The role of big data and machine learning on Black Friday

Machine learning and big data drive the sophisticated strategies that lead to Black Friday success.

Each Black Friday, online and brick-and-mortar retailers seek to kick off a successful holiday shopping season through strategically targeted sales. Machine learning algorithms make these sophisticated cmampaigns possible by using big data to predict demand, even as it changes through consumer behavior.

These strategies – that companies also apply on Cyber Monday and throughout the year – are vital to their success. The insight data scientists gain from applying machine learning to big data includes:

• Predicting demand
• Targeting customers with personalized recommendations
• Customer retention

These tactics and others made possible by data scientists, are essential to building a profitable Black Friday campaign.

Interested in making your impact through machine learning, start learning how with Meharry’s master’s programs in data science.

The History of Black Friday

Black Friday has not always described the most popular shopping day on the calendar. In fact, it has an auspicious history. In 1896, Black Friday described a market crash caused by a sharp drop in gold prices. Bond writes that the Philadelphia Police Department, concerned about the traffic jams and sidewalks crowded by frenzied shoppers, used the term for the day after Thanksgiving in the 1960s.

The following years featured more and more post-Thanksgiving, Black Friday marketing campaigns. Today, we commonly recognize Black Friday as the day after Thanksgiving. But more specifically, shopping for great deals. Stores will target consumers with increasingly sophisticated ads. Online and brick and mortar retailers have begun to not only assess past consumer behavior, but predict future demand. But how are they able to anticipate that demand?

Big data and Black Friday shopping

Consumer activity creates a massive volume of data commonly known as big data. This data includes:

• Credit card purchases,
• Google searches,
• product searches on websites like Amazon, and
• social media behavior.

This big data is useful for understanding past activity, but is most powerful when applied toward predictive analytics.

Data scientists make those insights possible. They collect, clean and organize that data. Then, they analyze it to identify relationships that indicate the likeliness that consumers will purchase specific items.

Swanson explains:

“Items are ranked for appeal based on how consumers have engaged with those products in the past, where they fit into current purchasing trends, available inventory, and whether similar items have sold well during previous Black Fridays. The discount amount itself is determined based on predicted sales coupled with the likelihood customers will purchase at a certain discount and how much additional volume is required to offset the revenue reduction from that discount.”

But, you may ask, consumer behavior is always occurring. How can a company adopt the constant stream of incoming data? Data scientists create models that are able to adapt and learn from those steams of data through machine learning algorithms.

What is machine learning?

Long Nguyen, Ph.D., assistant professor of computer science and data science, defines machine learning as a method that helps a machine imitate human intelligence, without explicit instructions, through data analysis and model building.

These models, Dr. Nguyen explains, can be very powerful.

“The machine learning model one builds should have sufficient intelligence to perform complex tasks that are close to or beyond human-level capability,” explains Dr. Nguyen.

We encounter machine learning through several applications in everyday life.

“Typical applications of machine learning are the self-driving car, email spam filters, and preventing bank fraud. We will usually observe machine learning applications whenever there is intelligence-driven behavior without human intervention,” says Dr. Nguyen.

But, as it relates to Black Friday, most consumers experience machine learning through recommendation systems. The model analyzes previous purchases to predict what consumers will want to purchase next. These recommendations are often in-sync with the interests of consumers, offering helpful suggestions rather than traditional advertising methods that can be more invasive.

Dr. Nguyen explains:

“Machine learning will help create recommendation systems that suggests products to users that they are likely to purchase right away. A store owner can use it to arrange products on shelves so that customers will find them quickly, which ultimately results in increasing sales.”

Machine learning and data scientists

Machine learning is a skill in the field of data science. A data scientist is a highly-trained technology professional with expertise in statistics, math and computer science. Their ability to use big data to create new information makes them highly attractive to employers in several industries.

For example, look at the following data science salaries from Galavanize.com. The following ten companies have the highest average compensation for data scientists, adjusted for cost-of-living index.

1. Oracle, $186,000
2. Walmart, $218,000
3. Nvidia, $222,000
4. Airbnb, $228,000
5. PayPal, $189,000
6. Lyft, $200,000
7. Cisco, $199,000
8. Apple, $203,000
9. Snowflake, $186,000
10. Twitter, $187,000

It is no surprise that popular retailer and Black Friday shopping destination, Walmart, is near the top.

Learn more about data science salaries, as well as how different industries apply data science.

Machine Learning and Black Friday

Retailers certainly want to drive traffic to their website or store. However, it is more important that visitors are likely to make a purchase. Machine learning can support that need efficiently. Companies can automate tactics like lead scoring and nurturing to help businesses reach their key performance indicators.

As Blitz explains, this means “. . .  you can make incredibly accurate predictions about a prospective customer’s lifetime value (CLTV), return on marketing investment (ROMI), likelihood of canceling their subscription, and a thousand other factors before you decide whether to commit a cent to showing them an ad.” This sophisticated targeting, made possible through machine learning, will reduce costs and achieve a more personalized approach that is more appealing to customers.

Next steps

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Sources

Andrews, Evans. The “Black Friday” Gold Scandal. History.com. Sept. 24, 2014. Accessed Nov. 18, 2021. https://www.history.com/news/the-black-friday-gold-scandal-145-years-ago

Blitz, Shelby. Is Your Marketing Machine Ready for Black Friday and Cyber Monday? ML Can Help. Exporium.ai. Nov. 2, 2020. Accessed Nov. 18, 2021. https://www.explorium.ai/blog/is-your-marketing-machine-ready-for-black-friday-and-cyber-monday-ml-can-help/

Bond, Casey. “Black Friday History: The Dark True Story Behind The Name.” HuffPost.com. Nov. 17, 2020. Accessed Nov. 18, 2021 https://www.huffpost.com/entry/black-friday-history-why-is-it-called-black-friday_l_5d951322e4b02911e1154386

Fleming, Nic. “How artificial intelligence is changing drug discovery.” Nature.com. May 30, 2018. Accessed March 8, 2021. https://www.nature.com/articles/d41586-018-05267-x

Juhasz, Kevin. Top 10 paying companies for data scientists in 2021. Feb. 4, 2021. Accessed Nov. 18 2021. https://blog.galvanize.com/top-10-paying-companies-for-data-scientists-in-2021/

Swanson, Ian. The science behind Black Friday and Cyber Monday pricing. DigitalCommerce360.com. Nov. 12, 2016. Accessed Nov. 18 2021. https://www.digitalcommerce360.com/2016/11/12/science-behind-black-friday-and-cyber-monday-pricing/