Interview with Sara Burdenski about advanced analytics

Advanced Analytics with Sara Burdenksi from evm: “We get to know our customers better.”

In her role as team leader for analytical marketing and customer loyalty at Energieversorgung Mittelrhein AG, Sara Burdenski is advancing the topic of advanced analytics as a part of data science. In her opinion, improvements for customers can only be achieved by achieving a 360° customer view.

Ms. Burdenski, how would you currently describe working with your data?

We are currently evaluating historical data to answer common descriptive questions and developing static scores. We carry out numerous evaluations and reports and also build a data model that easily enables data analysis from the customer's perspective.

Do you know your customers?

Not as good as we would like.

What is the reason for that?

We actually have enough data, but the plethora of different source systems and the lack of customer insight in the data obscure our view. We often only have a limited perspective on the customer and can only answer questions that concern us via detours.

That is why you are now consistently building a data model from the customer's perspective. What exactly is this about for you?

In doing so, we are not only creating the basis for a truly data-based and automated customer approach, but also for the data analysis itself. From the customer's perspective, we also hope that the data model will simplify data access, reduce the complexity of data management as well as ensuring a so-called single point of data truth for data analysis, advanced analytics and all data-driven business decisions.

In this context, you are talking about the 360° customer view that you want to achieve.

That's right. We are taking a big step in the right direction by building a flexible data model that gives us a simple customer view for data analysis. In addition, we try to ensure a 360° customer view by using the right IT systems and by paying particular attention to customer data flow within our system architecture.

What are the benefits of this, both from a marketing strategy and from the customer side?

This approach enables us to address customers consistently and in line with their needs. We also identify valuable customers and their development potential. We use data as an economic decision-making tool. Last but not least, we are able to carry out forecasts and trend analyses.

To do this, you rely on advanced analytics as a part of data science. What fundamental improvements would you like to achieve in addressing your users as a result?

We promote the active development of valuable customers by offering everyone the right solution. At the same time, we are not only expanding emotional customer loyalty with advanced analytics, but also increasing the positive customer experience. And: we get to know our customers better.

“At the same time, we are not only expanding emotional customer loyalty with advanced analytics, but also increasing the positive customer experience.” Sara Burdenksi/Team Leader Analytical Marketing and Customer Loyalty/Energieversorgung Mittelrhein

Which methods are particularly interesting for you in this context?

These include predictive modeling, data mining, machine learning, cluster methods and decision trees. With these methods, we focus primarily on prescriptive and predictive data analysis, which enables us to predict customer behavior, make recommendations for action for the company and thus react proactively to customer and market behavior.

Can you give us an insight into the use cases that are relevant to you?

The calculation of customer value, the cancelation forecast or the canceler score, the persona calculation and, last but not least, the identification of potential for marketing new products or cross-selling products are particularly interesting for us.

What challenges do you see when implementing your use cases, both analytically and technologically?

From a technological point of view, building the customer view in the data model and ensuring the single point of data truth, i.e. the flow of data from relevant source systems to evm's modern data warehouse, is a bigger task for us. It must be ensured that the data is correct and clear and that transparent access to all relevant data is guaranteed. Our challenge from an analytical point of view lies in the lack of data understanding, in the importance of Data analysis in companies and associated with the scarcity of resources to carry out data analyses.

Ideally, experienced data experts are available for all phases of data analysis. Based on your experience, which competencies should a data team ideally combine?

In recent years, we have gained an idea of what an ideal data analysis team could look like and are pursuing this idea as a vision. In our opinion, the roles of Analytics Consultant, Data Scientist, Data Engineer and Analytics Developer should be filled. They work on the tasks involved in a data analysis team collaboratively and in accordance with their existing know-how. From an EVM perspective, we have already taken important steps in the right direction.

Let's stick to the topic of vision. How do you think EVM will be able to work with data in the near future?

We pursue real data-driven economies in the form of prescriptive data analysis. Data analysis is established at EVM from the customer's point of view and as a standard process based on inquiries. Data understanding has not only grown across the EVM Group, but across departments at the same level.

Thank you for the insights, Ms. Burdenski.

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