Over the past decade, retailers have used the peak season, particularly Black Friday, as a blunt tool to shift inventory to free up valuable storage space ahead of Christmas. But times have changed – and not just socio-economically. Flat rate discounts are no longer the approach. In 2022, the most digitally savvy retailers are using cloud-based, cross-functional datasets and analytics to transform peak-season retail through highly targeted optimization across operations, writes Andrew Bithell, Senior Account Manager, CTS.
Cross Business Intelligence
Black Friday kicked off earlier than ever this year, no doubt in part in response to customers deciding to shop ahead for Christmas and snag a bargain. However, with retail costs rising, including transportation and warehousing, this year’s peak season needs to be far smarter if retailers are to maximize the opportunity to entice consumers.
It’s no longer just a matter of moving obsolete stock on Black Friday to free up storage space for Christmas winners. Digitally-enabled retailers are now exploring the granularity of forecasts provided by more advanced machine learning (ML) and artificial intelligence (AI) techniques to take a far more nuanced approach to peak seasons.
Advanced analysis tools provide the opportunity to rethink every aspect of peak season operations. Retailers can now understand in detail the impact of SKU-level pricing decisions, not only on inventory levels and the supply chain, but also on customer experience, sustainability goals, margin and brand equity. And that insight is transforming both retail performance and the customer experience.
The key to this new model is data diversity. Retailers can no longer rely on isolated sources of information – no matter how timely, accurate or detailed. Innovators use cloud-based analytics to combine multiple datasets, from EPOS to merchandising to supply chain management and customer channels, as well as external qualitative resources like weather and traffic to transform peak-season performance.
These organizations leverage not only the extraordinary wealth of data – from supply chains and customer behavior in real time – but also the power of the latest cloud-based technology to allow disparate information assets to be rapidly combined and explored.
Insight is no longer limited to a specific business area – like the warehouse or the point of sale. With powerful analytics tools, a retailer can now understand the business, not just at the store or category level, but down to the individual customer experience. Customer behavior data provides increasingly accurate predictions and forecasts.
Essentially, analytics tools enable optimization – and that means peak season is no longer just about maximizing (often discounted) sales, but capturing and using the extra customer traffic to address a key area of business problems.
This cross-store insight allows retailers to ask more complex questions. Does it make more sense to sell additional inventory to reduce storage space and minimize the cost of storing old inventory? Or is the intrinsic value of the share sufficient to maintain the margin for some time – despite storage costs?
Logistics is a priority for the majority of retailers, using advanced analytics to optimize the movement of containers into the UK and then to storage in the most sustainable and effective way. Data-driven strategic planning is used to better forecast demand, plan inventory to be stored, estimate logistics needs, and automate operations where possible.
Analytics also allows retailers to attract shoppers in other ways, whether that be through offers to bring people into the store and familiarize individuals with different parts of the product mix, or through a much smarter approach to localization to maximize local demand. Customer expectations are now set by retailers, who use data to be more engaging and create a more compelling offer.
And profitability is secured. Retailers monitor several rapidly changing variables, such as B. Prices offered by competitors, current consumer demand for selected products and margins to dynamically update the most optimal prices to meet sales and revenue targets.
Retailers have become adept at optimizing for Black Friday – assessing obsolete inventory, tweaking prices and arming the supply chain for a bulk discount bonanza. But that’s a one-dimensional model — and one that might not work as well for a cost-sensitive customer base that shops early in the holiday season. Without accurate insights and powerful analytics, retailers risk cannibalizing the holiday season with oversized Black Friday deals, leaving warehouses full of unsold full-price inventory.
With the right skills and the right inferences from retailers, data analysis can transform the entire peak season event to reflect each retailer’s specific business. Whether it’s increasing customer retention and enhancing brand image through customer satisfaction, or eliminating costs by reducing waste and increasing productivity, data analytics play a critical role in transforming peak-season profitability.