The importance of metrics in after-sales service: how to exploit your data?

Data collection in the after-sales service is an essential lever to maintain competitiveness and increase performance.

Mohamed BEN AYED
December 19, 2022

According to McKinsey*, out of 1000 companies with a turnover of more than one billion dollars, only 8% manage to exploit their data. Knowing how to make the most of the knowledge extracted from data is essential for the development of a company... Especially for the good management of after-sales service. An efficient and autonomous after-sales service is a pillar of the purchasing process, thanks to its metrics... As long as you focus this data in an analysis strategy. Indeed, the right use of your data can multiply your performance in terms of customer acquisition and competitiveness. Without further ado, discover how to use your metrics in after-sales service. 

Data collection: a widespread practice 

Develop knowledge about your business

Data is essential to the functioning of companies. For a long time, this information has been used to reason and structure adequate solutions to a problem. All companies use data to automate and optimize their activity. Data is collected through the implementation of various automation tools: 

  • CRM to process customer data 
  • E-commerce analytics: the interfaces of online stores record data related to acquisition channels, traffic and sales. 
  • ERP: Enterprise Resource Planning is a central tool in a company's information system that lists data related to the management of expenses, accounting, orders, etc.

The objectives of IT solutions 

If these computer tools facilitate the digitization of data, only their fine analysis allows to : 

  • Make the right decisions: information from data collection allows you to react in an informed manner and in real time. 
  • Develop the business with a better understanding of customer and product needs. In the long term, the analysis of all sectorial, competitive or web-based user and stakeholder information is used for strategic purposes. 
Data collection and analysis in after-sales service

Why use analytics in after-sales service?

Among the main drivers of a brand, the management of the after-sales journey benefits from the use of data analysis.

Product knowledge accessible to all 

Dealing with a product after-sales service requires the involvement of several players. To be efficient, the retailer, the supplier and the repairer must have access to information related to the complaint. That is to say, information about the customer, the manufacturer and especially the product: 

  • its date of purchase, 
  • the possible guarantees on which it depends, 
  • the SAV problem concerned, 
  • the after-sales history of similar products. 

Shared with the entire chain of after-sales service actors, all this data facilitates the resolution of the problem.  

Monitoring for better intervention

The company keeps an eye on the quality of its products through the recorded requests. After collection, the data is archived. This history generates inputs that contribute to the improvement of the product quality. Thus, according to the recorded after-sales complaint, the manufacturer will be able to : 

  • Improve a product that repeats the same after-sales problem on several customers. 
  • Remove the product from a catalog.
  • Have the product recalled.

Data collection also helps identify recurring problems with a product. Indeed, a major alert can lead the company to send a recall campaign for maintenance or product use advice... or even to stop production.

Quickly spotted, these situations limit possible consequences such as : 

- financial losses, 

- the impact on the brand image, 

- the confidence of the partners

- customer satisfaction: with 39% of customers expecting a response from the first solicitation (according to the 2021 study on customer relations by Qualimétrie), efficiency is a very important satisfaction criterion for the end customer!

Making the right decisions 

A manufacturer is constantly looking for ways to improve his product. 

Data from users and the product can reveal the potential or an area for development. For example: 

  • Automate decisions based on product and supplier.
  • Set up a process and optimize it.
  • Adjusting processing times...

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How to use data in after-sales service?

You will have understood: analytics is essential to the after-sales service to gain in efficiency and competitiveness. However, optimizing the use of its metrics must be done with rigor and precision.

Collect the right DATA

As far as after-sales service is concerned, the information collected to solve product problems is :

  • the origin of the article (type, supplier, date of purchase...), 
  • the type of failure, 
  • supplier contracts, 
  • the outcome of the proposed solutions, 
  • information on after-sales service files (out of warranty / under warranty / solution...)

Centralize information 

The data you get comes from your different touch points. Between in-store complaints, chat bots, the call center and other messaging channels... data gets isolated and can get lost. To process it efficiently, it's essential to use an omnichannel customer care system that brings all your information together in one place.

Data analysis in after-sales service
Analysis of service data

Analyze the DATA

To analyze the data collected, you must first know your objectives. Because, before optimizing the metrics, you need to know which ones to monitor in order to draw the right lessons.

And it's not that simple...

The real challenge in identifying the right data is not technical but business knowledge. Indeed, determining a clear and defined trajectory requires knowing the risks, opportunities, strengths and weaknesses.

The analysis of your metrics will provide you with the lessons to capitalize on to refine your trajectory.

The choice of tools

Now that you have identified the data you need to collect, all that's left to do is put your operating plan in place!

When you think of metrics, you think of automation tools. Among the solutions at your disposal: 

Analytical tools on the market. As these solutions are autonomous, the first challenge is to connect them with your other tools to obtain an optimal collection strategy between your different channels. These analytics tools can then centralize the information and communicate it to you. 

Collected in this way, these data are sometimes difficult to analyze.... The second challenge? Mastering the use of the software in order to correctly interpret the resulting metrics.

Without this skill, the analysis will have to go through a department or a dedicated data center in the company. The disadvantages of this alternative? The company loses agility, data sharing and rapid updating of VAS analyses. 

An embedded module in your after-sales service
The second option is to create an embedded module directly in your all-in-one after-sales solution. 

Designed by experts in after-sales needs, this option facilitates the structuring and aggregation of data. How does it work? Thanks to the grouping, cross-referencing and relationships between all the data from the complaints. Indeed, the digital and omnichannel solution accumulates and analyzes a large amount of data before extracting the necessary information. 

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Knowledge results for ROI

Micro use in favor of the product

Following the collection and analysis of data, the perfect knowledge of the products in the after-sales sector allows the company : 

- Anticipate programmed degeneration: by collecting a good amount of data on the life of a product, a company will be able to have a vision on the programmed degeneration of a product and thus anticipate possible after-sales requests.

- Detect quality problems quickly: the analysis of the number of complaints on a product makes it possible to warn the quality teams of the manufacturers to anticipate the campaigns of product recalls or the waves of returns. 

- Reduce repair time: Knowing the metrics across the entire after-sales service chain can highlight potential bottlenecks and slowdowns that impact service fluidity. Once identified, these dysfunctions could be resolved in order to design an optimal after-sales experience and reduce repair time.
The actual repair time of an after-sales claim is also a metric that, given to the agent in store, allows to improve the handling and to inform the customer accurately. 

- Guarantee a good customer satisfaction rate: all these anticipations and knowledge together ensure a fluid, clear and efficient communication between the company and the customer. Knowing that the criteria of customer satisfaction in the after-sales service include : 

  • the speed of care, 
  • the seamless customer experience, 
  • effective repair.

Metrics play a crucial role in satisfying customers. 

The ROI in after-sales service

Macro use to improve ROI

The use of such a strategy is beneficial for the brand, which benefits from a double return on investment: 

  • The influence of data on business decisions: With a common point of contact between all stakeholders, the company benefits from a global vision of the entire after-sales service chain. Data is an indispensable decision-making tool for making informed choices to improve a company's business.
  • Long-term investment: If data collection allows you to obtain immediate indications on the situation of a product... In the long term, this collection constitutes an ultra qualitative database and brings a real competitive advantage. This type of solution can help control management costs and reduce administrative expenses. 

Data has become a tremendous source of information and knowledge to guide companies in both acquisition and customer retention. Whether it's presenting valuable information or providing solutions to problems, companies need to consider a more strategic approach to their data. 

Thanks to its all-in-one after-sales service platform and in particular to its embedded data collection and analysis module, Platana offers a global and precise vision of your after-sales service. A major innovation to help companies develop their competitiveness in the long term. 

Source of the study https://www.mckinsey.com/capabilities/quantumblack/our-insights/breaking-away-the-secrets-to-scaling-analytics

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