Retailers’ Attitudes are Changing Towards AI Pricing Post-COVID-19
AI pricing trends have evolved through the pandemic months. At the onset of the pandemic, global supply chains were disrupted, consumer demand patterns shifted unpredictably, and retailers’ ability to operate their stores varied wildly depending on the goods sold and whether they were classified as ‘essential’ or ‘non-essential’.
For those retailers that were deemed essential, including those in the grocery sector, their worlds turned upside down with little warning, as Brits spent nearly £60m on stockpiling in the first week of the coronavirus pandemic alone.
In response to the panic buying, supermarkets were forced to place fair purchase limits on products such as dry pasta, toilet roll, and hand soap, and had to contend with rapidly emptying shelves and queues outside their stores. And perhaps one of the greatest challenges of this cataclysmic shift in consumption was the impact on retailers’ pricing strategies.
Recommended Blog: New Adobe And Epsilon Report: Lack Of Data Maturity Hampers CDP Success
These were unprecedented times, and yet somehow it was expected that retailers would be able to adapt their prices accordingly, across tens of thousands of SKUs in the case of an average grocer, at a time when consumers’ price sensitivity was at an all-time high.
It was a complex and delicate situation, with retailers trying to avoid any pricing changes that could be viewed as exploitative by consumers; the difficulty was compounded by rising costs from their own suppliers.
As the pandemic wreaked havoc on the standard dynamic of pricing based on supply and demand, retailers increasingly looked to science-based technologies to regain control, particularly digital technologies and artificial intelligence.
It became clear that digitalization was key to surviving COVID-19-related challenges in retail – including in the pricing realm – and that the use of data-driven technologies would become table stakes long after the pandemic had subsided.
The Role of AI in Retail Pricing
In today’s hyper-competitive world, more retailers are recognizing that AI pricing strategies are necessary to support robust, profitable demand.
Before COVID-19, AI price optimization was largely seen as a solution only for the major players in retail. But as the pandemic accelerated digital transformation initiatives – across all functions of retailers’ businesses – the adoption of AI pricing has experienced a marked increase.
With AI, self-learning algorithms can scan vast amounts of data, calculate a myriad of pricing scenarios, and suggest the best possible price to satisfy customers, drive increased sales, and optimize profit margins.
Retailers are able to account for the numerous factors that influence consumer demand, such as weather, public holidays, competitors, and footfall, to name a few. Using this information and more, retailers can predict demand with accuracy and precision, and offer shoppers the right price, in the appropriate channel, at the right time.
As Jon Duke, research vice president of IDC Retail Insights, noted: ‘Retail of the future is customer-driven, analytically robust, and automated. Achieving that is effectively impossible without a modern solution for price optimization’.
Putting AI to Work in Retail’s Recovery
For those retailers that had to close their doors during lockdowns, with vaccines being rolled out and stores reopened in most areas, their attention will now turn to post-pandemic recovery. Likewise, for essential retailers that remained open during lockdowns, they too can now focus on the road ahead and strategic, long-term priorities versus the immediate need to manage a crisis.
In either retail scenario, pricing is one of the quickest and most dynamic levers a retailer can pull to drive immediate change. Pricing inherently helps retailers control the relationship between supply and demand. It can also be a powerful tool to help drive shopper frequency, both directly and indirectly.
Understanding how the price will influence a shopper’s purchase behavior is critical, and the use of AI and machine learning algorithms is instrumental to this process.
The ‘Next Normal’, Not Back to Normal
The reality is that in-store traffic is unlikely to reach pre-pandemic numbers anytime soon, as some consumer behavior shifts towards online shopping will stick around – not to mention the increased competition that emerged from lower consumer brand loyalty during stock shortages and shopping restrictions.
No matter where a consumer is interacting with a retailer, pricing can be a powerful catalyst to build long-term price perception, increase shopper frequency, regain market share, drive profits, and fund sales-boosting activities such as promotional campaigns and investments into omnichannel capabilities.
Although many retailers have been hit hard by the pandemic, there is no better time to reset and build a pricing structure for tomorrow – one built on the analytical tools and data that will make retailers successful no matter what disruptions lie ahead.
[To share your insights, please write to us at email@example.com]