Advanced analytics
for your business

Decision making suppurt using advanced analytics tools

Retrospective and predictive analysis based on intellectual data processing

Implementation of up to date visualization and reporting tools

Details

Goals you can achieve
working with us:

Marketing campaign effectiveness increase

Customer churn management

Demand forecasting

Display only relevant items for each specific customer

What will we do to increase
your effectiveness:

Develop a digital transformation plan for your business

Provide products development recommendations

Evaluate projects economical effectivenes

Create reporting standards

Design and create analytical reports

Why does it make sence to work with us?

We solve your business problems

We work with both:
big businesses and small teams

We implement up-do-date
workflow methodologies

Our pricing
is highly competitive

Our team has a proven
highly qualified expertise

Our executed projects results

75% average decrease in warehousing stock

Objective:

Decrease of items stock in stores avoiding the "out of stock" situation on the shelves

97% accuracy of sales volume interval prediction VS actual sales

Solution:

The developed model predicts weekly sales volume of each item for each week of the year

Tools: R, Python

Algorithms: XGBoost, CART, k-means

10% maximum diviation
from expert forecast

Objective:

Head office financial planning automation by means of regional budgetary expences forcasting

over 120 cases
of fraud revealed

Solution:

A custom solution based on KXEN and SAP BI had been developed and implemented within customer's infrastructure. As a result, the regional subsidiaries' expences are being forecasted based on their historical costs statistics and current economic envorinment. In additional fraud detection mechanism is implemented which inputs these unexpected costs into head office's financial plan

Tools: R, KXEN, SAP BI

Algorithms: KXEN time-series algorithms

9 is the minimum quantity of clusters with clearly defined borders for targeted marketing approaches

Objective:

Differentiation of customers into groups for targeted marketing. Detection of customers who also use other providers' services

86% accuracy in customer classification, who also use competitive services

Solution:

Development of a custom analytical tool that divides the customer base into clusters defining each group's features to support the marketing department decision making. Separation of customers who also use competitive services

Tools: R, Python, Spark, Hadoop, Hive

Algorithms: XGBoost, CART, k-means, k-medoids, t-SNE

8 different data sources

Objective:

Tracking and prevention of oil refinery products losses (in volume and quality)

revealed over 1000 instancies

Solution:

Big Data platform based on Hadoop stack had beed deployed from scratch and integrated with data sources. Developed and implemented ETL (Extract, Transform, Load) processes. Created tracking and analytics modules which, together with other tools are available via the created web application

Tools: Python, JavaScript, Shell, SQL, Hadoop, HDFS, Spark, Yarn, Hive, Tez, Hue, Zookeeper, Luigi, Kafka, NiFi, PostgreSQL, Microsoft SQL Server, Oracle, Pi System, Django, AngularJS, Bootstrap, CSS, Docker, CentOS/RHEL