Customer Care

How After-Sales Data Analytics Build the Foundation for Customer Care Excellence

By Marek Olszowy - October 15, 2017

When it comes to the nuts and bolts of ensuring an excellent customer experience, your use of data must be of paramount importance. That which forms the basis of the business model for large online brands like Amazon or Netflix has always applied to our business: after-sales services. Customer care only works well if service processes are measured and optimized by using data.

Consumer electronics brands that have a global presence and outsourced a large part of their after-sales functions to repair partners, logistics providers and call centers, have never got by without a solid database – regardless of the fact that we have been recently dealing with this topic using buzzwords such as chief data officer or predictive analytics. Data has always been a fundamental basis for a perfect service experience. However, because top management have long paid little attention to after-sales, we find ourselves in a less than ideal situation.

After-Sales Services Are in the Spotlight When It Hurts

The growing importance of after-sales services for the overall success of a brand is an exciting development, but it must include a review of this question regarding data. In the mobile industry, classic brands such as Nokia or Siemens have placed great value on excellent customer service although this did not prevent their downfall. On the other hand, today’s market leaders have triumphed with innovative product features, elegant design and powerful brands. But now that the smartphone market is slowing down in the more developed markets, we are seeing a renaissance in after-sales services.

In our global study Smartphone and IoT Consumer Trends, it became clear that excellent customer service has substantial impact on overall customer satisfaction. Only those brands offering their customers a seamless customer experience will gain loyal customers. After-sales services are becoming a success factor for mobile and IoT brands, which means they can no longer see it as an optional extra. At the same time, the increasing cost pressure, forces device manufacturers to outsource, since hardly any provider can afford their own comprehensive service network. With outsourcing however, the need arises to ensure that service processes are 100% transparent. It is only then that it is possible to take corrective action – or even better, to detect emerging problems before they become visible to the end-customer.

Focus on Topics that Lead to Negative Word-of-Mouth

In practice, this means specifically focusing on issues that most upset customers and that lead to negative word-of-mouth. This is particularly the case when customers have to send in their device for repair twice because the fault was not corrected properly first time round or a new fault occurred during the service process – the relevant KPIs are known as bounce rate, re-repair rate or unsuccessful repair rate. In addition, long waiting times are extremely detrimental to levels of satisfaction. According to the results of our study, anything beyond the 24-hour limit does not meet the customer’s expectations nowadays.

Most of these negative cases can be avoided by intelligently selecting the KPIs in the first place, followed by a practical handling of the collected data as the second step. After-sales data analytics is not rocket science, and customer care organizations can also do without a Chief Data Officer. Let’s take the example of turn-around time: if you only measure the repair lead-time afterwards i.e. post the repair, the damage has already been done. It would be much more intelligent to choose a KPI such as ‘work in progress’ which provides us the information about the current workload of a repair partner (all open and pending repairs and their status). If we find that the incoming repair orders are stretching the capacity limit, then we still have the possibility of choosing an alternative procedure such as quickly exchanging a device or adding additional manpower on the repair center side to work this backlog out.

Thousands of Data Points Provide Real-time Information

To do this, we need accurate data at all times. Up until now, there has been the challenge of enormous volumes of data. It is not an unusual approach that even now, in some big OEMs, after-sales service managers download data from various database systems and copy it into Excel spreadsheets to conduct their own analysis – a waste of time and money, producing no good results. The good news is that in cooperation with customer care providers like us, most manufacturers have now established global customer care ecosystems that have thousands of data points providing real-time (or at least almost real-time) service information. This allows us to manage after-sales services in a professional and customer-oriented way.

At B2X, we have chosen to adopt an approach that follows the principle of less is more. With SMARTANALYTICS, our platform for after-sales data analytics, we conduct analyses live on the basis of real-time data – without intermediate steps, without a loss of quality. We have configured our applications to focus exclusively on the pain points. It is about reducing data noise – we want to concentrate on the essentials i.e. those processes and elements where the service experience seems likely to fail.

Green KPIs are automatically filtered out and attention is drawn to where it hurts. We are also investing significantly into predictive and prescriptive analysis capabilities, which will help us to anticipate where it might hurt in the near future – “This KPI is green today, but if you do not implement the following measures, it will turn red after three days.” Predictive patterns can tell us that something is going wrong beyond our view e.g. a repair partner will soon not have enough capacity or that the spare parts delivery is delayed. This may seem trivial for an individual case, but in a complex service network with hundreds of partners around the globe, the extent is much greater. As I said earlier, this is not rocket science. However, working with data is not about winning the competition for the most impressive buzzword, it’s about hands-on work which forms the basis for an excellent after-sales experience.


Customer Care

About the Author

Marek Olszowy

Marek Olszowy is Head of Global Service Operations at B2X.