Machine Learning and Artificial Intelligence for Retail, Distribution, Supply Chain, and Logistics

We can help you develop a customized software

Companies have more data than ever. At the same time, retailers have less and less time to collect and process this data and to think about market changes. It is not surprising that artificial intelligence could be a promising solution to today's retail challenges.

Machine learning analyzes data to the next level. Using massive amounts of product and price data, sophisticated algorithms learn different pricing and sales patterns. Using an endless number of simulations, the algorithm identifies patterns that are beyond human reach. Machine learning algorithms have been proving to be effective over other methods for years now.

Machine Learning Solutions

Customer Analytics

Help algorithms to understand human speech and text to find the right information quickly, automate customer service, create chatbots for different departments, and easily find topics in text documents.

Predictive Analytics

Understand your data from the past to predict the future for eCommerce warehousing or Supply Chain. Build forecasts to understand how your company can get more profits.

Recommender Systems

Improve your conversion rate with more relevant recommendations. Create the most personalized experience for your customers.

Patterns and Forecasting

Find patterns in your historical data and dig deeper to predict trends and seasonal changes. Forecast demand for your products. Create a pricing strategy to beat competitors.

NLP (Natural Language Processing)

Analyze customers’ behavior and build segmentation models. Optimize targeting, personalization, and overall customer experience.

Computer Vision

Recognize goods to control their availability for on-time stock replenishment. Use biometrics, AR, and face recognition for automating tasks and gathering more information.

What You Can Achieve Using Machine Learning

  • Forecasting the demand for discounted goods.
  • Identify abnormal behavior to detect fraud, security issues, information breaches, medical problems, structural defects, and other malfunctions.
  • Identifying and using patterns of customer behavior in the past to predict their actions in the future.
  • Offering certain groups of buyers (segments) of certain products at the moment of greatest need
  • Replacement of sales assistants with an automated system for the selection of goods based on the experience of previous buyers and individual preferences of a particular client.
  • Determination of the optimal number of staff for high-quality customer service at a specific point at a specific time (during seasonal sales, marketing campaigns, etc.)
  • Optimization of product placement in the store (taking into account customer behavior patterns, seasonal changes, trends, etc.).
  • Optimization of warehouse storage - every centimeter of storage space will be used profitably.
  • A virtual assistant to monitor the shopping schedule and remind the buyer of the need to place a new order or select the best product based on their preferences.
  • Demonstration of targeted (depending on gender, age) digital content at points of sale to stimulate demand and increase sales.

Our Approach

Business Understanding

Data Acquisition & Understanding

  • Building data pipeline
  • Setting up environment
  • Data wrangling, exploration & cleansing

Modeling

  • Feature engineering
  • Model training
  • Model evaluation

Deployment

  • Scoring
  • Performance
  • Monitoring
  • Support

How We Work

Our main value is to deliver valuable and cost-effective solutions to our clients. That’s why we developed an approach to R&D projects that allows us to see the progress at every stage and deliver solutions incrementally, allowing clients to decide if additional efforts are worth investment or a change of direction is required.

Phase 1.1: 2–4 weeks

Feasibility study

  • Research applicable datasets in terms of data volume and set of fields; create ETL
  • Test different ML models, algorithms, libraries
Phase 1.2: 1–3 months

Building PoC

  • Chose most appropriate dataset, model and model parameters
  • Prepare ML model for a simulation with production data
  • Elaborate on a suitable integration approach
Phase 2: Duration depends on the project

Going live

  • Prepare and integrate a production ready ML model
  • Optimize and improve the model with new production data, weights, parameters
  • Improved model rollout
Phase 3

Support

  • Support and minor enchancements
  • Effectiveness monitoring

Benefits

  • Increase sales and optimize pricing
  • Increase in conversion and average check
  • Increase response from marketing campaigns
  • Reduce production, logistics, and other costs
  • Increase customer loyalty
  • Optimize warehouse and distribution
  • Improve customer experience
  • Manage customer behavior

Case Studies

Why Work with Us

Artificial Intelligence and Data Science project methodology is significantly different from traditional research for software delivery projects.

It requires companies to:

  • Develop new data science and AI skills (such as NLP, computer vision, machine learning, deep learning, etc.)
  • Build new infrastructure for big data and model deployment (often cloud based)
  • Adopt new culture of collaboration between the business and data scientists

DataArt can help to bootstrap AI capabilities, or fill data and analytics gaps for companies that do not have the expertise internally or do not want to hire new talent until the benefits of AI are proven.

DataArt focuses not only on research, but also on delivering end-to-end solutions starting with solution design and ending with deployment of ML-model and integration into the existing or newly developed client environment.

Technology

DataArt engineers work with the most popular modern technologies, including world-class cloud-based MLaaS solutions and classic or deep learning open source libraries.

MLaaS integration and training

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