Enkla Elbolaget supplies climate-wise electricity to both private and corporate customers. The company is very dedicated in their work for good customer service. In 2020, Enkla Elbolaget won the “Brilliant Awards – Customer Experience 2020” for best customer service in the electricity industry. On behalf of the electricity company, JUPEL does a thorough analysis of the company’s private customers, their characteristics and what drives the customers’ various decisions and behaviours. Electricity customers tend to move easily between different suppliers. For example, JUPEL identifies differences in characteristics between 1) loyal customer groups and others, 2) customer groups that have come in through different recruitment channels, 3) customers in different geographical areas and 4) customers with different consumption patterns. JUPEL’s analytics solutions enable Enkla Elbolaget to

  • offer the company’s customers better solutions
  • reach new customer groups
  • decrease churn
  • become more efficient in their customer communication
  • increase profits and competitive edge

Supplementing the company’s customer data with external data, JUPEL generates in-depth and exhaustive information about underlying mechanisms for different customer segments.

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On behalf of the Gotland Region, JUPEL carries out a statistical analysis of users and non-users of the municipality’s libraries in the project “St√§rkta bibliotek” (with state funds from the Swedish Arts Council). The purpose of the project is to identify which groups use and which groups do not use the libraries in order for the libraries to target, design and develop the activities so that they increasingly meet the objectives of the Swedish Library Act. According to the Library Act, library activities must be accessible to everyone. JUPEL combines data from the local library data system with microdata from Statistics Sweden. We provide detailed descriptions of different user segments and analyses regarding underlying factors that influence library use. Based on our analyses the municipality can take steps to optimise library operations.

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A company that works with operational support for hotels, offers its customers a Revenue Management system based on booking and competitor price data. The company is about to complement its system with data analysis functionality. JUPEL assists the company in developing statistical analysis methods for making predictions of occupancy and dynamic pricing. Our algorithms will be integrated into the company’s systems so that the users of the system (hotels and hotel chains) get a more powerful foundation for decision-making in their Revenue Management work.  

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A retail company recently decided to start working in a more data-driven way by investing in Data Science, analytics and AI solutions. The company is in the process of investigating opportunities and challenges of integrating a higher degree of data-driven procedures, and needs strategic advice. JUPEL starts by making an up-to-date description of the company’s existing data warehouse and conditions in the form of business systems (ERP/CRM/MA/Accounting/Commerce) and IT environment. We review the company’s specific needs for data-driven insights on business processes and customers. We deliver a strategic plan with recommendations for concrete analytics solutions, how these can be linked to the company’s business system and which external data sources can add additional value.

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For a telecom company having problems with large customer loss, so-called churn, where about 30% of customers leave the company every year, JUPEL does a customer analysis. Reducing customer loss is extremely valuable to the company. Research shows that the cost of attracting a new customer can be up to 10 times the cost of retaining a current one. JUPEL’s analyses help the company find which specific individuals they should target activities to, thereby preventing churn. We analyse all relevant customer data and build algorithms based on appropriate statistical models. Our delivery consists of automatically identifying the customers who are most likely to leave the company within the near future with high accuracy. The algorithms can then be implemented in the company’s business system to automate the analysis and continuously assist the marketing department with indications for the need of preventative actions. Such ongoing data analysis also enables the models and the algorithms to be continuously fine-tuned and updated.