Online and offline retailers understand that the outside world has become too complicated and unpredictable as the number of items available is becoming too difficult to handle by hand. Therefore, they’ve decided to use algorithms to establish the best prices, to predict stocks, among others. Those retailers with the most optimal price end up winning the market since buyers will always go after the best deal.
So, how do you offer the most optimal price?
Since the number of items as well of stores increases on a daily basis, managers aren’t able to work with all of the historical as well as competitive data to establish the best prices. Often, all that they can do is offer new prices across their main items whereas the rest are sold at completely incorrect prices resulting in a loss of revenue.
Therefore, businesses are trying to find more subtle and accurate, and more dependable ways of providing prices. This is where AI comes in to play as it can handle copious amounts of information and in return, offer data-driven pricing recommendations in a short period. A few retailers have already taken advantage of machine learning algorithms for pricing and are already benefiting immensely from it. They are now the leaders in their respective industries.
In this text, we’ll cover both the pros and cons of AI retail. So, to start, what exactly does machine learning bring to retail?
The three main benefits include:
- Retail managers can move from routine, time-consuming tasks and instead focus on high-level decision-making activities. Algorithms help out by becoming pricing assistants, otherwise known as “second brains”, and can process lots of data at lightspeed and then follow up with pricing suggestions. Managers only have to figure out which data-driven strategy would be the best for price optimization and track the results of the pricing software decisions to see if any adjustments need to be made.
- Retailers can scale up every successful action, steer clear of repeated failures, and onboard new staff with ease as the algorithms make a central knowledge database that holds all of the information regarding each transaction while learning from each pricing decision made.
- Pricing teams can make precise measurements regarding pricing strategies. The algorithms can utilize any pricing scenario based on a variety of limitations, such as seasonality, consumer behavior, and business objectives to allow retailers to try out the practicability of any pricing decision.
So, how come AI isn’t everywhere yet?
Even though they boast some advantages, machine learning solutions haven’t gained much traction yet as:
- The algorithms require both high-quality historical and competitive data to make proper pricing predictions. Company data typically doesn’t meet all of the requirements as they aren’t all one format, new, properly structured, accurate, or they don’t span a minimum of three years.
To combat this, some data can be AI-simulated based on the data that there is.
- It’s relatively pricey and takes a bit of time to activate and then maintain in-house.
To combat this, retailers can find an external AI provider that can offer a ready-to-use customizable tool that is driven by an entirely functional infrastructure and a support team that’s quick to react.
- Algorithms are quite complicated which is why it’s impossible to know precisely how they work along and understand the kind of logic that they use. As a result, retail teams struggle with senselessly trusting what the “black box” suggests, making it seem counterintuitive at times.
To combat this, a pilot can be used to prove how effective an AI solution is, thus winning over pricing teams.
How does AI work, though?
Before making predictions, the algorithms have to be trained. Often, this training is based on historical data, and the target functions are sales, revenue, profit, or market share.
During the learning process, the model evaluates every single variable that influences sales, such as prices and traffic, and then creates a function that best outlines those sales. As soon as the algorithm has finished its training and proven that it’s capable of providing accurate forecasts, retailers can begin utilizing its suggestions regarding the values necessary to make the most sales in the future.
For the algorithms to learn correctly, they need a lot of competitive data as that’s what gets the retail engine to start. That’s how to differentiate a market leader from an ordinary player.
Now, what if you’re missing some data? Don’t worry; down below we’ll outline the methods that can help you get a hold of missing data for forecasting algorithms, starting from purchasing data all the way to machine learning modeling. But first, let’s begin by detailing why data goes missing in the first place.
Reasons for missing data
Leading retailers struggle with algorithms as historical data often isn’t complete or it’s mixed which is why it can’t be used for training. A few reasons why this would be, include:
- The format of the data has changed. The thing is, whether it be an internal system or an IT solution, for instance, data is gathered differently such as by day or by transaction, which is why the data is collected in different formats for different periods.
- The data has been gathered for other reasons. If the data has been collected at the top level, for instance, to pay category managers their bonuses, it doesn’t work for those algorithms.
- The amount of time the retailer has spent in business may not be enough. Therefore, during the first stage, sales are almost completely dependant on website traffic, 90% to be more precise. As a result, it’s difficult to evaluate how prices impact sales during that period.
- There could be flash sales. Should a retailer operate in flash sales mode, which is when there are sales for various categories or brands for short periods, the algorithms won’t be able to work with that type of heterogeneous sales data.
Should your data not suffice for either model training and forecasting due to one of the instances above or any other for that matter, retailers need to use the best values from the data that they have or model what they don’t have.
Working with the data you have
If the issues lie in the data format, the retailer needs to combine it into one format. Should some data have been gathered, but then the retailer starts to add more information based on new factors such as competitive prices, they need to wait around a year before gathering new information. They could, on the other hand, purchase the data that they don’t have.
Forecasting models can be made even without the data gathered from the market.
They won’t be exact, it’ll take more time, and retailers will have to make more assumptions to model the missing information. However, it should suffice and it can be used widely.
Simulating missing data
Retailers can use a few methods to predict the missing values of other variables thanks to the data that exists of certain variables. For instance, if a retailer’s got both their prices and sales from a previous couple of years and they’ve got a history of competitor’s prices from the past year and a half, a simulation can be used to figure out what the missing competitive prices are.
Classifiers are used to find a solution to those issues. They can predict the missing values from independent variables from the data available. The two main types of “smart” data imputation include:
- Predictive model: To predict the missing values, the gathered data needs to be divided into two sections. The first one contains the existing data while the second one has the missing data. Therefore, the first one will be the training set while the latter will become the forecast target. As a result, a binary classifier is used to answer whether or not an event occurred, such as whether the items had been on the shelf. A categorical classifier then assigns an item to a certain segment, such as a price segment.
- KNN (k-nearest neighbor) method: This method predicts the missing values based on the “closest” variable to the target. Then, the distance estimated determines how similar the variables are.
Conclusion
Companies have to set the most optimal prices to sell more since customers will always go with the retailer that offers the best price. Retail teams aren’t able to gather and process the never-ending amount of pricing data on their own, so they aren’t able to set the most optimal price for each product, resulting in a loss in revenue.
Retailers have opted to try out AI hoping that they’d receive more data-driven pricing decisions for each product.
Each machine learning algorithm provides some advantages including:
- Getting pricing to be more data-driven and optimal;
- Breaking down each pricing and promotional decision to repeat their success or steer clear of going down the same rabbit hole once again;
- Allowing managers to onboard fast and with ease;
- Saving managers from doing routine tasks and allowing focusing on more strategic ones.
For every disadvantage found in AI, there is a solution to help combat the issue. For instance, there are some methods that retailers can use to fill in missing data or forecast prices despite a small amount of data. You’ll receive the most optimal prices by gathering and processing historical data as it allows for much simpler neural networks training, thus, making the forecasts much more dependable.
Companies that take advantage of them end up winning the market.