28.09.2019 | Author: Ilse Melotte

At NrgFin we are introducing data science and machine learning in some specific areas. We are building the first predictive functionalities for energy suppliers ready to move to the next maturity level in terms of forecasting performance.

Working with our customers, we noticed that Marketing and Sales prioritize AI and machine learning higher than any other department. Finance on the other hand is relying on in-memory analytics when it comes to creating actionable insights. And indeed….

Dresner Advisory Services recently organized its 6^th annual Data Science and Machine Learning Market Study, which revealed some interesting insights. The study found that advanced initiatives related to data science, machine learning and predictive analytics are ranked the 8^th priority among the 37 technologies and initiatives surveyed in the study. Key insights from the study include the following:

Data mining, advanced algorithms, and predictive analytics are among the highest-priority projects for enterprises adopting AI and machine learning in 2019. Reporting, dashboards, data integration, and advanced visualization are the leading technologies and initiatives strategic to Business Intelligence (BI) today. The following graphic prioritizes the 27 technologies and initiatives strategic to business intelligence:

 

40% of Marketing and Sales teams say data science encompassing AI and machine learning is critical to their success as a department, where finance argues to be less depending from AI and machine learning, but yet highly focusing on Data Analytics.

Source: State Of AI And Machine Learning In 2019 – Louis Columbus

 

The EB Insights^TM Maturity Model considers Gross Margin Forecasting as follows:

 

At the defined stage, the energy stream forecasts are quantified for the contracted portfolio based on expected energy price evolution and contracted parameters. We call this calculation “1. Contracted”.

At the managed stage, the energy stream forecasts are quantified for the contracted portfolio based on expected energy price evolution and contracted parameters. Then, aggregated sales, churn and retention assumptions are allocated to the portfolio and valorized based on average parameters. We call this calculation “2. Straightforward Contracted – top down corrected”

At the optimized stage, the energy stream forecasts are quantified for the contracted portfolio based on expected energy price evolution and contracted parameters. Individual Customer Value is valorized and adjusted for predictive churn, predictive retention and Sales. We call this calculation “3. Straightforward Contracted – bottom up calculated”

Within the NrgFin Product Catalogue, all predictive functionalities are situated in the EB Insights^TM domain: Market and Customer Insights.