Predica - Churn analysis - Don’t lose your most valuable customers
One of the most important challenges facing modern enterprises is dealing with customer migration (churn). The importance of preventing customers' departures is increasing due to the need to meet growing competition on one hand and more and more demanding customers wanting higher quality at a lower price on the other. So, how we can face this challenge? The answer is "churn analysis" with Machine Learning modelling together with customer scoring and segmentation methodologies.Kapcsolatfelvétel a partnerrel Videó megtekintése
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Churn analysis and score - what is it?
Customer migration (churn) affects an increasingly large group of industries. Preventing customer departures becomes crucial, especially when we have a relatively mature market and few new customers for a product or service, and it is easy for the customer to change supplier.
Countering customer migration is beneficial because the cost of acquiring new customers is usually higher than keeping existing customers. It is also worth noting that long-term cooperation with the client gives benefits in the aspect of increasing revenues and promoting the company.
In order to prevent departures, we use data analysis to acquire general knowledge about clients (regarding the entire population) and detailed analysis in which we anticipate the departure of a specific customer.
There are many methods that use data to analyze customer behaviour. From a business point of view, a very important question that needs answering is how much is the client worth to the company. This is known as "customer scoring." Based on such information, we are able to determine the efficiency of marketing campaigns and decide whether a customer is important enough for us to maintain advertising spending. The correct assessment of customer value is of the utmost importance for the business. When it comes to communication, building loyalty, preventing churn and retaining clients, it translates into a savings of time and money.
Thanks to Power BI, the client receives a visualization of churn analysis, which facilitates searching for customers important from the viewpoint of the revenue potential for the company, and tracking those on whom we should be more focused.
What are the key stages in churn analysis?
The essential element of churn analysis is the selection of relevant data. Therefore, the first phase involves the identification of data sources that will help us analyze the behaviour of our clients or allow us to broaden our knowledge about them. It's worth taking into account the transaction history of the client, verifying data, and carrying out a preliminary statistical analysis to determine potential variables for modelling. During this phase, you must define the business conditions that define the status of the churn. For example, a variable can be described using the following question: “Has the customer returned to us (used the service again) in the last 12 months? “.
Depending on the number of data sources, this phase may last from three weeks up to three months. This is also when Predica specialists meet with the customer. Talking to people from the business units allows us to gain domain knowledge and factor it into the solution. This is a very important stage because well-defined business conditions will later translate into accuracy in metrics.
Finally, it is worthwhile to verify the quality of the simplest predictive analysis model, logistic regression. These steps create a benchmark for the modelling stage. In relation to the quality, we will be referring to the tuning and parameterization of more advanced modelling methods.
After the data analysis phase, the team proceeds to pick five models that will be compared to determine the best fit for the business. The range of available possibilities that can be analyzed here will be narrowed down to models whose explained variables are 0.1 because of our resulting value will be the probability of churn.
The last stage is the construction of the data visualization layer. The data that has been enriched in the previous phase with the churn probability value should be visualized in a way that will improve the work of business units such as marketing or sales. Reports like this can be used to build retention-based marketing campaigns. A common practice is to show the effectiveness of marketing on churn.
The duration of the data visualization phase depends on the number of required reports: 1 month for every 10 pages of reporting.
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