A pricing reboot

The unprecedented economic impact of the global pandemic created income insecurity and heightened price sensitivity in shoppers worldwide. A shopper study in Brazil, France, Germany, UK and the US found that 41 per cent of the shoppers reported "less" or "much less" income than prior to the pandemic, while 27 per cent anticipated that they would have less or much less income post-pandemic.
With shopper, market and competitor behaviors changing at an unprecedented rate, it's clear that retailers need new strategies to understand their shoppers' price expectations.
Pre-Covid, only 7 per cent of shoppers were primarily online. Today that number is 35 per cent. The imperative for channel-aware, dynamic pricing, promotions and markdowns is urgent. Today's science can detect accurate demand signals and generate price recommendations that meet shopper expectations on the items where they are most price-sensitive, while knowing where in the assortment to safely recover margins. Since shopper expectations and preferences vary by channel, retailers can harness science insights to craft the prices and offers that will most effectively engage shoppers to meet business goals. Moreover, dynamic capabilities enable automation of pricing updates to speed responsiveness and reduce manual interventions.

Many retailers take a siloed approach to everyday pricing, promotions and markdowns. Modern science enables what-if scenario planning to understand the impact of a potential price change or promotion. Similarly, science-drive markdowns deliver the optimal mark depth and cadence to meet business goals by the target clear-by date. Moreover, large shifts or disruptions in consumer demand patterns like the one caused by the global pandemic drive the need to revisit KVIs (key value indicator).
Retail pricing was one of the earlier real-world areas to benefit from applied AI and Machine Learning (ML) science. So the technology is road-tested and robust, and some applications take an outdated approach of force-fitting a few ML models to cover all retail pricing challenges.
While clearly today's rate of change is unprecedented in retail, ultimately change represents opportunity. DIY retailers who move decisively to leverage price optimisation science have the…