Unstable Prices: How Artificial Intelligence Predicts Retail Sales

The amount of data necessary to gather and process to create a solid pricing strategy only keeps growing. Therefore, retail teams are now struggling to collect and evaluate all of it manually, making it difficult to set reasonable prices at the right time and respond to their competitors’ actions on time. Indeed, those ahead of their competition have begun adopting in-house or external AI-powered solutions to track competitors and offer their category and sales managers the pertinent data necessary to make a revenue-boosting strategy. Only dynamic and flexible retailers that will skim the cream off the market will succeed.

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What Companies Miss without AI

Companies that only depend on humans to price their items or services run into these issues:

• Retail teams cannot gather or process all the data they need to set the correct prices promptly.
• 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 spend 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 humans make tend to be subjective and are always based on how competent the expert is; the better the expert is, the better their choices 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 isn’t easy 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 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 experience, good and bad, evaluate it, and then make the information available to the retail teams at any time.

In addition, they consider both the retailer’s needs and goals when making predictions.

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 competitor price scraping solutions.

Incorporating competitor price scraping into retail strategy can significantly level the playing field. By systematically gathering pricing data from competitors, retailers can gain actionable insights to make informed pricing decisions. This not only helps in staying competitive but also in identifying pricing trends and opportunities for promotions or discounts.

The providers of those tools make an AI-powered model to predict demand and sales by establishing relationships between variables and relevant data points. The top models can have up to 98% prediction accuracy.

Potential Problems with Integrating AI

Even though AI boasts many advantages, they’re still relatively 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.
• Integrating isn’t easy, as the entire retail team needs to be involved.
• There can be trust issues from managers who’ve been making pricing decisions from their intuition for years and don’t understand how the solution operates.

Conclusion

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, can process copious amounts of data, and hold onto data in one format and 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 they 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 when the retailer witnesses Ai-driven margin/sales growth.

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