Historically, millions of Americans took advantage of Black Friday and Cyber Monday sales. This made Thanksgiving one of the most important times of the year for retailers. As the internet proliferated Black Friday and Cyber Monday deals abroad, maximising profit this time of year became vital for companies everywhere.
This year, Black Friday sales have become even more fraught. According to McKinsey, deep discounting has been a primary survival mechanism for companies in the wake of the Covid-19 pandemic. Retailers have already slashed prices to Black Friday and Cyber Monday levels with mixed results. On the oft-proclaimed biggest shopping weekend of the year, no one can afford to lose money. Sales have to be done right or already struggling businesses will face real trouble.
Unfortunately, many companies have always struggled to approach Black Friday sales strategically: a fact that doesn’t bode well post-Covid. More brands are offering massive discounts than ever before, yet fewer people are buying. 57% of buyers are anxious about the idea of Black Friday purchases, a number that’s been trending upwards for weeks.
Even worse for retailers, 61% of consumers have no plans yet to shop on Thanksgiving weekend at all: price-sensitive customers are temporarily gone, therefore cutting prices works less than in the past.
Companies have long tried to use historical sales data to predict optimal discounting and stock allocation strategies with mixed success. Post-Covid, these failures are magnified. Markdown strategy errors could previously be balanced by the overwhelming increase in foot traffic at stores. Last year, however, only 20% of shoppers did all their Black Friday and Cyber Monday shopping in person. Fewer are expected to venture outside in a pandemic. The margin for error has evaporated.
While Black Friday and Cyber Monday present businesses with opportunities to increase sales and revenue, a poorly executed Black Friday strategy can lead to stock shortages and waste — or even lost profits due to unbalanced discounting strategies.
3 ways to maximise Black Friday profits using data
Companies who poorly allocate stocks or over-discount are now less likely to make a profit. Even in the best cases, these retailers miss out on profits on one of the biggest shopping weekends of the year.
Making Black Friday and Cyber Monday as predictable as possible is vital for retailers hoping to compete against online monoliths.
Luckily, big data has made it possible for businesses to more accurately predict the prices and inventories most likely to maximise Black Friday and Cyber Monday profits, even amidst the chaos of a pandemic.
1. Data-based pricing strategy, not customer grab
It’s easy to look at Black Friday and Cyber Monday as an isolated phenomenon, but the reality is that Thanksgiving weekend kicks off the holiday shopping season. On average, Black Friday shoppers have only completed about half of their holiday shopping by the end of Cyber Monday. This means that a pricing strategy can ensure that the limited-time discounts offered over Black Friday and Cyber Monday are balanced over the entire quarter, maximising profits over the holiday season and entire year.
In fact, historical data, as well as industry data and other data from outside sources, can be leveraged within pricing algorithms to determine the prices for a retailer’s entire inventory that will produce the highest returns. Slightly increasing prices on certain high-interest products that customers may be tempted to purchase at the same time or recommending products related to discounted items can compensate for low prices. The key is to carefully analyse the long-term impact of any pricing decision beyond the Black Friday weekend.
Don’t just grab for customers because Black Friday promises volume. In a tough economy, attention won’t necessarily lead to profits without a big-picture strategy.
2. Forward inventory and supply chain decisions
Determining the ideal inventory to have in each retail store and in warehouses that fulfil online purchases on Black Friday weekend poses a unique challenge for retailers. Stocks must be large enough to meet surging demand in a way that maximises profit, yet stores cannot be hampered by wasteful excess inventory.
Not only can a misallocation directly hurt a company’s profits, but it can also impact the bottom line in less direct ways. Excess inventory hinders sustainability goals, causing significant supply chain waste. The brand is diluted when products significantly discounted on Black Friday and Cyber Monday must stay discounted or be slashed further due to lower-than-expected sales. Poorly allocated inventory must be later moved to more appropriate locations, causing shipping and labour costs to rise.
The best solution is obviously to optimise the supply chain before products are distributed to stores. But how can businesses successfully predict buying behaviour on Black Friday and Cyber Monday in a post-Covid world? They must rely on predictive analytics that doesn’t just report best guesses based on past performance. Accurate sales predictions must take future trends and the current pandemic realities into consideration.
At Evo, for example, our algorithms include data on the buying behaviour of 1.2 billion consumers. The goal is to understand how consumers are behaving right now to more appropriately replenish inventories while minimising waste. Even in these uncertain times, such large numbers ensures that you’ll have enough data to make informed, data-driven decisions.
No matter what system a company uses to determine stocks on Black Friday weekend, it must be automated. Automated models can respond to new data quickly to prioritize purchase trends, changes in customer behaviour, item popularity and profit margins with a view for the future in order to increase profit in a predictable way. If the decision-making process ignores any element, ROI quickly diminishes. A model can handle this complexity; a human alone cannot.
3. Joint discounting and allocation strategies
It’s easy to assume that decisions on Black Friday and Cyber Monday discounts can be made independently of inventory allocation considerations or that such supply chain decisions depend solely on established pricing decisions. Unfortunately, either assumption will reduce overall ROI. In order to increase profits in a predictable manner, companies must take a holistic approach to both pricing and inventory allocation.
Maximising ROI depends on optimising both the discount offered and the volume of the product sold: prices are only sustainable if the predicted sales on the discounted product lead to sufficient overall profit. Supply chain decisions are a vital element in the equation.
Retailers must analyse not just how much of the discounted products they will need to achieve maximum ROI, but also which products should appear alongside those discounted in order to augment sales. Every choice is linked.
Making these kinds of calculations is immensely complex, especially when trying to accommodate all the probabilities associated with trends, virus levels and other emerging factors that impact sales. That’s why the most successful companies depend on machine learning to make automated recommendations. When discounts and stock levels are automatically set using machine learning, retailers tend to see higher profits with less inventory.
Black Friday and Cyber Monday can’t succeed unless retailers equally consider both price and allocation of products together — something algorithms have proven highly adept at doing.
There is so much data available about Black Friday and Cyber Monday. With Covid-19 overshadowing the entire retail sector, no business can afford not to leverage data effectively to have the most profitable Black Friday ever. It’s time for every company to embrace algorithms that guarantee more predictable Black Friday and Cyber Monday success.
PS I regularly write about Business Science. Recommended follow-up reading:
Supply Chain Optimization: Worth It or Not?
The paradox of mathematical optimization
Fashion Is Broken. Science Is Fixing It
Transforming the 5 core fashion processes
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