Business
Why Analytics is Changing Peak Season for Digitally Savvy Retailers
Published by Jessica Weisman-Pitts
Posted on November 16, 2022
5 min readLast updated: February 3, 2026
Global Banking & Finance Review®
Company
Published by Jessica Weisman-Pitts
Posted on November 16, 2022
5 min readLast updated: February 3, 2026
By Andrew Bithell, Senior Account Manager at CTS
For the past decade, retailers have used peak season, especially Black Friday, as a blunt tool to shift stock in a bid to free precious warehouse space ahead of Christmas. But times have changed – and not just socio-economically. Blanket discounting is no longer the approach. In 2022, the most digitally savvy retailers are using cloud-based, cross-functional data sets and analytics to transform peak season retail through highly targeted optimisation across the entire operation, explains Andrew Bithell, Senior Account Manager, CTS.
Cross Business Intelligence
Black Friday started earlier than ever this year, no doubt partly in response to customers’ decisions to shop ahead for Christmas and bag a bargain. But given rising retail costs, including transport and warehousing, this year’s peak season will need to be far more intelligence driven if retailers are to maximise the opportunity to entice consumers.
This is no longer just about shifting out of date stock on Black Friday to free up warehouse space for Christmas winners. Digitally empowered retailers are now exploring the granularity of forecasting provided by more advanced Machine Learning (ML) and Artificial Intelligence (AI) techniques to take a far more nuanced approach to peak season.
Advanced analytical tools provide a chance to rethink every aspect of peak season operations. Retailers can now understand in granular detail the impact of pricing decisions at SKU level, not just on warehouse stock and the supply chain, but on the customer experience, sustainability goals, margin and brand value. And that insight is transforming both retail performance and the customer experience.
Cloud-Based Analytics
Key to this new model is data diversity. Retailers can no longer rely on siloed sources of information – however real time, accurate or detailed. Innovators are leveraging cloud-based analytics to combine multiple datasets, from EPOS to Merchandising to Supply Chain Management and Customer channels, as well as external qualitative resources such as weather and traffic, to transform peak season performance.
These organisations are exploiting not only the extraordinary wealth of data – from real-time supply chains and customers’ behaviour – but also the power of the latest cloud-based technology to enable diverse information resources to be quickly combined and explored.
Insight is no longer limited to a specific business area – such as the warehouse or point of sale. With powerful analytics tools, a retailer can now understand the business, not only at store or category level, but down to the individual customer experience. Customer behavioural data is fuelling ever more accurate predictions and forecasts.
Optimisation Priorities
Essentially, analytics tools allow optimisation – and that means peak season is no longer just about maximising (often discounted) sales but capturing and utilising the additional customer traffic to address a key area of business pain.
This cross-business insight is allowing retailers to ask more complex questions. Does it make more sense to sell off additional stock to reduce warehouse space to minimise the cost of storing aged stock? Or is the inherent value of the stock sufficient to ensure the margin will hold up – despite the cost of storage – for some time?
For the majority of retailers, logistics is the priority, using advanced analytics to optimise the transport of containers into the UK and then out to store in the most sustainable and effective way. Data driven strategic planning is being used to better forecast demand, plan inventory required to be stocked, estimate logistics needs and automate operations where possible.
Analytics are also allowing retailers to entice shoppers in a different way, whether that is using offers to bring people into store and expose individuals to different parts of the product mix or take a far more intelligence driven approach to localisation to maximise local demand. Customer expectations are now being set by those retailers using data to be more exciting and create a more compelling offer.
And, profitability is being safeguarded. Retailers are monitoring multiple rapidly changing variables such as pricing offered by competitors, current consumer demand for select products and margins to dynamically update the most optimal pricing to meet sales and revenue targets.
Conclusion
Retailers have become adept in optimising for Black Friday – assessing aged stock, tweaking pricing and gearing up the supply chain for a mass discounting bonanza. But that is a one-dimensional model – and one that may not work as well for a cost sensitive customer base shopping early for Christmas. Without accurate insight and powerful analytics, retailers risk cannibalising Christmas sales with over generous Black Friday deals, leaving warehouses full of unsold full price stock.
With the right skills and correct inference from retailers, data analytics can transform the entire peak season event in a way that reflects each retailer’s specific business. Whether it is in increasing customer loyalty and boosting brand image by delivering customer satisfaction or eradicating costs by cutting wastage and improving productivity, data analytics are playing a vital role in transforming peak season profitability.
Cloud-based analytics refers to the use of cloud computing resources to analyze data from various sources, allowing businesses to gain insights and make data-driven decisions more efficiently.
Customer behavior analysis involves examining consumer actions and preferences to understand purchasing patterns, which can help businesses tailor their marketing strategies and improve customer satisfaction.
Inventory management is the process of overseeing and controlling the ordering, storage, and use of a company's inventory. It ensures that the right amount of product is available at the right time.
Data diversity refers to the variety of data sources and types that organizations use to gain comprehensive insights, enhancing decision-making and operational efficiency.
Optimization in retail involves improving various aspects of the business, such as pricing, inventory levels, and customer engagement strategies, to maximize profits and enhance customer experiences.
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