Today, retail faces new challenges and needs to learn very fast to process all available market data, gain useful insights, and evaluate outcomes. The easiest way to achieve this is by having a dynamic pricing strategy that uses machine learning techniques.
If you follow retail news, you’re aware of different tips and tricks retailers around the globe use to keep customer engagement high. They create cashier-free stores, self-driving shops, and use new engagement techniques in old-fashioned offline stores.
But all these are lame attempts as long as the price is still a prevailing reason to buy stuff for 60% of shoppers.
That’s why modern retailers need an intelligent dynamic pricing strategy. Although new ways of collecting and processing data greatly help them with that, most specialists still use manual crawling techniques, Excel Spreadsheets, or simple price tracking solutions like Price2Spy/Prisync.
The aftereffect of such an approach is obvious: only 39% of retailers succeeded with useful insights.
Price adjustments made for the whole front store’s inventory in just a second, in response to real-time demand, is much more effective than those set manually with all the human mistakes. It is where machine learning steps into the room, giving retailers an option to optimize not only prices, but also business strategy, costs, and managers’ efficacy.
Keep reading to find out how exactly a retailer can employ strategic pricing and outperform his competitors.
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