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Predictive Analytics — Advanced Solutions for Your Business

Our predictive analytics helps businesses tackle problems, seize opportunities, and improve overall performance by using data to inform their decisions.

Background

The Road to Growth

Our custom big data predictive analytics solutions offer a range of substantial benefits to businesses, including B2B companies using predictive analytics for sales.

01
Data-driven insights that
empower business.
02
Advanced algorithms
to identify risks.
03
Understanding customer
preferences to improve a journey.
04
Boosting sales through
targeted marketing.
05
Streamlining processes
for efficient operations.
06
Addressing issues
and anomalies.

Cases of Using Artificial Intelligence and Machine Learning

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

Emotion Tracker

For a banking institute, we implemented an advanced AI-driven system using machine learning and facial recognition to track customer emotions during interactions with bank managers. Cameras analyze real-time emotions (positive, negative, neutral) and conversation flow, providing insights into customer satisfaction and employee performance. This enables the Client to optimize operations, reduce inefficiencies, and cut costs while improving service quality.
15%

CX improvement

7%

cost reduction

Alex Rasowsky photo

Alex Rasowsky

CTO Banking company
View case study
Emotion Tracker preview
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They delivered a successful AI model that integrated well into the overall solution and exceeded expectations for accuracy.

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.
5%

customer retention boost

25%

profit growth

Christopher Loss photo

Christopher Loss

CEO Dayrize Co, Restaurant chain
View case study
Client Identification preview
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The team has met all requirements. DATAFOREST produces high-quality deliverables on time and at excellent value.

Insurance Profitability Analysis Tool

This project involved developing a tailor-made data analysis tool for a U.S. insurance provider who were facing challenges analyzing a significant volume of data. The Client needed a professional and customized solution which would enable effective analysis of their data and provide actionable insights to improve their business operations. Our solution delivers real-time processing of data, flexible filtering capabilities through dashboards, and also supports dashboards detailing the evaluation of insurance loss or profit by industry vertical. Additionally, a predictive model for profitable insurance cases was built using historical data, and a reporting system was created to show significant factors and profitability based on different metrics.
> 10TB

data processed

89%

accuracy improvement

Sean B. photo

Sean B.

CEO Insurance provider
View case study
Insurance Profitability Analysis Tool case preview image
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Great work! The team provided an excellent solution for consolidating our data from multiple sources and creating valuable insights for our business.

Show all Success stories

Critical Phases in Our Advanced Predictive Analytics Process

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Problem Definition
In this initial phase, we work closely with your team to pinpoint the specific challenges or opportunities that predictive analytics services can address, ensuring alignment with your goals.
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Exploratory Data Analytics
This stage calls for exploring the data to identify patterns, anomalies, and potential variables that can influence the predictive models, allowing us to understand the problem's nuances better.
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Interpretation and Insights
We provide in-depth insights and actionable interpretations of the analytics results, enabling data-informed decision-making and strategy formulation.
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Data Collection and Cleaning
We gather and prepare the relevant data, cleaning and structuring it to ensure its quality and compatibility with the analysis process.
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Model Selection and Development
Leveraging state-of-the-art algorithms and techniques, we construct predictive models tailored to your problem, fine-tuning them for accuracy and relevance.
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Validation and Testing
Rigorous testing and validation procedures are implemented to assess the performance and generalizability of the predictive models, ensuring their effectiveness.

What Data Science technologies do we use?

The tool for implementing the methods in the data science service is the program code, which can be divided into levels of integration as follows:
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
consultation icon

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FAQ

Is predictive analytics part of data science?
How much data do I need for a predictive analytics project?
How scalable is predictive analytics for businesses of different sizes?
How accurate are the predictions generated through predictive analytics?
What are some everyday use cases for predictive analytics?
What tools or software are commonly used for predictive analytics?
Can predictive analytics help in identifying trends and patterns in data?
How can businesses ensure the privacy and security of their data in predictive analytics?
Can predictive analytics help with resource optimization and cost reduction?
Can predictive analytics be integrated with existing business intelligence systems?
How can businesses measure the success and impact of predictive analytics?
Can data mining and predictive analytics be applied in various industries or domains?
What is the difference between predictive analytics and forecasting?
How does BI reporting differ from predictive analytics?
Can companies use data science, predictive analytics, and big data at the same time?
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Let’s discuss your project

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We will carefully check and get back to you with the next steps.

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DataForest, Head of Sales Department
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DataForest company founder
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