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DATAFOREST Services for the E-Commerce Industry

Optimize business performance, enhance customer engagement, and drive innovative growth in the digital marketplace with AI-adopted technologies.

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Data Engineering Services Satisfy Needs

Scale infrastructure effectively, optimize user experience and supply chain, improve customer service with AI, and derive actionable insights.
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The Core Essence
of Data Science

Harness advanced analytical techniques and AI/ML algorithms to interpret complex data. Analyze user behavior and preferences, forecast demand, and use intelligent chatbots.
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Data Scraping and Customer
Service Automation

Gather customer feedback and queries from various platforms to train and improve AI models.
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Managing Large-Scale
Infrastructure with DataOps

Optimize data workflows, making scaling more responsive to fluctuating demands.View page
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Data Integration for Informed
Decision-Making

Aggregate and harmonize data from diverse sources, providing a comprehensive view. Combine big data from various supply chain sources.
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Web Development for
User-Friendly Trading

Create dashboards and reporting tools that are accessible through a website or an intranet.

Better Products and Experiences

Benefits of e-commerce using our services and solutions:
01
Enhanced decision-making
02
Optimized inventory management
03
Customer segmentation for
targeted marketing
04
Efficient supply chain management
05
Personalized customer experiences
06
Fraud detection and prevention
07
Predictive analytics for
future planning
08
Competitive advantage
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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 DATAFOREST will meet your business needs.

AI-Powered Web Platform for Data-Driven E-commerce Decisions

Dropship.ai is a powerful data intelligence platform that helps e-commerce businesses identify profitable products, analyze market trends, and optimize sales strategies. Using large-scale data scraping, AI-driven insights, data enrichment solutions, integrations with Shopify, Meta, and Stripe, it enables smarter product decisions and drives revenue growth.
3M+

total unique users

200K+

active users per month

Josef G. photo

Josef G.

CEO, Founder Software Development Agency
View case study
Case preview
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If we experience any problems, they come back to us with good recommendations on how the project can be improved.

Lead-collecting Web Solution

Leadmarket is the lead-collecting web tool made by Dataforest. We’ve built a solution that provides a fast and precise lead search from various sources like Google Places, Facebook Business Pages, Yelp, and Yellowpages in one place. The collected lead bases from the USA's e-commerce, insurance, retail, and finance industries can be set to auto-update as quickly as every 10 minutes!
10

minutes auto-update

904

Search categories

Leadmarket preview
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Leadmarket is the lead-collecting web solution made by Dataforest.

Optimise e-commerce with modern data management solutions

An e-commerce business uses reports from multiple platforms to inform its operations but has been storing data manually in various formats, which causes inefficiencies and inconsistencies. To optimize their analytical capabilities and drive decision-making, the client required an automated process for regular collection, processing, and consolidation of their data into a unified data warehouse. We streamlined the process of their critical metrics data into a centralized data repository. The final solution helps the client to quickly and accurately assess their business's performance, optimize their operations, and stay ahead of the competition in the dynamic e-commerce landscape.
450k

DB entries daily

10+

sources integrations

Lesley D. photo

Lesley D.

Product Owner E-commerce business
View case study
E-commerce Data Management case image preview
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We are extremely satisfied with the automated and streamlined process that DATAFOREST has provided for us.

Infrastructure Audit & Intelligent Notifications

An e-commerce company had issues with managing its complex IT infrastructure across multiple cloud providers. We helped to analyze the current architecture and develop a strategy for unification, scaling, monitoring, and notifications. As a result, we implemented a single cloud provider, CI/CD process, server unification, security and vulnerability mitigation actions, and improved reaction speed and reliability by 200%.
200%

performance boost

24/7

monitoring

Dean Schapiro photo

Dean Schapiro

Co-Founder, CTO Ecom Innovators, E-commerce company
View case study
Infrastructure audit case image
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Not only are they experts in their domains, but they are also provide perfect outcomes.

Web app for dropshippers

The Client wanted to create a web app for people who sell products online (dropshippers) to show them the most popular products and calculate the potential profits. DATAFOREST designed and built the web app from scratch, creating high-load scraping algorithms to extract data from different e-commerce marketplaces, developing AI algorithms to calculate profits, and integrating the payment system with various functionalities.
100k+

hourly users

1,5 mln+

Shopify stores

Josef G. photo

Josef G.

CEO, Founder Software Development Agency
View case study
Web app for dropshippers case image
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If we experience any problems, they come back to us with good recommendations on how the project can be improved.

Show all Success stories

What Data Science technologies do we use?

Pandas icon
Pandas
SciPy icon
SciPy
TensorFlow icon
TensorFlow
Numpy icon
Numpy
ADTK icon
ADTK
DBscan icon
DBscan
G. AutoML icon
G. AutoML
Keras icon
Keras
MLFlow icon
MLFlow
Natural L. AI icon
Natural L. AI
NLTK icon
NLTK
OpenCV icon
OpenCV
Pillow icon
Pillow
PyOD
PyOD
PyTorch icon
PyTorch
FB Prophet icon
FB Prophet
SageMaker icon
SageMaker
Scikit-learn icon
Scikit-learn
SpaCy icon
SpaCy
XGBoost icon
XGBoost
YOLO icon
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?
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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.
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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.
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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.
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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.
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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.
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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.
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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.

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Still have questions about data science services?

Can you handle large volumes of data from various sources, including sales, inventory, and customer interactions?
How does your Ecommerce Data Processing service integrate with our existing systems and processes?
How can DATAFOREST help us personalize our marketing strategies and improve customer experiences?
How long does it typically take to process and analyze our e-commerce data?
Can you tailor your E-commerce Data Processing solutions to our business needs and industry?
What types of reports and visualizations do you offer to help us monitor and measure the impact of your services on our business growth?
How can we get started with ordering E-commerce Data Processing services from DATAFOREST?
Do you offer ongoing support and assistance after implementing your data processing solutions?
How can DATAFOREST help us optimize our inventory management and supply chain operations?
How do you ensure the processed data is accessible and easily interpreted for our team?
What is the role of big data in general and big data analytics in particular for data engineering in e-commerce?

Let’s discuss your project

Share project details, like scope or challenges. We'll review and follow up with next steps.

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DataForest company founder
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