Unstable Prices: How Artificial Intelligence Predicts Retail Sales
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The amount of data necessary to gather and to process in order to create a solid pricing strategy only keeps on growing. Therefore, retail teams are now struggling to collect and evaluate all of it manually, making it difficult to set the proper prices at the right time as well as respond to their competitors’ actions on time. Only dynamic and flexible retailers that will skim the cream off the market will succeed. Indeed, those who are ahead of their competition have begun adopting AI-powered solutions, in-house or external, to track competitors and offer both their category and their sales managers the proper data necessary to make a revenue-boosting strategy.
What Companies Miss without AI
Companies that only depend on humans to price their items or services run into these issues:
- Retail teams aren’t able to gather or process all of the data they need in order to set the right prices in a timely manner.
- Managers often end up making errors when gathering and evaluating data.
- Pricing Managers don’t have enough time to figure out a balanced long-term pricing strategy as they use up most of their time gathering and evaluating data. Therefore, they experience “symptomatic” pricing since they alter prices for a narrow group of items instead of for the entire assortment.
- Pricing decisions that are made by humans tend to be subjective and are always based on how competent the expert is; the better the expert is, the better their decisions will be.
- It’s impossible to have the same success from pricing decisions each time as they’re based on the intuition of the set manager and thus can’t be taken apart and turned into an action plan.
- It’s difficult to onboard a new pricing professional as the retailer doesn’t have a documented pricing history.
The Advantages of Machine Learning
AI-based algorithms devour every single available data point regarding each transaction in the retailer’s pricing history along with data about seasonality, customer behavior, and many others and learn from them. They stock every single experience, good and bad, evaluate it, and then make the information available to the retail teams at any time.
In addition, when making predictions, they take into consideration both the retailer’s needs and goals.
With the help of ML, retailers can:
- Predict the impression of each pricing decision
- Duplicate pricing decisions that went well
- Pay attention to high-level tasks instead of gathering information
- Onboard a newly hired manager with ease as all of the data is correct, organized, and held in one place
- Track their market position in real time.
In house solutions need a lot of investments and continuous IT support, often making them economically impractical. Instead, retailers will benefit more from external out-of-the-box price optimization and price scraping solutions.
The providers of those tools make an AI-powered model to predict both demand and sales by establishing relationships between variables and data points that are relevant. The top models can have up to 98% prediction accuracy.
Potential Problems with Integrating AI
Even though AI boasts many advantages, they’re still quite scarce in retail because:
- There isn’t enough high-quality data spanning a minimum of three years which is what algorithms need to operate efficiently.
- It’s difficult to integrate as the entire retail team needs to be involved.
- There can be trust issues from managers who’ve been making pricing decisions from their own intuition for years and they don’t understand how the solution operates.
AI-powered tools for pricing work to solve many problems that retailers face today. They work faster than humans, are error-free, don’t get tired, are able to process copious amounts of data, and hold onto data in one format and in one place, streamlining pricing predictions and teaching new staff.
However, companies are still uncertain about allowing machines to increase their revenue as they are a “black box”, meaning they don’t understand how the work, they don’t have enough high-quality data, and they feel the need to have everyone play a part in pricing for the integration. Those fears disappear, though, when the retailer witnesses Ai-driven margin/sales growth.