Black Friday: the day of dramatic demand.
Beyond the big day to Cyber Monday and onwards to Christmas, shop floors and warehouses need to be suitably stocked to meet and exceed expectations.
More people in store, demanding deals, discounts and instant stock availability. The high street competition all doing their utmost to capture footfall and secure sales.
Retailers can use educated but flawed guesswork to navigate these choppy waters. Or they could recruit a new captain of the supply chain ship: machine learning.
Here we explore how this brainy branch of artificial intelligence can really deliver the goods at the busiest time of the shopping year.
What Is Machine Learning?
Machine learning is a sub-category of AI, the concept of using technology with the characteristics of human intelligence. Machine learning algorithms spot patterns in the data gathered to provide insights and improve decision-making.
In retail, its application is immensely powerful.
From understanding staff preferences to boost their productivity to personalising the customer offering, it eliminates the need for gut feeling and guesswork.
And when it comes to predicting when and how often shelves need replenishing at peak times, its data-crunching capabilities are unrivalled by any human brain.
So while staff are focused on providing exceptional customer service and creating an amazing brand experience this Black Friday, trusty tech can be working hard behind-the-scenes to meet demand, increase efficiencies and drive sales.
Rafik Salama, Head of Data Science at REPL, said: “Retail is a data goldmine. As the cost of storing data has gone down from millions to just pennies, we’ve acquired more and more of it over many years.
“The challenge now is to use the tech we have available to make our data meaningful. Machine learning allows us to do that. Our desktop machines can process terabytes of data and produce conclusions for us.
“We end up with accurate, in-depth knowledge and insight across all areas of a business. Gradually, these insights start to facilitate small changes which over time become transformational.”
Using Machine Learning To Predict Stock Surges
The sea of data lurking in the depths of retailers’ computer systems has the power to really make a splash.
The trick is to have the technology in place to take all those facts and figures and translate them into tangible answers and solutions. Old-school methods just won’t cut it.
Machine learning algorithms devour data, both historic and in real-time. They’ll eat their way through every morsel:
- Historic sales patterns
- Footfall figures
- Weather patterns
- Traffic reports
- Events and promotions
- Location of item
- Staffing including who was working, were they productive, even whether or not they were smiling
This data deluge will then be analysed and modelled: what happened? Why did it happen? What will happen in the future? What’s the best decision to achieve the best outcome?
The result? Machine learning can provide the answers to numerous Black Friday stock conundrums:
- What sold well last year?
- Where in-store should we place popular products?
- What cultural trends should we consider this year?
- Who should we put on the shop floor to deliver the best customer experience?
- How might the weather impact sales this year?
- Who should they be working alongside to maximise productivity?
- How can current stock levels inform our discounting strategy?
All these complex decisions can be automated across thousands of product lines in hundreds of stores, thanks to the power of predictive analytics.
Fed by big data, this shifts the focus away from merely stacking unsold stock high and hoping it sells, based on traditional insights. Instead, your demand decisions will be based on the lightning speed, infallible logic and precision of AI.
And this process will become more and more accurate year-on-year as algorithms learn lessons from previous decisions, continuing their optimisation mission.
Rafik said: “Retailers can forget about keeping track of hundreds of dashboards, all telling them different things. Linking all this data together and using predictive analytics makes sense of it all and ranks the factors that will affect future performance.
“On Black Friday, for example, retailers can use all the data they have at their disposal to predict what shoppers will spend their money on this year with more accuracy than ever.
“Machine learning connects the dots for you, letting you focus on delivering great customer service while increasing sales and efficiency, and decreasing waste.”
The speed and capacity of AI compared to the error-prone limitations of the human brain is beyond dispute.
Harnessing its power to meet stock demand will mean fewer disappointed shoppers leaving stores empty-handed on Black Friday. With radically reduced chances of being greeted by the wrong bargains at the wrong price in the wrong place sold by the wrong people, consumers will be encouraged to spend.
“The possibilities of machine learning in retail are very exciting,” said Rafik. “We’re starting to see some forward-thinking retailers who know that using dozens of Excel spreadsheets is no longer viable.
“These pioneers are leading the charge to meet the industry’s challenges head-on by embracing the best tech available. The others will soon have no chance but to follow.“