Alexandr Galkin, CEO & Co-founder of Competera, price optimization software for enterprise retailers looking to increase revenue and stay competitive. Forbes contributor, speaker at IRX, eCommerce, RBTE conferences.
How to enhance customer experience with AI-powered pricing
The fight for customers in retail is growing rougher and more expensive. Empowered by modern technology which allows for instant access to any data and immediate sharing, consumers expect retailers to use every piece of their digital footprint to cater to their personal needs — for example, location-based offers. At the same time, shoppers won’t buy into price discrimination which derived from the data about their willingness to pay — their human nature can’t allow them to stand someone else having a better bargain. Therefore, businesses need to fine-tune their data collection and usage strategy to ensure they do not lose the trust of their customers.
Also, technology enables buyers to compare retailers’ offers — including prices, delivery terms and customer support, among other factors — within a matter of minutes through online stores websites, price comparison engines and mobile applications.
Thus, buyers, not sellers are in control in the market. Retailers who will provide customers with anything they want, when they need it, will win the market, says recent PwC Retail and Consumer Report 2018.
To win customers, businesses tend to revamp marketing and logistics by adapting up-to-date technology, including AI, while neglecting pricing. Even at mature companies with powerful automated pricing engines, humans are still responsible for making the majority of pricing decisions.
In a highly intensive and dynamic market, such an approach seems outdated as it leads to multiple errors, causes a lag in terms of real-time pricing as compared to competitors and, as a result, provokes revenue loss. On top of everything, humans are simply unable to consider all the necessary pricing and non-pricing factors when setting prices. For this reason, customers are attracted to a retailer by a compelling marketing offer and delivery terms and then are deterred by too high a price.
Hence, the price of a product is at the core of rewarding customer experience.
The first movers of the industry, such as Amazon, are increasingly adopting AI-based solutions to improve customer experiences through enhancing pricing managers or even outsourcing a big chunk of pricing decisions to machines. The US retail giant is making as much as 35% of its revenue through its AI-powered price recommendations engine: it offers optimal prices to customers based on their purchasing history.
Additionally, 40% of manufacturers that presently benefit from AI to make their offers more personalized have used it to customize pricing and promotional campaigns on the fly.
Why Artificial Intelligence? Retail teams use price optimization powered by artificial intelligence to create the right price perception as the algorithm always knows whether to set higher or lower prices (or keep them the same), which products to reprice, and how high or low the prices should be.
This allows retail teams to receive real-time price recommendations to ensure optimal prices at any given moment and predict the effect of every pricing decision. Errorless self-learning algorithms establish non-linear interconnections between many variables and recommend the right prices for a product or the whole product portfolio based on data regarding customer behaviour, all past transactions of the retailer, profitable and negative pricing decisions, seasonality, stock availability, and the retailer’s business goals — essentially, every relevant piece of data about both pricing and non-pricing factors.
Saved from the routine of data collection and analysis, the retailer’s managers switch to more high-level tasks.
The final results of pricing enhancement usually depend on the retailer’s pre-AI business indicators, the speed of decision-making, and efficiency. However, in general, AI-powered price optimization software showcases the following results:
● Customer LTV soars by 20%;
● Revenue increases by 1% to 5%;
● Margins surge by 2% to 10%;
● Discount approvals decline by 80%.
Retailers need the “fuel” — historical, competitive, sales and Google Analytics data — to feed the algorithms. The data has to be high-quality and well-structured, and span no less than a year. The next step would be to teach the team to trust AI price recommendations; sometimes managers sabotage the suggestions either in fear that the AI solution will render them irrelevant to the company or because they doubt the effectiveness of such recommendations.
Despite the difficulties of adopting an AI-powered price optimization solution, it is an effective tool to build the right price perception in the minds of customers and, as a result, to increase revenue.