Artificial Intelligence Tools Make Pricing Customer-Oriented

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Serving the customer through personalized and even localized offers is becoming the biggest goal of click-and-mortar retailers.

Serving the customer through personalized and even localized offers is becoming the biggest goal of click-and-mortar retailers. As the expectations of the buyers are soaring, while their choices are growing next to endless, retail businesses struggle to build the best possible customer experience to entice consumers.

This includes engaging shoppers with optimal prices.

For years, retail teams would be forced to focus on technical aspects of pricing rather than a strategy. They would dedicate the majority of their time to collecting and verifying data about competitors’ pricing and promotional activities to make them the basis for their pricing decisions.

Even armed with automated price scraping engines and solutions allowing for dynamic pricing set to offer prices lower than their competitors, retailers would still be far from their goal of pleasing the customers with the right prices.

As the market has been growing extremely dynamic, retail managers have been pushed to change their prices without knowing the objective reason for it or being able to forecast the effect of their decisions.

At the same time, retailers would grow to understand that they need to factor in hundreds of other variables such as customer behavior, business goals, and weather when setting prices.

As the data has been accumulating into huge unmanageable databases, very often unstructured or owned by several departments and thus inaccessible and unusable, the managers are drowning.

They lack time to collect all of the data, let alone to analyze it and come up with a feasible strategy aimed to build the right price perception.

Today retailers want to find a way to let their teams fully focus on the needs and expectations of the customer and switch from technical tasks.

To land a successful pricing strategy to satisfy buyers, managers require a “sandbox” to test the efficiency of their moves — something which could predict demand and the result of each of their pricing moves and pinpoint pricing opportunities based on vast amounts of data from multiple sources.

Additionally, they already have many successful pricing experiments under their belt, but they require a way to debug them to repeat and scale them up.

Meanwhile, Amazon, which is excellent at persuading its customers in offering the lowest prices in the market by using the power of AI, is expanding across new markets and competing with local retail enterprises.

The US giant goes as far as to let the algorithms recommend prices to buyers and generates over 35% of its revenue through such recommendations. Thus, regional players require something to level up the playing field and retain their clientele.

Along with Amazon, the industry’s first movers broadly apply machine learning algorithms to boost the operational efficiency of various departments and processes to better serve the needs of customers.

These include warehouse, stock, and assortment management, communication through chatbots and voice assistants, and pricing, among many others.

Price optimization through data-driven demand prediction, the ability to analyze massive piles of data from many sources regarding customers, competitors’ activities, and business goals, as well as to identify pricing opportunities make AI a go-to pricing assistant.

The algorithms learn from every bit of the data to set non-linear interconnections between hundreds of variables, forecast demand accurately and suggest optimal prices. They spare managers dozens of working hours dedicated to data crunching and allow building customer-oriented winning pricing strategies.

By enticing more customers, retailers significantly increase their earnings. Although the results of AI-based price optimization depend on the retailer’s initial business indicators, the average revenue boost amounts to 5%.

However, it can reach as high as 16% — as it happened during a one-month pilot of Competera’s platform with consumer electronics retailer Foxtrot.

In the near future, retailers would be required to ensure a rewarding customer experience to remain competitive.

As the market leaders are already leveraging the power of AI to optimize every bit of their operations, including pricing, to become closer to the customer, the rest of the industry will be forced to follow.

The laggards refusing to embark on the next technological shift are likely to leave the market and allow tech-savvy companies to reap the results of AI-based optimization.

ABOUT THE AUTHOR

Nikolay Savin, Head of Price Optimization Product at Competera. Combining 8 years of experience in supporting technology businesses and entrepreneurship in Europe on their effort in Silicon Valley with building a product for retail revenue growth Nikolay is passionate about sharing stories on technologies and innovations for retailers to help them grow.

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