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Digital Retail with DATAFOREST

The objective of data engineering in retail is to improve operational efficiency, reduce costs, increase sales, and deliver a better shopping experience for customers.

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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.
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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.
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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.
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Web Applications enhance
customer experiences

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

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.
02
Quicker order processing and fewer
errors in in-store operations.
03
Boosting targeted promotions and
raising purchase likelihood.
04
Higher conversions, satisfied
customers, and increased revenue.
05
Products are available where
and when needed.
06
Standing out with tailored
customer experiences.
07
Cutting costs through reduced
errors and manual work.
08
Adapting to changing retail dynamics
for sustained relevance.
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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.

Employee Tracker

The large Retail company was facing a significant challenge in managing and tracking our employees' working hours and needed a solution that would automate the process and ensure accuracy. We developed a system for counting employees' working hours. Employees simply approach the device upon arrival and the system automatically identifies them and records their check-in time.
100h+

manual work reduced

13%

work experience boost

Bernd Herzmann photo

Bernd Herzmann

CTO Retail company
View case study
Employee Tracker preview
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DATAFOREST has an excellent workflow and provide constant and close communication. The team brings in a range of technical talent to address issues as they arise.

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.
17%

increase in sales

15%

revenue boost

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Amir R.

CEO Fashion Retailer
View case study
Performance Measurement preview
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They easily understand industry-specific data and KPIs, and their efficiency as a team allows them to deliver results quickly.

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.
900h+

manual work reduced

100+

system integrations

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Michelle Nguyen

Senior Supply Chain Transformation Manager Unilever, World’s Largest Consumer Goods Company
View case study
Supply chain dashboard case image
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Their technical knowledge and skills offer great advantages. The entire team has been extremely professional.

Steps
Towards Good Development

These data engineering development stages ensure that solutions are well-designed, thoroughly tested, and aligned with business objectives.

How 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

After completing all sprints, the project enters the Quality Assurance phase. The entire solution is rigorously tested to identify and fix any bugs or issues. This stage ensures the solution functions smoothly, meets requirements, and aligns with quality standards. The answer is ready for release once QA is complete and necessary adjustments are made.
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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.

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

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Still have questions about data science services?

What are the benefits of data engineering solutions for retail?
The benefits of data engineering for retailers include efficient data processing, seamless integration of diverse data sources, and the foundation for robust data-driven decision-making to optimize operations and enhance customer experiences.
How can data engineering help retailers optimize their supply chain management?
Data engineering can help retailers optimize their supply chain management by integrating and processing real-time data from various sources, 
enabling better visibility, demand forecasting, and efficient inventory management. The same is true with big data in retail.
What types of data do retailers typically collect and analyze, and how can this information be used to gain insights?
Retailers typically collect and analyze customer transaction data, website interactions, inventory levels, sales performance, customer demographics, and social media interactions to gain retail data insights into customer behavior, preferences, and market trends, enabling them to make data-driven retail decisions for targeted marketing, personalized recommendations, optimized inventory management, and improved customer experiences.
How can data engineering help retailers improve their customer experiences?
Data engineering can help retailers improve customer experiences by efficiently processing and integrating customer data from various sources, enabling personalized product recommendations, targeted marketing, and seamless omnichannel experiences.
What challenges do retailers face in data engineering, and how can these challenges be addressed?
Retailers may face challenges in data engineering related to data quality, data integration from disparate sources, scalability of data pipelines, and ensuring data privacy and security, which can be addressed through data cleansing, adopting modern data integration tools, optimizing data pipelines, and implementing robust data governance practices.
What is the role of data engineering in e-commerce, and how can it improve sales and customer loyalty?
The role of data engineering in e-commerce is to efficiently collect, process, and integrate vast amounts of data from various sources, enabling personalized product recommendations, targeted marketing, and seamless customer experiences, ultimately leading to increased sales and improved customer loyalty.
What is the role of data engineering in brick-and-mortar retail, and how can it improve operations and customer experiences?
The role of data engineering in brick-and-mortar retail is to collect, process, and analyze in-store data, enabling retailers to optimize store layouts, manage inventory efficiently, and provide personalized customer experiences, ultimately improving overall operations and enhancing customer satisfaction.
How do data storage solutions impact retailers' ability to manage
and analyze data, and what solutions do you offer?
Data storage solutions significantly impact retailers' ability to efficiently manage and analyze large volumes of data, and OpenAI offers data storage solutions that are scalable, secure, and designed to meet retailers' specific needs, providing seamless access to retail pricing data for advanced analytics and decision-making.
How do real-time data processing technologies like Apache Kafka, Spark Streaming, and Flink help retailers gain insights and make decisions?
Real-time data processing technologies like Apache Kafka, Spark Streaming, and Flink enable retailers to process and analyze data as it arrives. This allows them to gain immediate retail industry data analysis into customer behavior, inventory levels, and market trends, enabling data-driven decision-making for enhanced customer experiences and optimized operations.
What steps do you take to ensure data security and privacy for retail clients?
To ensure data security and privacy for retail clients, steps include implementing encryption protocols, access controls, regular security audits, data anonymization where necessary, and compliance with relevant data protection regulations.
What data migration projects have you completed for retail clients, and how do you ensure a smooth transition?
Data migration projects for retail clients may include moving customer data, big data in retail sales, inventory information, and other relevant online retail data sets to new systems or platforms, and ensuring a smooth transition involves careful planning, testing, and validation to minimize disruptions and ensure data integrity.
What are some examples of successful retail data engineering projects you have completed, and what were the outcomes?
Examples of successful use of data in retail systems engineering projects include implementing personalized recommendation systems resulting in increased sales and customer engagement and supply chain optimization projects leading to improved inventory management and reduced operational costs.

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DataForest, Head of Sales Department
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