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AI-based Pricing for Winning Customer Experience
By Alexandr Galkin, CEO & Co-founder , Competera | Friday, January 25, 2019
“Machine learning applications will be commonplace within the next five years,” says a report from Deloitte. I believe that artificial intelligence can enhance retail teams to build a perfect customer journey and increase annual revenue by 5 percent. Such giants as Amazon have been successfully using the power of neural networks to entice buyers: AI-powered pricing recommendations account for 35 percent of the retailer’s earnings.
“Offering consumers what they want, when they want it, will separate the winners from the losers,” says Retail and Consumer Report 2018 by PwC. But from my experience in retail, “what they want” is not only about well-oiled marketing and logistics systems, or the product quality, but also about the prices which buyers deem right. As much as 60 percent of shoppers choose retailers with optimal prices.
Competera fully supports the idea that retailers need to build the right customer experience since this is what buyers require and what allows businesses to stay ahead of the market. The price of a product is at the top of the customer experience pyramid. It is important during the whole purchasing process and afterward as even after having tested the product at home consumers still compare the experience they enjoyed with the price they paid for it.
Crafting optimal prices is extremely hard to do as shoppers are becoming increasingly demanding. They expect highly personalized offers, have a great range of choices, and technology to quickly compare offers. At the same time, product, customer, and historical data is piling up and the retail market is growing more dynamic. Additionally, omnichannel retailers need different pricing strategies for multiple selling channels and markets. For this reason, retail businesses have to juggle enormous amounts of data and consider hundreds of variables to be able to ensure the right prices at any given moment.
Meanwhile, pricing remains underrated as a business process. It is managed by category or pricing managers and IT departments and is poorly supervised, as they usually are swamped with a myriad of other tasks. Quite often pricing decisions are not fully data-driven and are partially based on human expertise. As a result, the price of the product does not consider all necessary factors for building a rewarding customer experience. No matter how balanced the marketing strategy a company uses, it still loses consumers who are deterred by too high or imbalanced prices.
In many cases, retailers have tons of data covering years of their operations. This data is unstructured, distributed among many departments and, as a result, is never or rarely used. After five years of experience in the industry, we realized that mature omnichannel retailers want to debug the reasons for their successful experiments in pricing based on their data in order to repeat and scale them in the future. They want to be able to predict the effect of every pricing decision to fine-tune their prices according to their business needs and shoppers’ wants, ensuring a seamless customer experience.
The market leaders like Walmart are already benefiting from the power of machine learning at various stages of a customer journey, including pricing. Algorithms analyzing enormous amounts of data, unmanageable for humans, establish non-linear connections between thousands of variables, consider various pricing and non-pricing factors, and suggest scalable pricing decisions with predictable outcomes in real time.
In addition to setting the right prices, the algorithms take over all routine tasks and allow managers to finally focus on a strategy. Algorithms also create a unified database storing the results of all the bad and good experiments businesses have paid for. This makes onboarding new managers much easier and faster, and increases the operational efficiency of businesses.
All-in-all, creating the right price perception lays the foundation for a rewarding customer experience. Human managers are no longer capable of analyzing the vast amounts of data necessary for crafting a better experience for customers. That is where AI jumps in: to help retailers to make the right pricing decisions, forecast their results and scale successful experience across thousands of products and multiple selling channels.