The amount of data retailers need to collect and process to craft a winning pricing strategy is continually growing. Thus retail teams are running out of capacity to gather and analyze the data manually, which makes them unable to set the right prices at the right time. The latter is the key to success: dynamic and flexible retailers skim the cream off the market, while those unable to quickly respond to the competitors’ actions lag.
To stay ahead of competitors, market leaders have started to adopt AI-powered solutions—either in-house or external—to monitor competitors and provide their category and sales managers with necessary data to create a revenue-increasing strategy.
AI-less Pricing: What’s Missing
These are the main problems retailers who rely on humans (and by extension on luck) in pricing have to deal with:
- Retail teams are not capable of collecting, let alone processing, all the data necessary for the right and timely prices.
- Managers frequently make mistakes when collecting and analyzing data.
- Pricing Managers lack time to come up with a balanced long-term pricing strategy since they spend most of their day collecting and analyzing data. As a result, they suffer from “symptomatic” pricing as they change prices for a limited group of products and never for the whole assortment.
- Pricing decisions made by humans are subjective and always depend on the competence of the expert: the better the expert, the more beneficial the decisions.
- It is impossible to repeat successful pricing decisions since they are based on the intuition of a specific manager and cannot be “dissected” and transformed into an action plan.
- It is hard to onboard a new pricing professional since there is no recorded pricing history of the retailer.
Machine Learning in Retail: the Benefits
AI-based algorithms “eat” all the available data points about all the transactions in the retailer’s POS pricing history, as well as the data about seasonality and customer behavior, among other parameters, and learn from them. They store all the bits of both bad and pleasant experience, analyze them and make them accessible to retail teams anytime.
Besides, the factor in the retailer’s business needs and goals when making predictions.
With Maching Learning solutions, retailers can:
- Predict the impact of every pricing decision.
- Repeat successful pricing decisions.
- Focus on high-level tasks instead of collecting data.
- Easily onboard a recently hired manager since all the data is accurate, structured and stored in one place.
- Monitor their position in the market in real time.
In-house solutions require substantial funding and constant support of the IT department, which makes them economically unfeasible in most cases. Therefore, it is much easier for retailers to benefit from external out-of-box solutions for scraping prices.
The providers of such tools create an AI-powered model to predict demand and sales, which does it by establishing relationships between relevant variables and data points. The best models can boast up to a 98% prediction accuracy.
Why AI Integration Is Problematic
Despite the apparent advantages of ML solutions, they are still very rare in the retail landscape for the following reasons:
- Lack of high-quality data: to operate effectively, algorithms require structured and accurate data covering at least three years (which is rarely the case with most retailers).
- Complex integration: the whole retail team—from managers to executives—has to be involved in the integration.
- Trust issues: managers who have been basing their pricing decisions on intuition for years find it difficult to trust pricing recommendations made by a machine since they do not know how exactly the solution works.
Conclusion
AI-powered tools for pricing tackle a wide range of problems retailers have today. They are much faster than humans, mistake-free, never get tired, can process enormous amounts of data, store data in the same format and in one place, while streamlining making pricing predictions and teaching new team members.
However, businesses are still hesitant when it comes to letting machines boost their revenue for some reasons. These include the algorithms being a “black box” (meaning it is difficult to understand how they work), retailers having an insufficient amount of high-quality data and the necessity to involve everyone responsible for pricing in the integration. All these fears disappear when the retailer experiences the AI-driven margin/sales growth.
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