Minimal change barriers as well as similar service and quality standards mean that the energy industry is subject to a huge amount of competition. Consequently, good service and custom-made tariffs are becoming increasingly important criteria for the customer when deciding on a provider. In this context, the provider's extensive volume of data can prove to be a business-crucial support feature. It is essential that data is interlinked in a targeted manner using a high-performance BI or Big Data solution.
These days, it is relatively simple for customers to change their energy supplier. Consequently the customer is happy to "churn" - i.e. change to another provider. By linking different data sources, potential "churn" candidates can be precisely identified. This means that you can take counteractive measures in good time with targeted marketing. The system will also assess the respective customer value so that you can exclusively target lucrative customer accounts.
Based on customer behaviour up to the current point in time, new offers can be individually developed for each customer. For example, in the event of the extension of a contract, you can generate a variety of options which are attractive to the customer as well as promising an increase in revenue.
Data pulled from your call centre or from social networks can contribute to a comprehensive improvement in complaint management. Targeted analysis can inform you about the topics most important to your customers so that you can optimise your products and services in a targeted manner. In addition, you can uncover weaknesses existing within the organisation and gain insights for possible staff development programmes and ideas. All this contributes to a sustainable increase in customer satisfaction and loyalty.
Energy portals are perfect sources for determining the attractiveness of your products and finding out your position in relation to the competition more precisely. The data will tell you which offers customers prefer. You can also compare and contrast typical customer groups and their preferences. Long-term comparisons will ultimately show how your position changes in relation to the competition in the case of different inquiries. As a result, you receive various helpful indications which will help you to modify your service offers.
Contract management supplies you with data which can be used to answer a variety of questions: Which products and contractual constellations function? Which don't? Which customers prefer specific contractual constellations? How high is the potential turnover per quarter/ per year? How big is the difference between new and existing customers? How high is the rate of contract extensions? And last but not least, you receive important information on the profitability of your customer and his readiness to change provider.
The Customer Lifetime Value (CLV) is one of the most important indicators for energy providers. A modern data platform allows you to accurately determine a customer's value over his entire usage life cycle. In addition to past and current turnover, future forecasts can also be taken into account. For example, with a tailored prediction model, it is possible to reliably predict the profit margin of a new customer at the signing of the contract.
Intelligent electricity metres and networks provide comprehensive opportunities for the optimisation of business activities. So it is possible to filter out detailed usage patterns and peaks for every household and use this information to improve energy efficiency and develop individual tariffs and products. The inclusion of demographic and meteorological data can provide even more accurate calculations. In the same way, the energy consumption of neighbourhoods and whole cities can be analysed. This facilitates infrastructural capacity planning and forecasts in relation to service demand in supply areas.
Recording the number of revolutions and positions of wind-powered devices means a constant mass of data. Using this information means that operations and energy yield can be optimised in particular weather conditions and seasons. Likewise, typical defects and manifestations of wear can be identified which can be used for predictive maintenance and servicing measures. Plus, you can use your data to decide on locations for future wind parks.