The discussion about AI has been going on for years, and lately it’s getting even more intense. Furthermore, it’s been currently moving from the phase of considerations to reality and changing the form of all industries in an unprecedented way, including retail. According to WBR Insights, more than half (57%) of traders are planning to invest in AI within 5 years. The most common reason for that is optimization of logistics and customer service improvement. So if your competitors are not using AI yet, they will probably start soon.

If enterprises want to achieve long-term prosperity, they can’t rely on seasonality and historical data. The new approach is based on accuracy, precision and real data that can be provided only by modern technology. This technology complemented by the internet of things and real data offer tools for a much higher level of customer service. And together with it, a significant competitive advantage. But when it comes to retail, pricing is the most important tool. Traders that are able to use AI for pricing optimization will definitely get ahead of their competitors.

Where can AI help us?

Enterprises used to rely on estimations when setting prices. However, a strategic approach to this tool is needed in today’s competitive environment. Pricing is a tool that has the potential to create a strong demand and at the same time, complement the optimization of goods, business team and distribution. The pricing policy should also correspond with the brand and its position on the market. If we sum it up, traders need to set the right price in the right place, at the right time.

Traditional price optimization was based on mathematical models that analyzed how customers react to various price levels of products and services in individual distribution channels. In this manner, it was possible to set prices that pointed to meeting the company goals and profit maximization.

The present is based on data. Price optimization can lean on a whole myriad of technological tools. Machine learning sets pricing procedures for various product groups (the whole range of goods or individual product series) within a specific time (season, weeks, months, …) and for specified locations (price zones, online, shops, …). Then we can move to reactive and proactive behaviour, thanks to AI and predictive models.

Prediction instead of historical data

Traditionally, vendors used to rely on historical data when setting prices. Based on customer behaviour in the past, they were trying to predict the nature of their future activity. That’s being changed with the arrival of AI, because technologies are able to work with hundreds and even thousands of variables. From long-term behaviour patterns of customers to external factors like weather, events of various characters or Public Holidays.

AI and machine learning can go much deeper when it comes to all of these factors and therefore, offer information of a higher quality than it was possible to obtain earlier. That could help traders analyze the behaviour patterns of customers and also the competition and count the probability of specific demand levels in detail. With these tools, it is possible to influence customer preferences and then the demand and offer the customers personalized services at the top level. The most advanced solutions based on artificial intelligence do not only provide information and predictive tools. They can optimize pricing based on accurate real-time data, delivering much better results in terms of turnover and sustainability.

Better prices, better economic result

AI and machine learning enable traders to use dynamic price models using which it is possible to prevent warehouse surpluses and lower volume of unsold goods overall. It’s also possible to use strategies that take end of season into account, or the need to sell out remaining items.

Another area where AI is very useful is waste reduction. Thanks to an accurate demand prediction and corresponding pricing strategies, the enterprise is able to optimize the movement of goods in stores. Based on the understanding of the complicated relationship between the price movement and demand, the enterprise can be sure that it will get the goods just where it’s needed. 

The right price thanks to the right decisions

If retailers have the opportunity to use the data from sales and get a deeper understanding of the demand using AI tools and machine learning, they are able to act strategically and plan optimal customer service better.

In spite of all of its benefits, AI raises concerns over its functioning and impact on jobs. However, the truth is that technologies like machine learning and artificial intelligence help a greater and greater number of traders to reach profitability and their employees to focus on the best customer service possible. And the most important fact is that technologies exude any estimates and assumptions from pricing, which eliminate the risk of making bad decisions.