How can you predict aftersales issues with analytics?

1 Define your aftersales goals

Before you can use analytics to predict aftersales issues, you need to define your aftersales goals and how to measure them. For example, you may want to reduce the number of complaints, increase the customer satisfaction score, or lower the cost of service. You also need to identify the key performance indicators (KPIs) that reflect your goals, such as the average resolution time, the net promoter score, or the service margin. These KPIs will help you track your progress and identify areas for improvement.

2 Collect and integrate your aftersales data

To use analytics to predict aftersales issues, you need to collect and integrate your aftersales data from various sources, such as your CRM, ERP, service management, and feedback systems. You also need to ensure that your data is accurate, consistent, and complete. You can use data cleansing, validation, and enrichment tools to improve the quality of your data. You also need to store your data in a centralized and secure location, such as a cloud-based data warehouse or a data lake, where you can access and analyze it easily.

3 Analyze your aftersales data

To use analytics to predict aftersales issues, you need to analyze your aftersales data using different methods and tools, such as descriptive, diagnostic, predictive, and prescriptive analytics. Descriptive analytics can help you summarize what has happened in your aftersales service, such as the number of service requests, the types of issues, and the customer satisfaction levels. Diagnostic analytics can help you understand why something has happened, such as the root causes of issues, the factors that influence customer behavior, and the correlations between variables. Predictive analytics can help you forecast what will happen in the future, such as the likelihood of issues, the demand for service, and the customer churn rate. Prescriptive analytics can help you recommend what actions to take to optimize your outcomes, such as the best service strategy, the most effective communication channel, and the optimal resource allocation.

4 Visualize and communicate your aftersales insights

To use analytics to predict aftersales issues, you need to visualize and communicate your aftersales insights to your stakeholders, such as your managers, technicians, and customers. You can use data visualization tools, such as dashboards, charts, and graphs, to present your data in a clear and engaging way. You can also use data storytelling techniques, such as narratives, context, and emotions, to convey your message and persuade your audience. You also need to tailor your communication to your audience's needs, preferences, and expectations, and use feedback loops to ensure that your insights are relevant, actionable, and valuable.

5 Implement and evaluate your aftersales actions

To use analytics to predict aftersales issues, you need to implement and evaluate your aftersales actions based on your insights. You can use project management tools, such as timelines, milestones, and tasks, to plan and execute your actions. You can also use change management tools, such as stakeholder analysis, communication plans, and training programs, to facilitate and support your actions. You also need to monitor and measure the results of your actions, such as the impact on your KPIs, the feedback from your customers, and the return on your investment. You also need to review and refine your actions based on your results, and use continuous improvement tools, such as the PDCA cycle, to ensure that your aftersales service is always evolving and adapting to your customers' needs and expectations.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?