Artificial Intelligence in Retail

Increase conversions and understand your customers better using intelligent systems


How AI affects challenges retailers face

Upgrade your pricing planning

Today, most retailers apply expert-based pricing to save time spent on analyzing large volumes of online and offline price data. Machine learning allows you to automatically handle historical and competitive data and offer pricing recommendations. Unlike human managers, it can detect hidden relations between different variables and provide accurate ROI prediction.

Engage and support customers

Any activity leading to better customer service will bring retailers massive payoffs - especially if it can be done without allocating more resources. AI presents an opportunity to respond to customer requests faster and address arising issues instantly. Computer vision allows for improving security and plan store layout for better engagement. And analytics tools will help you learn about purchasing trends and predict customer satisfaction.

Automate warehouse management

Effective supply chain operations rely heavily on how warehouses are run: their design and layout, accuracy of inventory counts and locations, and organized packing and shipping processes. Most inventory mistakes are the result of human error or manual quality control. While automating warehouse work with robots may initially be a serious disruption, computer vision solutions or sorting algorithms can eliminate most mistakes and optimize supply chain management.

Uncover opportunities of AI for Retail

Supply chain planning based on big data

Utilize big data analytics in retail using machine learning techniques and be rewarded with an expanded analysis of your supply management productivity: demand forecasting, AI-driven production planning and scheduling, personalized customer experience, etc.

Inventory management using prediction techniques

Keep track of products using automated monitoring systems. Improve customer relationships by recommending goods based on historical data. Examine retail customer analytics to predict product availability with great accuracy.

Price optimization to support your pricing strategy

Make use of big data to predict whether specific user groups will buy specific goods at a specific price. Predictive analytics in retail lets you transform decision-making to create the best pricing strategies for promotions and discounts.

Market basket analysis for customer research

Reveal the hidden relationships between different products to understand customers’ purchasing behavior. Support your promotion strategy using data about different store locations, demographics, and seasons.

Lifetime value prediction to plan customer relationships

Accurately estimate the reasonable cost of acquiring or retaining given groups of customers. Calculate what future income a certain customer can generate.


Take Our Client's Word for It

Denys Arysmiatov

Denys Arysmiatov,Head of Promotion Department, Homsters

At Homsters we strive to help buyers of new-built property find the best offers by providing all information needed to make a choice. AltexSoft team did a great job developing a recommender engine for search pages. Initially, we had the same results for all visitors.  As part of the cooperation, we've created a solution that track the user behavior, analyzes it and personalizes the list of properties, that may be relevant for each particular visitor. We’ve managed to considerably improve engagement metrics on the website and increase the percentage of users that have contacted developers using our platforms.
Steve Mack

Steve Mack,CEO at Bravo Store Systems, United States

We have always looked for external resources that matched our core values and with AltexSoft and the leadership of Oleksandr Medovoi we finally found a smart and conscientious team. I have personally referred AltexSoft and highly recommend.
Kelly York

Kelly York,Director of Software Development at Bravo Store Systems, United States

AltexSoft listened to our needs and made sure we were getting the correct value for the service provided. Their leadership recognized our requirements and their groups efforts materialized. It became a very cohesive relationship.
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