than 9 percent over 2013. 
  • More Digital Coupons, Greater Savings for Consumers1As consumers become more comfortable with digital couponing, IBM predicts shoppers will save dollars this holiday season as they cash in on online deals. Consumers will spend on average $123.28 per online order over the five-day holiday period, a decrease of 2.9 percent over 2013.  At the same time, the average number of items included in those purchases will be 4.4 items per order, an increase of 17 percent year-over-year. 
  • Retailers Give the Gift of Less Spam2: IBM predicts click-through rates for emails sent during the five-day shopping period will be 10 percent higher than the same period last year, thanks to data-driven insight which allows marketers to reduce the amount of unwanted email and instead, deliver personalized and relevant promotions. The company also estimates that 35 percent of all click-throughs will happen on a mobile device.  The highest volume of emails is expected on Cyber Monday. 
  • Smartphones Browse, Tablets Buy1:  Smartphones will continue to lead in mobile browsing over the five-day shopping period, accounting for 29 percent of all online traffic versus 15 percent for tablets.  However, IBM predicts tablets will account for twice as many mobile purchases than smartphones thanks to the larger screen size.
  • In-store Growth Led by Health and Beauty Gifts3:  The retail industry can look forward to strong holiday in-store sales with four percent growth predicted during the November and December shopping period.  Health and Beauty products are expected to lead the way with 4.2 percent and 4.7 percent growth, respectively, followed by women’s clothing at 2.61 percent.

“Without question, this will be a strong holiday shopping season, supported by the power of Big Data and analytics, which are helping brands better understand their consumers and make crucial decisions in real-time,” said Jay Henderson, Director of Strategy, IBM ExperienceOne. “Regardless of industry or time of year, the data the IBM Digital Analytics Benchmark provides, is an example of the type of advanced analytics and cloud-based technologies competitive brands rely on to improve consumer engagements and overall performance.”

The insight provided through the IBM Digital Analytics Benchmark and IBM Retail Quarterly Forecast gives retailers and marketing professionals access to data-driven insight to drive more personalized and targeted customer engagements. 

To learn how IBM’s real-time analytics delivered through the cloud can improve end-to-end customer engagement, visit  To join the conversation, follow hashtag #smartercommerce on Twitter. Stay up-to-date on the latest holiday trends by visiting:


Data Sources

1 Delivered through IBM ExperienceOne, IBM’s Digital Analytics Benchmark analyzes hundreds of terabytes of real-time shopping across participating U.S. websites.  The resulting insight – based on 370 performance indicators – helps retailers benchmark themselves against industry peers while driving more targeted customer engagements. 

2 Denotes Silverpop-specific data not included in the IBM Digital Analytics Benchmark report. Silverpop, an IBM Company, is a cloud-based digital marketing provider that offers email marketing and lead management solutions. The research examined messages sent by Silverpop's global client base, combining data from a variety of brands and message types. Messages include promotional emails, content-based newsletters, notifications and transactional emails.

3 The IBM Quarterly Retail Forecast applies patented algorithms to data from the US Census Bureau, which captures total sales observations from over 46,000 North American retail establishments monthly from 1992 to the present, to model seasonal peaks and patterns. This depth of data allows IBM to predict in-store trends with 99 percent accuracy.

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