Recommendation engines predict a buyer’s preference for a product. The preference target number is used, among other things, to:
In our analyses of social media, we read texts from many sources, including websites, facebook, twitter and other social channels. We collect and analyze the texts. Our solutions answer questions such as:
Fraud and deceit can today be detected through the massive and intelligent use of data analysis, computer science, machine learning, language processing and graph analysis. We collect data, analyze these and detect suspicious discrepancies.
Analysis of customer service center. We analyze speech and texts to identify your customers’ implicitly expressed complaints, inquiries, opinions and attitudes.
Chronos has its own analytics solution that can be tailored and connected to most help desk systems. We collaborate with the consulting company Effecto Consulting, which has extensive experience with customer service processes.
Self-learning systems can learn which facts influence and determine the collection of facts and decisions in the case processing. Based on historical decisions, and the most complete information possible leading to the decision, we build a self-learning system that carries out the case processing. The basic information can be pure structured data in a case processing system, all associated documents, as well as any external sources.
Today, the prices of an increasing number of products are changing dynamically and at an ever-faster pace. Prices can change based on time, demand, inventory, etc. Trading exchanges, such as the energy market, have ongoing dynamic pricing. Chronos sets up self-learning systems that can automatically negotiate a price based on the factors and guidelines decided by the company.
In capacity planning, the business estimates and plans the need for production capacity based on demand for products, infrastructure or services. We find the optimal workload the company can complete taking into account quality problems, delays, material handling, etc. Unlike traditional so-called "integer programming" and "dynamic programming", we use intelligent learning algorithms for this.
We analyze historical business data and external data that correlate to make good predictions.
We predict sales, earnings and resource needs and other projections of time series. We design, build and tune advanced solutions based on advanced statistical learning algorithms and neural networks.
We optimize traffic in logistics and material handling systems. Traditionally, this has been optimized by simulation models and capacity tests in the plant.
We build so-called reinforcing learning models that converge towards optimal performance based on information about historical and ongoing performance and ongoing feedback in production.
Chronos uses advanced algorithms to uncover customer groups and customers at high risk of terminating their customer relationship. We analyze what it takes to retain these customers.