Data which retailers need to analyze to set optimal prices in real time is so abundant that it seems impossible to process. But the success of a growing number of market players (like improving sales and revenue by 15 percent and 8 percent respectively) proves that it’s possible. In the digital age, you just need to start with enhancing your team with the right technology.
While retail is transforming at an incredible speed, many brick-and-mortar businesses remain paralyzed and confused with where exactly to go and what to improve upon, and whether to use technology solutions at all. But the portal to the tech-powered future is closing extremely fast. Advanced retailers are realizing it and doing whatever it takes to stay relevant. Jim Donald, ex-CEO at Albertsons, stated in a recent interview, “Status quo, especially today in today’s four-walls/no-wall world, is a death sentence. And if we’re pushed and challenged by companies like Amazon, Walmart, Kroger and by startups, it’s all good … Now is our time to sharpen our saws on the customer experience.”
The price of a product is at the core of a rewarding customer experience. Therefore creating the right price perception is a must for any retailer wishing to attract shoppers and grow. Retail giants like Amazon have nailed the science of persuading shoppers in offering the lowest prices in the market. According to a recent report by Epsilon, 64 percent of respondents said the price of a product was their primary reason for shopping Amazon. Another study shows that nearly 90 percent of Amazon’s product views come from the retailer’s own product search.
What’s behind the success? The company has been using artificial intelligence algorithms to empower its pricing teams to calculate and offer the right prices. As much as 35 percent of its revenue is being generated by AI-powered price suggestions. However, for other brands, setting optimal prices for the entire product portfolio to gain more per item remains a problem.
Why is setting optimal prices still a challenge?
Simply, retail teams need to analyze too much data to make real-time pricing decisions. Most retailers stick to SKU-based pricing (whether it is manual or automated based on the rules crafted by managers), which entails setting prices for products independently without factoring in all the cross-dependencies between price changes and demand of all the portfolio items. Considering retail teams usually need to reprice thousands of products weekly or even daily, optimal prices for such a significant number of items is a challenge, even with automated solutions. Retail teams usually end up setting optimal prices for their key, or the best and worst performing, products exclusively. Suboptimal prices for the majority of products mean reduced margins and, by extension, revenue.
But what if you need to craft optimal prices for every one of your products? The amount of data you need to analyze in this case grows exponentially, while no number of rules (or no power of automation) can cover all the parameters you have to consider. These parameters include but are not limited to customer behavior, weather, competitive prices and traffic for each item in your portfolio. To be able to come up with the right and timely pricing decisions, retail teams need to be empowered with next-gen technology — machine learning.
What’s so good about machine learning for pricing?
The answer is simple: machine learning algorithms enhance pricing teams by providing real-time price recommendations. First and foremost, they help businesses switch to portfolio-based pricing (when all the cross-dependencies between products are factored in and retailers gain more per item) and make well-informed, real-time decisions based on any number of parameters for any number of items across any number of price zones. This translates into growing sales and revenue. Secondly (but no less crucial), saved from the necessity to monitor and analyze data to reprice thousands of products regularly, managers can focus on tactical and strategic development.
When it comes to using machine learning in pricing, retailers have a choice: build the whole system by themselves or hire a technological partner with a ready-to-use solution. Both options have their pros and cons.
Developing an in-house solution
Such an endeavor seems a feasible solution for large companies having significant financial resources. Building up and maintaining an AI-powered pricing system requires the constant engagement of the retailer’s IT department, which calls for additional funding. Also, such a system needs to be built by someone with extensive industry expertise, which is not always the case with even internal IT professionals.
Retailers who create in-house pricing systems are usually forced to reinvent the wheel and focus on something that does not stand for their competitive edge, as suggested by Vimal Kohli, Dick’s Sporting Goods’ VP and Head of Data Science, Analytics and CRM, at the Shoptalk conference earlier this year. In the digital age of startups, who are passionate about what they do, it seems feasible to stop reinventing the wheel, find someone who specializes in what you need, and concentrate on the things that help you differentiate from competitors.
Partnering with an AI-provider is a good option for those who want to get almost immediate results and be minimally engaged in the whole process. Such solutions transform along with the needs and wants of retailers when it comes to infrastructure and pricing models. They can help brick-and-mortar companies boost revenue by 8 percent.
All in all, advanced retailers are realizing that to succeed in today’s market they need to learn to manage unprecedented amounts of data and make on-the-fly pricing decisions. It’s not that bad, though. Retail managers can have a sidekick if they will. Technology is there to help them become more efficient and productive, and finally switch from mundane tasks to strategic thinking. What’s no less important, it is there to help retail businesses boost their sales and revenue.
About the writer: Nikolay Savin is the Head of Price Optimization Product at Competera. Combining a decade of experience in supporting technology businesses and entrepreneurship in Europe on their efforts in Silicon Valley with building a product for retail revenue growth, Nikolay is passionate about sharing stories on technologies and innovations for retailers and tech professionals to help them grow.
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