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Maskinlæring | Kunstig intelligens | Dyp læring

Stordata | Datavitenskap | Datavarehus

-fra data til handling

Recommender systems

Recommender systems predict what kind of preferences a buyer have for a product. The preference target number is used, among other things, to:

  • Target advertising and promotions

  • Differentiate pricing
  • Carry out cross-selling and up-selling activities

Social media analysis

In our analyses of social media, we read texts from many sources, including websites, Facebook, twitter and other social media. We collect and analyse the texts. Our solutions answer questions such as:

  • What concerns customers?
  • To what extent are customers satisfied with our business or not?
  • Who contributes to spreading good news about our business and who disseminates negative messages?

Fraud detection

Today, fraud can be detected through the massive and intelligent use of data analysis, computer science, machine learning and graph analysis. We collect data, analyse it and detect suspicious nonconformities.

Analysis of customer centre communication

Customer relation management. Analysis of customer service communication. We analyse speech and texts, and, unlike many customer service systems, we also find those of the customer's complaints, requests, opinions and attitudes that are hidden beneath the surface. Chronos has its own solution that can be tailored and connected to most customer service systems. We work with the consulting firm Effecto Consulting, which has extensive management experience in the customer centre process.

Fully automatic case processing

Eller management

Self-learning systems can learn which factors influence and determine the gathering of facts and decisions in the case management process.

Based on previous decisions, and information that led to the decision, we build self-learning systems that can replace the need for human case management efforts.

The basic information can be structured data in a case management system, all associated documents, as well as any external sources.

Dynamic pricing

Today, prices for more and more products are changing dynamically and at an ever-faster pace. Prices are subject to change based on time, demand, inventory and so on. Trade exchanges, such as the energy market, have ongoing dynamic pricing. Chronos sets up self-learning systems that can automatically negotiate prices based on the factors and guidelines set by the business.

Capacity planning

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 business can complete taking into account quality problems, delays, material handling and the like.

Unlike traditional so-called "integer programming" and "dynamic programming" we use intelligent learning algorithms for this.

Prediction of turnover, inventory, personnel and other resource needs

We make good predictions based on analyses of correlating data, for instance historical business data and external data.

We predict revenue, profit and resource needs, and other projections of time series. We design, build and tuning customised solutions based on advanced statistical learning algorithms and neural networks.

Intelligent material and goods flow

We optimize traffic in logistics and material handling systems, which, traditionally have relied on simulation models and capacity tests in the plants.

We build so-called reinforced learning models, which, using historical and ongoing performance information and feedback in production converge toward optimal performance.

Prevent customer churn

Chronos uses sophisticated algorithms to uncover customer groups and customers at risk of ending their customer relationship. We analyse what it takes to keep these customers.

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