Contact us

Retail

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.

Let your data create value

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.

Data Science for retail trends
and optimizing inventory

Retailers analyze sales data, customer behaviors, and market trends to predict future demand, identify popular products, and plan inventory accordingly.

Data Scraping gathers
external data for insights

Scraping competitor pricing data and monitoring social media trends provide insights for adjusting marketing strategies. Vendors tailor their promotions and advertisements to target audiences effectively.

Managing Large-Scale
Infrastructure with DataOps

Web applications manage customer orders, track sales data, and optimize checkout. They also facilitate customer interactions and provide personalized shopping experiences.

Data Integration provides
automation of various processes

The service automates restocking inventory when it’s low, streamlining supply chain operations and managing orders efficiently by integrating data from multiple sources (sales, inventory, and customer records).

DevOps maintains the
technology backbone

DevOps is crucial in the retail industry for creating and maintaining the systems that support online and in-store operations. It ensures that the software and tools used for automation and data processing run smoothly.

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

Simplify data acquisition with VOLTERA

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!

01 Better decision-making through trend
analysis and actionable insights.

I want it!

02 Quicker order processing and fewer
errors in in-store operations.

I want it!

03 Boosting targeted promotions and
raising purchase likelihood.

I want it!

04 Higher conversions, satisfied
customers, and increased revenue.

I want it!

05 Products are available where
and when needed.

I want it!

06 Standing out with tailored
customer experiences.

I want it!

07 Cutting costs through reduced
errors and manual work.

I want it!

08 Adapting to changing retail dynamics
for sustained relevance.

I want it!

Boost Work Efficiency and Accuracy with Expert Machine Learning Support.

Get in Touch Now!

Get in Touch

Cases of Using Artificial Intelligence and Machine Learning

Check out a few case studies that show why VOLTERA will meet your business needs.

All Success Story
Healthcare
Data Science
Data Insights & Forecasting

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.

10% productivity boost 7% increase in sales
Mark S.

Mark S.

Partner Pharmacy network,

View case study →
Stock relocation solution

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.

Christopher Loss

Christopher Loss

CEO Dayrize Co, Restaurant chain,

View case study →
Client Identification

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.

Amir R.

Amir R.

CEO, CEO Fashion Retailer

 View case study →
Performance Measurement

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.

Michelle Nguyen

Michelle Nguyen

Senior Supply Chain Transformation Manager Unilever, World’s Largest Consumer Goods Company

 View case study →
Supply chain dashboard

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.
How do we help companies?

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 1 of 5
Free consultation

carousel2

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 1 of 5
Free consultation

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 1 of 5
Free consultation

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 1 of 5
Free consultation

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 1 of 5
Free consultation

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 1 of 5
Free consultation

carousel5

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.

Step 1 of 5
Free consultation

Related articles

All publications
February 21, 2025
17 min

Custom E-commerce APIs Integrations: Types, Pros, Cons & FAQs

E-commerce

February 21, 2025
17 min

Data Analysis Leads to 3.6% Weekly Sales Growth

E-commerce

February 21, 2025
16 min

Big Data in E-commerce: Stars in the Sky

E-commerce

Still have questions about data science services?

What are the benefits of data engineering solutions for retail?
How can data engineering help retailers optimize their supply chain management?
What types of data do retailers typically collect and analyze, and how can this information be used to gain insights?
How can data engineering help retailers improve their customer experiences?
What challenges do retailers face in data engineering, and how can these challenges be addressed?
What is the role of data engineering in e-commerce, and how can it improve sales and customer loyalty?
What is the role of data engineering in brick-and-mortar retail, and how can it improve operations and customer experiences?
How do data storage solutions impact retailers' ability to manage
and analyze data, and what solutions do you offer?
How do real-time data processing technologies like Apache Kafka, Spark Streaming, and Flink help retailers gain insights and make decisions?
What steps do you take to ensure data security and privacy for retail clients?
What data migration projects have you completed for retail clients, and how do you ensure a smooth transition?
What are some examples of successful retail data engineering projects you have completed, and what were the outcomes?