Digital Retail with
VOLTERA
The objective of data engineering in retail is to improve operational efficiency, reduce costs, increase sales, and deliver a better shopping experience for customers.
Our Services in the Retail Industry
The new retail world needs data engineering to make sense of all the info, run things smoothly, and treat customers like VIPs with intelligent tricks.
Customized E-Commerce Data Solutions
Imagine our data engineering solutions are like building blocks. Now, in the retail world, we take these blocks and tweak them to fit the specific needs of your retail business.
Retail Efficiency with a Unified Inventory Central Nervous System
Don’t know how to manage and integrate data from different sources? We create a centralized data warehouse and implement integration solutions to streamline retail data management.
Don’t make the process harder than it is.
Jack Welch, American business executive
Tailored Retail Solutions Kick Business Goals
Customized solutions in retail help stores predict trends accurately, do tasks faster, make more sales, manage stuff smoothly, save money, and keep everyone smiling!
Better decision-making through trend
analysis and actionable insights.
Quicker order processing and fewer
errors in in-store operations.
Boosting targeted promotions and
raising purchase likelihood.
Higher conversions, satisfied
customers, and increased revenue.
Products are available where
and when needed.
Standing out with tailored
customer experiences.
Cutting costs through reduced
errors and manual work.
Adapting to changing retail dynamics
for sustained relevance.
Boost Work Efficiency and Accuracy with Expert Machine Learning Support.
Get in Touch Now!
Cases of Using Artificial Intelligence and Machine Learning
Check out a few case studies that show why VOLTERA will meet your business needs.
Stock relocation solution
The client was faced with the challenge of creating an optimal assortment list for more than 2,000 drugstores located in 30 different regions. They turned to us for a solution. We used a mathematical model and AI algorithms that considered location, housing density and proximity to key locations to determine an optimal assortment list for each store. By integrating with POS terminals, we were able to improve sales and help the client to streamline its product offerings.
Client Identification
The client wanted to provide the highest quality service to its customers. To achieve this, they needed to find the best way to collect information about customer preferences and build an optimal tracking system for customer behavior. To solve this challenge, we built a recommendation and customer behavior tracking system using advanced analytics, Face Recognition, Computer Vision, and AI technologies. This system helped the club staff to build customer loyalty and create a top-notch experience for their customers.
Performance Measurement
The Retail company struggled with controlling sales and monitoring employees’ performance. We implemented a software solution that tracks sales, customer service, and employee performance in real-time. The system also provides recommendations for improvements, helping the company increase profits and improve customer service.
Supply chain dashboard
The client needed to optimize the work of employees by building a data source integration and reporting system to use at different management levels. Ultimately, we developed a system that unifies relevant data from all sources and stores them in a structured form, which saves more than 900 hours of manual work monthly.
What Data Science technologies do we use?
Pandas
SciPy
TensorFlow
Numpy
ADTK
DBscan
G. AutoML
Keras
MLFlow
Natural L. AI
NLTK
OpenCV
Pillow
PyOD
PyTorch
FB Prophet
SageMaker
Scikit-learn
SpaCy
XGBoost
YOLO
Steps
Towards Good Development
These data engineering development stages ensure that solutions are well-designed, thoroughly tested, and aligned with business objectives.
Step 1 of 7
Initial Project Assessment
and Definition
In the early phases of our data engineering development process, we engage in a free consultation to gauge project compatibility. During the discovery and feasibility analysis, we adapt to your needs, whether it’s high-level requirements. We gather information to define project scope through discussions, including feature lists, data fields, and solution architecture. We craft a project plan to guide our progress, reflecting our dedication to achieving project goals and delivering effective data engineering solutions.
Step 2 of 7
Discovery
So, you have finally decided that you are ready to cooperate with DATAFOREST.
The discovery stage involves delving into the details of the project. Data engineers gather requirements, analyze existing systems, and understand the needs of the business. This step is crucial for laying the groundwork for development, as it ensures that the project aligns with business goals and user needs.
Step 3 of 7
Tech Design and Backlog Planning
In this stage, the technical architecture and design of the solution are formulated. Data engineers plan how data will be collected, stored, processed, and presented. Simultaneously, the project backlog is created — a list of tasks and features to be developed. This backlog is prioritized, ensuring that high-priority items are addressed first.
Step 4 of 7
Development Based on Sprints
Development takes place in iterative cycles known as sprints. During each sprint, the development team tackles tasks from the backlog. The team focuses on coding, testing, and integrating the components. At the end of each sprint, a functional part of the solution is ready for review.
Step 5 of 7
Project Wide QA
Quality Assurance is an ongoing process that permeates the entire project development lifecycle. It ensures rigorous testing, identification, and resolution of any bugs or issues to guarantee the solution’s smooth operation, compliance with requirements, and alignment with quality standards. The solution is prepared for release as QA activities persist and necessary adjustments are continuously implemented.
Step 6 of 7
Deployment and Rollout
The deployment phase involves releasing the solution to the production environment, making it accessible to users. It requires careful planning to ensure a seamless transition and minimal disruption. After deployment, the rollout phase begins, involving training for users and ongoing support to address any hiccups.
Step 7 of 7
Support and Continuous Improvement
In the final stages, we ensure ongoing excellence. We guarantee optimal performance and swiftly address any issues. Simultaneously, our feedback process empowers us to continuously enhance the solution based on user insights, aligning it with evolving needs and driving continuous innovation.
