Optimization of customer support through clustering of target group data.

MIDDLE LEVEL

Initial situation of strategy consulting

A medium-sized company that offers Software-as-a-Service (SaaS) for e-commerce platforms noticed that, despite an increase in the number of customers, turnover was not growing as expected. There was also an increase in complaints about support.

Data basis and methodology

The company decided to carry out an in-depth data analysis with us. For this purpose, customer data from various sources (purchase history, support tickets, software usage data, feedback surveys) was processed and presented. Once the data basis was understood, an AI-based clustering method from the field of unsupervised learning was used.

Development process

Several interesting patterns were identified by clustering the data. People who needed intensive support within the first two weeks of purchase had a 3-fold higher churn rate in the first year. People from certain industries (e.g. fashion) had more technical problems than others. People who used the software intensively were less likely to give negative feedback, even if they opened support tickets.

Implementation and optimization of internal processes

Based on these findings, the following measures were taken:

➲ Proactive support: New customers received proactive support in the first two weeks to ensure that they could use the software without any problems.

➲ Industry-specific adaptations: The software was optimized for industries with frequent technical issues.

➲ Reward system: A reward system was introduced for person who actively use the software, rewarding them for using the software and giving feedback.

Success story

By optimizing support and making industry-specific adjustments, customer satisfaction has increased significantly. This led to:

➲ A 25% reduction in the churn rate.

➲ A 15% increase in referrals from new customers.

➲ A 10% increase in average contract value, as satisfied people were more willing to pay for premium features.

The above increases resulted in an additional 25% increase in revenue within one quarter. This means a projected total increase of 40% at the end of the first financial year compared to the estimate without the implementation of the optimizations.

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