How AI Can Help You Personalize Your Prices

Retailers who have been around for some time are now trying as hard as they can to make sure that their pricing is personalized. A few of them are optimizing prices by taking advantage of machine learning in retail so that they are delivering on the desires of their customers by setting data-driven prices. Indeed, based on studies by both Deloitte and Salesforce, 40% of retailers that personalize offers do so via AI that way they can adjust both prices and promotions in actual time. Take, for instance, Walmart; they have used machine learning, which is at the heart of artificial intelligence, in order to optimize their pricing, their warehousing, as well as their customer support. As a result, they are able to stay competitive while also see increases in revenue by a minimum of 5%. Indeed, Amazon makes 35% of its revenue simply via their AI-powered price recommendations engine. So, why exactly is AI helpful when it comes to providing personalized prices?

Benefits of AI Towards Personalized Pricing

Since AI solutions are much cheaper now, they are able to cater to way more kinds of businesses, allowing retailers to figure out just how powerful it is to set optimal prices for certain groups of customers in actual time. Prior to machine learning, companies would have to put their customers into groups of no more than 20 segments with the help of data that they couldn’t even depend on from customer polls; this simply isn’t satisfactory enough in today’s retail market. Through AI, on the other hand, retailers are able to break up customers into many more groups such as depending on how much they are willing to pay. They way it works is that the algorithm utilizes any kind of data about shoppers in order to break the customers up into specific groups via information such as age, sex, location, and even online behavior, just to name a few.

Now, AI algorithms stand out from other forms of customer categorization because they are able to provide optimal prices via highly accurate machine-made decisions. Those solutions that are AI-powered are able to go through and analyze copious amount of item, customer, and competitive information while taking into consideration a variety of pricing and non-pricing factors such as price elasticity, business objectives, the weather, and even a grace period. As a result, retailers can establish actual-time balanced prices as well as create the proper price perception for the entire product portfolio rather than just for a single item. Humans simply aren’t able to accomplish such.

In order to introduce pricing solutions that are backed by machine learning algorithms, companies have to gather data and then either create an internal solution or enlist the help of an external solution.

How Powerful Algorithms are Able to Personalize Prices

Today, the market already provides retailers with AI-powered price recommendation systems that can be used right away without having to spend a fortune or spend too much time on creating and then dealing with the maintenance of a complex pricing software. In fact, they can be put into operation as soon as all of the data needed is collected as well as structured. Therefore, the first thing that you would have to do is teach the algorithm the proper way of categorizing customers and providing them with optimal prices depending on a number of both pricing and non-pricing factors. After, they need to try a pilot project in order to see just how efficient the AI-powered system is in actual time. If it is a success, you can scale it across the entire assortment.

Take, for instance, Find Me a Gift, a UK-based omni channel retailer that wanted every single one of their items to play a part in the amount of revenue made by setting prices that are personalized. Their purchasing and product development senior manager, Jean Grant, claimed that “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.” Therefore, in order to optimize their prices, they decided to enlist the help of an outside AI-driven solution, causing them to see a growth in sales by 22% with profit margins increasing by 14%.

Internal vs External Data Collection

Prior to taking any other steps, retailers have to make sure that they have all of the information that the algorithm needs. The Head of Cross-border Projects at Northern European retailer RD Electronics, Bogdan Nesterenko stated that “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.” On top of the data about both your competition’s pricing and promotional work, retailers have to have information such as macroeconomic, historical, sales, and Google Analytics all in one relevant format that goes back the past three years. If needed, retailers are able to either simulate or simply purchase any of the data that they don’t have.

Instead of collecting information, some firms decide to create in-house solutions instead. Even though it does seem like a feasible task, in reality, it turns out to be essentially impossible with very negative side effects. In order to create a data gathering system, retailers typically have to get the help of IT departments that have their own KPIs and who also aren’t that knowledgeable of the retail market. Therefore, they end up making a solution that actually gets retailers removed from either the marketplaces or the sites of their competitors. On top of that, since online stores are constantly seeing technological improvements, the internal data collection solution always needs to be enhanced so that it can stay up-to-date with all of the market changes. As a result, other firms end up going with external data providers since they are able to cater to their needs when it comes to the expected data quality, delivery, and confidentiality. Regardless of the solution chosen, though, firms do need to make sure that the information that they end up basing their pricing decisions on is new, correct, and complete. As a result, retailers often utilize a data quality control system as it is able to pinpoint whether or not the information is appropriate and high-quality.

Conclusion

Overall, customers are becoming a whole lot more demanding as they hold a lot more power in the modern retail market. As a result, it is crucial for retailers to provide personalized prices if they want to get the attention of customers while also stay competitive. It is simply unfeasible for humans to calculate all of the information that is necessary in order to establish optimal prices, therefore, retail firms that are more mature are utilizing the strength of machine learning in order to calculate as well as provide optimal prices for as many customer segments as they can.

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