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.
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
Take a look at our tailored solutions.
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 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).
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.
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.
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.
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.
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.
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.
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.
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.
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...
With millions of clients conducting millions of operations every day, the bank needed to automatically assign a unique category to every banking transaction (card...
Together with the bank, we used several years’ worth of transaction descriptions and a number of transactions of a specific type to identify a...
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...
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...
The digital landscape is evolving at an unprecedented pace, and with it comes a new set of challenges and opportunities for IT professionals, data...