Data-Driven Decision-Making: AI & Analytics
Making good decisions requires new analytics techniques and AI/ML. We help our clients make data-driven decisions by processing data with the help of artificial intelligence (AI) before making it accessible in advanced analytics tools. In doing so, we leverage the leading AI and cloud frameworks.
Artificial intelligence and machine learning are playing an increasingly important role in companies, offering infinite ways in which they can be used and promising greater productivity and efficiency as well as new business values. Perhaps the best-known example are chatbots in customer service – virtual employees who enable 24/7 processing of customer inquiries.
In today’s companies, artificial intelligence primarily comes into play when repetitive tasks (i.e., recurring, rule-based activities) need to be automated. Accelerating data analyses with complex decision-making processes is a key part of this.
AI can support companies across a wide range of strategic objectives, getting you far closer to hitting your goals, including:
- Increasing revenue and business growth
- Increasing cost efficiency
- Improving market positioning
- Developing and implementing innovative business models
- Improving decision-making (in terms of quality, efficiency and innovation)
- Increasing customer satisfaction
- Improving working conditions and work performance
- Increasing employee productivity
- Reducing empty time, rejects and downtimes
According to IDC, 94 percent of the companies surveyed are convinced that artificial intelligence offers a significant competitive advantage. The decisive factor here is finding the right use cases.
Before you introduce AI, you first need to be clear about the specific business case you want to use it for and what you hope to gain from it. First things first, you need to identify a central area where artificial intelligence can sustainably optimize and thus contribute to the company’s success.
The use of AI solutions lends itself to all areas, including marketing, sales, logistics and customer service, to name just a few. There’s never been a better time to learn more about the potential uses of AI. We have some exciting examples to showcase just what artificial intelligence is already capable of in everyday business.
Maintenance of Devices and Machines
Production plants or machines usually go through regular maintenance cycles. Sensor-based AI systems can help optimize these cycles, with automatic classifications predicting or detecting wear and tear or incipient defects at an early stage. This means machines only have to be maintained when necessary – not proactively. Minimize your downtimes.
Product Comparisons and Market Analyses
In the digital age, it’s easily possible to keep an unbelievably close eye on the supplier market for individual products. For example, price search engines can identify all current prices for electronic items. When the big players adjust their prices, you usually find smaller retailers respond to these price adjustments just a few hours later. The goal is always to be noticed by the consumer or, if market prices are too low, to deliberately avoid demand.
This is possible for large numbers of items, as product numbers make identical items pretty easily identifiable. But when it comes to products such as clothing, things start to get trickier. While humans can easily spot any similarities, simple algorithms find that much harder. This is where artificial intelligence comes in. By automatically comparing item properties and images, similar items can be identified with ease.
Another typical use case for artificial intelligence is the subdivision of customers into different customer segments. AI is significantly more powerful than the classic ABC analysis, where customers are simply divided into segments according to a central metric.
A pretty serious number of key metrics or customer characteristics can be included in the analysis with the help of artificial intelligence, while additional information can be provided on how to speak to customers in the right way or using a targeted approach. Ideally, the result of this customer segmentation is automatically fed into the campaign management tool and updated when contact is next made with the customer. This means the campaign still has to be designed by the marketing manager and assigned to a customer segment, but the customer approach is automated.
Predicting the Ability to Pay
Artificial intelligence can help predict a customer’s ability to pay. Here, data on customers who have failed to comply with payment requests in the past creates a pretty solid basis. AI-supported algorithms use this data to predict a new customer’s willingness to pay, information which can then be used in the online store, for example, to automatically restrict the new customer’s payment terms.
Today, keywords and individual data points often hold information. Stock market prices are just simple price lists at given points in time, news agencies provide news as collections of keywords, and manufacturers initially list items as descriptions of individual properties, such as color or material.
Artificial intelligence has the power to generate complete free text from individual terms in a way that’s easy for the user to digest. Already, web pages detailing the development of stock market values, summaries of soccer matches and detailed product descriptions are all being generated using tried-and-tested methods. A quick review by one of the team and they’re ready for the consumer.
Customer care, otherwise known as giving the customer a helping hand to make the purchase, is the most common use of AI. Today, it is eminently possible to analyze how a (prospective) customer interacts with a company across all touchpoints.
This information is then used to specifically serve or speak to customers as well as to uncover and improve any weak points in said customer interaction. All this massively drives up customer satisfaction.
Plus, it also generates greater transparency about (prospective) customers, which strategically creates far-reaching added value throughout the entire customer journey.
Consistent, accurate, and unambiguous master data are the cornerstones of digitization. Data quality is an essential intangible asset. No matter your data issue, we support you throughout the process. To this end, we use initiatives such as Master Data Management (MDM), Data Fabrics and Data Lakes. Improving data quality means ensuring that business processes can be executed efficiently and accurately.
Customer experience is the driving force behind revenue and customer loyalty. In this context, data creates a directly measurable added value. We consider how data can be effectively processed, maintained, and presented to the customer. We use initiatives such as Product Information Management (PIM), Customer Data Platform (CDP), Digital Asset Management (DAM) and Publishing.
With the proliferation of cloud tools, data integration strategies have become increasingly important. Data integration comprises the methods, technologies, and tools to ensure consistent access to and provision of data for all applications and business processes across the organization. We focus on data integration tools and the integration of cloud data.