Follow Datanami:
November 27, 2017

5 Ways Analytics Drives Cyber Monday Sales


Today is expected to be the busiest online shopping day in history, with more than $6 billion changing electronic hands. So how are businesses poised to take advantage of all that spending? One big factor for retailers that could make the difference between profit and loss is data analytics.

The end-of-year holiday shopping frenzy got off to a raucous start late last week, when consumers spent nearly $8 billion online over Thanksgiving and Black Friday. That spending spree is expected to crest today with Cyber Monday sales that should bring in $6.6 billion, marking a single-day online shopping record, according to Adobe Analytics, which tracks spending at the majority of the largest retail websites.

While brick-and-mortar sales remain important to retailers, online sales are growing much faster. Adobe Analytics predicts that online sales today will be 16.5% higher than what retailers brought in for Cyber Monday last year. While Amazon and Wal-Mart battle for the title of low-price leader, there’s plenty of room for other retailers to differentiate themselves and their offerings through the power of big data analytics.

Here are five ways that analytics can boost your online sales this holiday season:

Personalize the Experience

In the early days of online shopping, everybody had the same digital experience atop the same static web pages. Converting the browsers into buyers hinged on old-fashioned sales tactics, like organizing the merchandise into familiar categories. A good search engine was also a must-have.

Today’s savvy online shopper demands much a much more sophisticated experience, not the least of which is having a mobile-friendly website. Delivering a personalized experience to individual users is considered one of the top ways to grab business in a super-competitive retail environment.

According to a report from MindTree, retailers should enthusiastically gobble up the “rich digital body language” that consumers are offering. This treasure-trove of data will provide the clues as to how retailers should personalize the experience for shoppers, such as through targeted promotions, relevant “related items” displays, and recommendations from peers.

However, companies may not be doing enough to improve personalization. MindTree says decision makers are spending more on providing multiple ways to pay, better shopping carts, and shopping list functionality than investing in personalization.

Review Models Often

If you’re using big data analytics to optimize your ecommerce operation, you undoubtedly have developed various model to help you forecast aspects of your business, including how to price items, when to make personalized offers, and whether fraudulent activity is occurring.

Don’t sit on these models for too long, as they can go stale quite quickly. Take the experience of Adobe Analytics, which runs one of the retail industry’s most sophisticated modeling operations. According to Adobe Analytics’ Tamara Gaffney, the company has had to reassess its assumptions based on fast-changing consumer behavior.

Holiday spending is condensing around big sale days, according to Adobe Analytics

“Consumers have become conditioned to wait for big discounts and global markets are shifting hundred year old behavioral patterns in just two years,” Gaffney writes in a recent blog post. “Given all this dramatic change, as data scientists we realized it was time to rip our model down to the studs and rebuild.”

Recent historical data shows online holiday buying patterns have changed dramatically in just the last two years, Gaffney says. The biggest change, she says, is that actual spending is being concentrated around a handful of big shopping days, including Thanksgiving, Black Friday, and Cyber Monday (with Amazon Prime Day being another big draw).

“For data nerds like the Adobe Digital Insights team, this process has yielded many exciting opportunities to uncover both macro and micro changes to the marketplace and report them in greater detail than ever,” Gaffney writes. “We can’t wait to see how retailers – small and large – are adjusting to these changing market dynamics to make sure they don’t become a victim of the so-called retail apocalypse.”

Retain the Customer

It’s a well-known fact that keeping the customers you have is much more cost-effective than finding new ones. According to a story in Harvard Business Review, finding new customers costs anywhere from five to 25 times more than keeping old customers.


So how does big data analytics factor into the picture? It can make an impact in a number of ways, including detecting which of your existing customers are about to jump ship, or “churn.” Delivering a targeted deal (i.e. personalized offer) to a potentially unhappy customer is a good way to bring them back into the fold.

So is using predictive analytics to make the overall shopping experience fast and easy, says Mark Morley, director of strategic product marketing at OpenText.

“A huge factor with competition is customer retention, especially during the holidays,” Morley says. “Customers want a fast and easy experience with shopping, and are likely to move on from one retailer to the next if they can’t easily locate items on their list.”

Security and Infrastructure Monitoring

We’ve had a bumper crop of security breaches this year, with millions of records lost by the likes of Equifax, Yahoo, Verizon, and Uber, which last week says it lost data potentially on 57 million customers and drivers.

Nobody wants to do business with a company that has lax security procedures in place and runs a high risk of losing its customer data. In addition to shoring up server settings, ensuring all configurations are up to snuff, and practicing good data hygiene, a retailer can use the combination of big and granular transactional data and high-powered advanced analytics to flush out the fraudsters.

One of the most common uses for machine learning models is detecting potentially fraudulent transactions, which in turn can improve the overall security of an ecommerce site. Gartner sees user and entity behavioral analytics (UEBA) emerging as a way to improve the detection of security breaches by correlating data collected from various endpoints.

Big data analytics can also help detect potential IT issues before it crashes the server — and takes all those sales with it.

“During the peak shopping season, any glitches that occur can cost an order of magnitude more than during the rest of the year,” said David Drai, founder and CEO of Anodot, which develops software for performing time-series analytics. “For retailers, the critical holiday season means online shopping spikes, and glitches can lead to larger losses, both monetarily and reputationally.”

Drai says online retailers are using his company’s software to identify and rectify glitches in real time, such as $2,000 televisions being sold for $200 or even a bad batch of SKUs causing a dip in mobile sales. to avoid losing revenue and prevent brand damage.  Spotting such events as they occur can next to impossible using traditional dashboards, which is why real-time analytics is needed, says Drai.

Inventory Visibility

Every holiday, there are a handful of items that get hot and sell out quickly. Every retailer is keeping an eye out to see what will fly off the shelves – or heat up the distribution center – this year.

(Don Pablo/Shutterstock)

Analytics can play a role by providing better visibility into the consumer goods supply chain. It’s all about digging deeper to get more granular data in closer to real time, according to OpenText’s Morley.

“Retailers have to integrate technology behind the scenes that provides real-time visibility into inventory and real-time analysis into purchasing behavior if they want to keep customers happy and see their shopping carts – physical and virtual – through checkout,” he says.

Related Items:

How Retailers Use Big Data to Gobble Up Sales

9 Ways Retailers Are Using Big Data and Hadoop