How AI Revolutionizes Retail

AI and data-driven applications enable a holistic, near-real-time view of the entire retail business — demand, supply chain, distribution, customer interaction, market dynamics, and emerging trends.

George Krasadakis
4 min readMar 28, 2022
Special prices promo — pixabay

Consumers expect the best experience at the lowest price, while Retailers compete on multiple fronts. The growing, intense competition puts extreme pressure on prices and raises expectations regarding the fast delivery of goods and the overall user experience.

To thrive in this competitive landscape, retailers need to find ways to reduce costs and optimize their key processes. While data seems to be the answer to these challenges, companies very often struggle to effectively process the massive amounts of data they accumulate; they struggle to derive actionable insights that can help them stay competitive and meet the expectations of the intensifying market.

In this context, the impact of AI on Retail is indeed transformational.
On the one hand, AI helps Retailers to streamline their key processes and dramatically reduce their operational costs. For instance, inventory optimization helps to maintain the right amount of stock and protect cashflows, while effective orchestration of fleets can reduce delivery times and the total cost of operation, through more efficient, adaptive route planning.

Similarly, the predictive power of machine learning models can be used to optimize customer service operations: by using highly accurate predictions of the volume and type of customer requests, Retailers can better plan their resources — customer service agents and teams. Moreover, automated handling of incoming customer requests — e.g., a smarter assignment of tickets to the most skilled, available agent — can significantly reduce waiting times and increase the effectiveness of customer care. AI enables better customer experiences, at a lower cost.

On the other hand, AI helps Retailers expand their business through new growth opportunities. Advanced analytical models allow an unprecedented level of customer understanding, which translates to opportunities for smarter, personalized customer communication — at the right moment, channel, with the right narrative.

At the same time, AI enables a deep understanding of the customer base in its entirety — through actionable insights that can be used to spot opportunities and experiment with innovative ideas in rapid build-measure-learn cycles.

The case for Price Optimization.

Consider how AI-powered Price Optimization creates value for Retailers. A Price Optimization system helps set the right price for each product, to ensure competitiveness but also a sufficient margin and an overall profitability level; it enables Retailers to maintain a central ‘pricing intelligence data store’ — that powers numerous business applications, and new, personalized customer experiences (personalized offers, discounts, special campaigns, and more).
Price Optimization has a remarkable potential for business impact and economic value. It may drive significant business impact on various performance metrics — among others, sales and profitability figures. Used in combination with personalized offers and smart discount strategies, Price Optimization can drive customer engagement and accelerate customer base growth by improving customer acquisition.

However, building a good price optimization system is not easy.

Such a system must be able to estimate effective price ranges, for each of the enlisted products, at any point in time. Considering the number of products in a typical retail product catalog, the seasonal demand patterns, and the heterogeneity of the data that must be processed, developing an adaptive, smart pricing strategy is a complex problem that can be best addressed by using advanced Machine Learning and related analytical technologies.

How such a pricing optimization system would look like?

The AI engine of such a system is fed by the retailer’s entire product catalog, sales history, user search history, customer insights along with public domain information regarding consumer trends, competitor pricing, and product specification and metadata. The AI engine processes this rich data set to identify seasonality and other price variation patterns, per product. By applying strategic parameters such as sales goals, target margins, supply predictions, and estimated product availability/ stock levels, such a system can recommend an effective price range for every product in the catalog, at any point in time and potentially against a given scenario (i.e., product listing vs personalized offers for specific customers that also take into account customer’s lifetime value).

Moreover, a modern pricing optimization system would offer APIs to allow easy integration with enterprise technologies — enabling internal systems to query the effective price for a given product; or, to export an optimal pricing scheme per product, product category, or even the entire product catalog.
Special tools on top of this pricing intelligence data store, allow business users to shape multiple different pricing strategies interactively -e.g., by scheduling price adjustments across categories and scenarios. The underlying AI engine can then perform sales simulation over a period — it uses estimated price sensitivity for each of the customers — to predict sales and assess each of the pricing strategies, based on their expected commercial/ financial impact.

Machine Learning and data-driven business systems enable a holistic, near-real-time view of the entire retail business — demand, supply chain, distribution, customer interaction, market dynamics, and emerging trends. This holistic view supports better, faster decision-making and a more effective impact measurement framework. Artificial Intelligence powers smarter, personalized consumer experiences, both digital and in-store.



George Krasadakis

Technology & Product Director - Corporate Innovation - Data & Artificial Intelligence. Author of Opinions and views are my own