How to use AI for personalised prices
Mature retailers are doing their best to ensure personalized pricing. Some are leveraging the potential of machine learning for price optimization to cater to the wants of their customers through data-driven price setting, remain competitive and raise revenue by at least 5%.
According to a recent Deloitte and Salesforce report, 40% of retailers personalizing their offers through AI use it to fine-tune prices and promos in real time. Such retail giants as Walmart and the likes use machine learning, which is the core of artificial intelligence, to optimize their pricing, warehousing, and customer support. For example, Amazon generates 35% of its revenue through AI-powered price recommendations engine.
Why AI is beneficial for personalized pricing
As AI solutions are becoming more affordable to a broader range of companies, advanced retailers are discovering their power in setting optimal prices for particular clusters of customers in real time. Before the age of machine learning, businesses would group their consumers into up to 20 segments by using the unreliable data of customer polls, which is insufficient for the modern retail market. With AI, retailers can divide customers into hundreds of groups based on their willingness to pay.
The algorithm uses any information about shoppers which helps to categorize customers as belonging to a particular group. AI bases its decisions on age, sex, location, and online behavior, among other factors, of a customer.
What puts AI algorithms before any other means of categorizing customers and offering optimal prices is the precision of machine-made decisions. AI-powered solutions analyze vast amounts of product, competitive, and customer data and consider any number of pricing and non-pricing factors, including but not limited to price elasticity, a grace period, business goals, and weather. This allows retailers to set real-time balanced prices and build the right price perception not for a particular product, but for the whole product portfolio. Such operations are unmanageable for humans anymore.
How to roll out AI-based solutions for customer-friendly prices
Launching pricing solutions powered by machine learning algorithms requires two steps — collecting data and building an internal or “hiring” an external solution.
Before anything else happens, retailers need to ensure that they have all the necessary data for the algorithm. “If you do not collect and analyze competitive data, and you offer a big number of SKUs, your price will be completely off the market,” says Bogdan Nesterenko, Head of Cross-border Projects at Northern European retailer RD Electronics.
In addition to the information regarding competitors’ pricing and promotional activities, retailers need macroeconomic, historical, sales and Google Analytics data in a single and relevant format spanning at least three years. Retailers can also simulate or buy the missing data, if necessary.
Some companies choose to build in-house solutions to gather data. However, the seemingly doable task very often proves unfeasible and even detrimental in reality. To design a data collection system, retailers usually engage IT departments which have their own KPIs and do not know much about the market of retail. As a result, they create a solution which can get retailers banned from marketplaces or the websites of their competitors. Besides, as online stores evolve technologically, an internal data collection solution has to be constantly upgraded to keep up with the market changes. Others lean towards external data providers that can tailor to the needs of retailers in terms of data quality and delivery, as well as confidentiality.
Be it an external or internal solution, companies need to ensure that the data they base their pricing decisions on is fresh, accurate and complete. To do so, retailers usually use a data quality control system which shows if the data is relevant and high-quality.
Powerful algorithms for personalized prices
The market already offers ready-to-use AI-powered price recommendations systems for retailers that are not willing to spend money and time on building and maintaining sophisticated pricing software. Such solutions can be launched once all the necessary data is gathered and structured.
The first step would be to teach the algorithm to categorise customers correctly and offer them optimal prices based on hundreds of pricing and non-pricing factors. Then, retailers should launch a pilot project to test the efficiency of the AI-powered system in real life. Once the pilot is successful, the solution can be scaled across the whole assortment.
UK-based omnichannel retailer Find Me A Gift wanted to make every product contribute to revenue by setting personalized prices. “We were running around like busy fools selling lots of stuff but we wanted to find a way to make each pound work harder for us,” said Jean Grant, purchasing and product development senior manager for the retailer. The company “hired” an external AI-driven solution to optimize prices. As a result, sales growth for the retailer was a staggering 22%, while its profit margins surged by 14%.
All-in-all, the customers are becoming more demanding and in power in today’s retail market. Therefore it is vital for retailers to offer personalized prices to win the hearts of shoppers and remain competitive. As calculating all the data necessary to craft optimal prices is humanly impossible, mature retail companies are discovering the power of machine learning algorithms to calculate and offer optimal prices for as many customer segments as possible.
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Nikolay Savin, Head of 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.