Data science and advanced analytics

Machine learning algorithms can detect weaknesses in the manufacturing process as well as unfair practices by clients. As a result, you can achieve campaigns that are up to ten times more successful and expand your target audience.

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Your data is the key to your company's further development in the digital era

Advanced analytics can help you get information such as how likely your clients are to buy other products and services, what services they might be interested in or how they navigate websites and mobile apps.

Machine learning algorithms can detect weak points in the production process or unfair client behaviour. Entrust your data to our care and you will get quality and key information to work better with your clients.

By integrating and processing different data sources, we can prepare richer prediction models or more accurate segmentation. We focus on current business issues, needs and challenges.

We follow current trends and look for ways to use them for better customer knowledge, for personalized interactions based on customer needs detection (and thus better responses for customer retention, loyalty building, service delivery), for more efficient processes within the organization, etc.

Our analyses must have a purpose and a mission! We evaluate big data not only with proven traditional approaches (mathematical-statistical models), but also with modern techniques of machine learning, deep learning and text analytics (NLP).


more successful targeted campaign than when using standard procedures without the use of text analytics


The target audience reached by the right advertisement in online media has increased tenfold

Transform data into business value

Take a look at our tailored solutions.


Prediction models from historical data


Proposals for the use of real-time generated data


Help you find added value in unused data and much more

Task types in Data Science

Customer insight

  • we identify their needs
  • we derive new information
  • we recognize typical patterns
  • we create event triggers
  • we describe customers’ characteristics and behavior


  • we predict the probability of a particular event, such as
    • a customer clicking on your advertisement
    • a failure occurring on the production line

Target groups segmentation

  • we create groups – segments with similar customers so that at the same time these segments differ significantly from each other

Text analysis

  • we process data from various text sources, including
    • classifying e-mails from customers
    • identifying the main content of the message

Analysis of semistructured and unstructured data

  • we process and analyze logs, sensor, telemetry and location data
  • we can enrich your data and analysis with external sources and open data

Geolocation data analysis

  • we enrich the analyzes with location data to calculate:
    • driving distances
    • nearest stores
    • optimal distribution of branches
    • availability of communication networks

Product recommendation

  • from data on customer behaviour, activities, preferences, we infer what the selected customer might want based on similarities with other customers (pattern analysis)

Web analytics

  • we extract your data from the web and digital channels
  • we evaluate their contribution to sales
  • we connect them into a 360° view of the customer and use them in other analyses

Get inspired with our use cases

Billions of sensor data from the new electric car are processed by the Data Analytics Platform (DAP) in less than 5 minutes

Every second of driving generates thousands of records from sensors. The goal of the project at a major car manufacturer was to efficiently process these records and present the results in managerial dashboards.

Thanks to DAP, the data is processed into a comprehensible format within minutes.

Managers adjust the analyses and reports themselves.

The rate of customer attrition after the warranty period decreased by 33%

The automotive company wanted to retain its customers and offer them additional service and sales services.

Predictive analytics, with the help of machine learning, helped to reveal the probability with which a customer would leave the authorized service.

As a result, the precisely targeted campaign was three times more successful than the campaign in the previous year.

A third of the approached customers took advantage of the offered promotions for additional services (a year earlier, only 10% of the broadly approached customers took advantage of the offer).

Customer 360 gained a richer perspective on the bank clients’ behavior

A major bank in the Czech Republic wanted to enrich its view of customer data with location attributes, and at the same time, identify branches suitable for closure.

We employed machine learning models – clustering and spatial analysis.

Thanks to big data analysis, we assisted the company in obtaining a more comprehensive Customer 360 and conducting more analyses that allow them to make better strategic decisions based on facts.

Data visualization also provided clear map outputs for business departments and managers.

Predicting account balances increased loyalty and reduced indebtedness

A major Czech bank wanted to predict the future balance of a client’s current account.

Based on historical data, we identified transaction behavior patterns and compiled an overview of future expenses. Now, the client always knows how much money remains in their account until the next income (e.g., until payday) after paying all regular/planned payments.

Standardization of projects and processes unified the approach of data science teams

At a major telco operator in the Czech Republic, we streamlined the way external and internal data scientists work on joint projects through standardization.

We started from the proven CRISP model, expanded the range of GIT functionalities used, and introduced 4 new technologies (GIT, MLflow, Papermill, PySpark). An exemplary project was created, encompassing comprehensive data science processes, including patterns/templates. We eliminated the specific procedures of individual data scientists.

Everyone adheres to standardized practices in both production and development environments. The Data Engineering department can deploy data science models more quickly. Data scientists can more easily share and collaborate on work thanks to a clearly defined structure of scripts and Jupyter Notebooks.

Estimation of client income resulted in an increased number of loan applications

A major Czech bank wanted to effectively implement digital/online processes during the COVID era, accelerate the loan process, and offer a relevant indicative loan to clients.

Using a two-phase model that draws from external and publicly available data sources, and a combination of statistical elements and machine learning, we identified significant differences between low and middle-income clients.

First, we estimated the income group affiliation, then calculated the probable income.

The indicative loan now aligns with clients’ expectations – resulting in an increased number of loan applications.

The project was completed in 6 months and was conducted entirely online, including all meetings and project handovers to the client.

Overdraft offers for corporate clients are 6 times more precise

We have delivered a propensity-to-buy model to the marketing department of a major Czech bank’s retail banking division. This model detects greater interest in a selected product or service.

  • Using machine learning, we estimate the chance of setting up a corporate overdraft for clients from the segment of small and medium-sized businesses.
  • In the set of clients chosen by the model, the chance of selling an overdraft in a targeted campaign is almost 6 times higher than in a blanket campaign.
  • We evaluate this using Lift, a popular marketing metric. This gauges how many times higher the probability is of selling a product or service to a select group of people than to an indiscriminate group.

Assessing the client’s real creditworthiness

We helped a large Czech bank to better assess its clients’ true creditworthiness. Today, many people use the services of multiple banks, and this may not be the client’s primary bank. In order to communicate with the client appropriately and offer them relevant products, the bank needed to know its clients’ true creditworthiness.

  • This knowledge makes it possible for the bank to work better with clients both in the branch and in marketing campaigns. It allows the bank to pre-select and, consequently, offer products and services that correspond to the client’s actual needs and possibilities.
  • We have reduced the weight given to primary indicators such as current account balances and inbound transaction volumes. We calculate creditworthiness of the basis of a copious variety of indicators (financial, behavioral, and so on).
  • We take into account client characteristics emerging from historical transaction data, use of the current account and other banking products and services, the client’s payment categories, etc.
  • We have divided an apparently disparate group of clients into several homogenous sub-groups with similar characteristics, with whom personal bankers can now more easily find common ground.

Just-in-time loan offers

Together with the bank, we used several years’ worth of transaction descriptions and a number of transactions of a specific type to identify a family with children.

  • Analyses of their behavior patterns have shown that customers in this segment spend the most in September. As a result, the bank offered this family a personalized loan product at the right time.
  • The approach was 10 times more successful than using standard procedures without analytics on the top of Big Data. Usually, simpler rules are used for credit offers (for example, account balance and credit limit).

Case studies

Equa Bank clients were fully migrated to Raiffeisenbank in 12 hours

When Equa Bank was being mergedintoRaiffeisenbank in November 2022, we handled the migration of Equa Bank’s client data into Raiffeisenbank’s CRM system. We also ensured the client master data was propagated to the bank’s core systems.  

hours instead of 3 weeks – shorter live migration thanks to 10 months of testing and agile development

subjects to migrate

people - each with 200 attributes, added to Raiffeisenbank’s client base after the acquisition of Equa Bank

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ŠKODA AUTO: data transfers on analytics platform are comprehensively managed and monitored by Adastra’s Adoki

In 2018-19, Adastra built an on-premise Data Analytics Platform (DAP) at ŠKODA AUTO. Its purpose? To visualize data and use advanced analytics and artificial...

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Automatic categorization for 98.5% of card transactions

With millions of clients conducting millions of operations every day, the bank needed to automatically assign a unique category to every banking transaction (card...

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Just-in-time loan offers: a 10x higher conversion rate

Together with the bank, we used several years’ worth of transaction descriptions and a number of transactions of a specific type to identify a...

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Get inspired on our blog

Adastra Group’s Revenue Exceeds 5 Billion CZK for the First Time

In 2022, Adastra Group's revenue reached 5.6 billion CZK, marking the first time in the company's history that it surpassed the 5 billion CZK...

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Adastra becomes a strategic partner of Škoda Auto University. Its specialists will teach data management, data science, and artificial intelligence courses

Adastra becomes a long-term strategic partner of Škoda Auto University. Its experts will be involved in teaching data management, data science, and artificial intelligence...

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Unraveling the Future: Top 8 Data Management Trends for 2023 and Beyond 

The digital landscape is evolving at an unprecedented pace, and with it comes a new set of challenges and opportunities for IT professionals, data...

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Ľuboslav Gabaľ
Business development director-international