Cyber Monday is expected to be a key component of a happy online holiday for retailers.
According to Adobe’s online shopping predictions for the upcoming 2019 holiday season (Nov. 1 – Dec. 31), U.S. online sales will increase 14.1% year-over-year to $143.7 billion. Total online and offline holiday retail spending is predicted to rise 4% over last year.
As part of this substantial online sales growth, Cyber Monday will set a new record as the largest- fastest-growing online shopping day of the year as sales skyrocket 18.9% to $9.4 billion. Online sales between 7 p.m. and 11 p.m. PT on Cyber Monday are expected to drive over $3 billion in revenue, with sales conversions nearly doubling during this four-hour window.
Thanksgiving Day sales are expected to increase by 19.5%, generating $4.4 billion. One out of five dollars this holiday season will be spent during Cyber Week between Thanksgiving Day and Cyber Monday, generating $29 billion or 20% of total online revenue this season.
With 22 days between Cyber Monday and Christmas Day, there are six fewer days of peak holiday shopping days than 2018, translating into almost $1 billion of potential revenue lost due to the abridged time period. According to Adobe, the compressed shopping calendar means that retailers will begin sales earlier than ever before, with each day in November and December surpassing $1 billion in online retail sales for the first time.
Additional predictions include:
- Best days for deals: Black Friday will be the day to pick up the best discounts on appliances (discounted by 9%) and sporting goods (6%). Dec. 1 will be the key deal day for toys (32%) and computers (18%). Cyber Monday (Dec. 2) will see the deepest discounts on TVs (19%), while furniture & bedding (10%) and tools & home improvement items (6%) will be the categories offering the best savings on Dec. 3. Dec. 27will see massive savings of 27% on electronics items.
- A shopping mall in your pocket: Americans will spend $14 billion more on their smartphones compared to last year, accounting for 36% of all online sales, which represents a 20% increase in share year-over-year, and 57% of visits, an 11% increase. With retailers optimizing for mobile, Adobe forecasts online spend on smartphones will increase from 30 to 47 cents per minute, a 63% jump since 2016. However, Adobe says consumers will continue to use desktops to make research-heavy purchases like furniture, electronics and appliances, resulting in a 28% higher average order value than on smartphones.
- A BOPIS bonanza: (Buy-online-pickup-in-store) BOPIS orders have experienced strong growth, with 39% more BOPIS orders expected to be placed this year compared to 2018. BOPIS share of revenue is anticipated to double during the week before Christmas as panic buying sets in. Thirty-seven percent of consumers said they are planning to use BOPIS this season, while 82% of BOPIS shoppers say they will likely shop for additional items when picking up their online order.
- Larger retailers win the retail battle: While online giants will see revenue increase by 65%, smaller retailers will only see a 35% boost. According to Adobe, large retailers (annual online revenue of over $1 billion) will be the clear winners on Black Friday and Cyber Monday, with smaller retailers (less than $50 million annual online revenue) failing to drive strong online traction. Additionally, large retailers are expected to benefit from higher conversion rates, with customers visiting their sites 32% more likely to convert versus smaller retailers (23%).
- Mad for ads: Fifty percent of consumers state that ads during the holiday shopping season impact their purchasing decisions while email continues to be the most preferred way to get an offer while holiday shopping. Smartphone visits to retail sites from social media have tripled in the past three years from 4% to 11%. However, visits coming from social platforms result in lower conversions compared to other channels like search or email.
Adobe leverages Adobe Sensei artificial intelligence and machine learning technology to identify retail insights from trillions of data points.