Big data, artificial intelligence and machine learning are the key to streamlining invoice auditing and increasing customer loyalty through digital assistants and personalized offers. Business can take advantage of a world of opportunities to unlock greater value – a must in today’s world.
In Germany, many companies have already started benefiting from the use of big data by reducing business risks or tailoring products more individually to their customers, to name just a few examples. The potential of big data is made particularly clear by the growing use of artificial intelligence and machine learning. As a result, it even offers inexperienced companies an incentive to address the issue plus new opportunities for the automation of work processes. These new technologies also mean a chance to personalize products and the way businesses talk to their customers, as illustrated by the following examples:
1. Meet customer needs better with personalization
Sales and marketing at many companies already use data analysis to segment customer groups and structure reports for management. However, personalized communication that addresses each customer individually is still a rarity. Digital businesses are leading the way in demonstrating how embracing personalization can be a success. Publishers, for example, can benefit by turning readers who consume news for free online into paying subscribers through personalization and custom-tailored offers – a customary approach in e-commerce too. Big data and machine learning make such individualized communication with substantial added value for customers possible. Today, artificial intelligence can help support the increasing use of digital assistants to communicate with customers across channels rather than just targeting them with marketing campaigns.
But digital businesses should not be the only ones conquering and controlling the point at which companies and customers interact. The logistics and industrial sectors also need to be active in this field. Transport companies, for example, can use big data to personalize their online portals and turn to artificial intelligence to further individualize the entire order process and increase customer loyalty.
As a result, big data and artificial intelligence expand upon traditional customer relationship management (CRM) while increasing both efficiency and customer satisfaction. In the future, artificial intelligence will be an essential part of every point at which businesses and customers interact. Chatbots are just one example of new, language-based interfaces.
2. Use unstructured data with machine learning
The digitization of documents, patents and maintenance reports, or of entire archives and image databases, generates a wide range of unstructured mass data that is not easily accessible for conventional analysis. Businesses can also use machine learning to transform this data into value-added benefits. One example is invoice auditing. Big companies receive thousands of printed invoices from various sources that have to be scanned, audited and matched with the correct cost center. Machine intelligence allows algorithms to independently learn how to audit and match invoices or identify commonly occurring errors by reviewing maintenance reports.
Machine learning is also enjoying increasing use in applications related to the Internet of Things (IoT). Smart sensors record and intelligently analyze data. One example is condition-based maintenance of equipment in buildings and factories. Sensor swarms can use vibration and noise measurements to learn to differentiate between the normal operating condition and impending damage – a feature that can be used in the maintenance of elevators, escalators and ventilation systems in buildings, as well as in highly sophisticated manufacturing machinery or machines and vehicles used in the construction sector.
Smart data analytics at Lufthansa Industry Solutions
- Conceptualization and implementation of big data solutions, from the development of architecture and implementation to staff training
- Development of analyses and reports or fully data-based minimum viable products (MVPs)
- Consultancy and use of machine learning and artificial intelligence technologies for the analysis of unstructured mass data
- Intelligent analysis of sensor data
- Development of prototypes at our Data Insight Lab
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