You're struggling with customer satisfaction. Can data analytics help you turn things around?

Daten lügen nicht – sie zeigen uns, wo wir wirklich stehen und wie wir weiterkommen. Die Datenanalyse zeigt uns den Weg zu einem besseren Kundenservice, denn wenn wir bestimmte Muster z.B. in unseren Kundenterminen oder Kundenanrufdaten erkennen und gezielt darauf reagieren, verwandeln wir Herausforderungen in Chancen und sorgen so für effizientere Abl?ufe und zufriedenere Kunden. Wer also Daten lesen kann, bleibt auf Erfolgskurs ??

Data insights are great; the question is what data and what insights. Asking the right questions will help you get to the root cause of the interactions. Once there, you can take measures to eliminate the peaks, bottlenecks, fluctuations, or whatever is causing your most pressing problems. Rather than analysing volumes, you can use generative AI tools to identify patterns in the interaction content and even ask for the root cause and solutions. You might be surprised how insightful the answers can be.

I've spent an equal amount of time managing in the service sector as I have building accountability systems that help others serve. If you're customer satisfaction is at issue, its like installing a smoke detector after your house has burned down. Its not going to do anything as you're going to be out of business. Customer service has and will be around for centuries. Often time the board will see themselves as the customer instead of the organization they've been selected to serve. Lamentable to suggest that if you do not have a grip on your key data and serving your customers, you're burning money.

Para el caso de los análisis de datos se debe ampliar el prisma de revisión. No es aconsejable tener solo una línea de revisión; se debe combinar diferentes variables. Aconsejo las siguientes: tiempo de Gestión, complejidad de solución, impacto al cliente, impacto en la satisfacción del cliente, recurrencias. La combinación de 2 o más variables de estas te entregarán resultados más acertados y con mayor claridad de que actividades ejecutar

If you are struggling with customer satisfaction,Data analytics can be a good starting point 1. Start focussing on low hanging fruits a. Look for the strengths, cases that are closed early, support engineers that are good in getting to the bottom of customer issue and able to resolve the same you should be getting good feedback on these b. Isolate the problem (People, Process, Technology). Look for areas to focus based on the the analysing customer feedback and going deeper on the case, engineer, workflow that is causing it c. Focus your energies on one thing that you want to turn around (faster response, reduced interactions, faster closure, empathy?), as all you need is a spark to ignite the can do attitude in the team and gain momentum

Data analytics is quite effective for streamlining call center operations. Tracking a call's route from inception to resolution allows us to discover bottlenecks or delays that impact efficiency. For example, if the routing system isn't working properly and isn't routing calls to the relevant agents, we can rapidly fix it. Furthermore, data can show reoccurring issues that could be remedied more efficiently, such as updating FAQs or giving further agent training. Optimizing these processes not only results in faster resolutions, but also in happy clients. Overall, analytics plays an important role in directing the call center to greater efficiency, which improves the overall customer experience.

As agents are humans too, on top of customer ratings (pressing a number to rate the call agent quality), with the advent of AI, it would be great if the customer could leave a voice message (one-liner) to feedback on what went well with the call and from there use AI to track the tone and vocab use over several calls. Having to hear one or two pieces of caring feedback from a customer via voice goes a long way in reinforcing an agent's good performance.

You would want to ensure that agents get lesser of mundane repetitive queries and are spending more time tackling advanced queries that challenge the human intelligence. Data helps us to determine what portion of incoming tickets need to move to deflection mechanisms like bots and forums.

Statt Fehler zu suchen, sollten wir den Fokus darauf legen, unser Team zu st?rken ??. Datenanalyse ist ein gutes Werkzeug dafür. Wenn wir erkennen, wie schnell und auf welcher Weise Reklamationen gel?st werden und wie zufrieden die Kunden mit der L?sung sind, k?nnen wir gezielt Unterstützung bieten. Aber es geht nicht nur um Zahlen – es geht darum, die Menschen hinter diesen Zahlen zu sehen ??. Ein einfaches ?Gut gemacht“ nach einem schwierigen Kundengespr?ch oder das Teilen einer positiven Rückmeldung kann wahre Wunder bewirken. Ein motiviertes Team sorgt für zufriedene Kunden – und das ist am Ende das, was wirklich z?hlt.

Predictive Analytics zeigt seine St?rke besonders bei der Analyse von Besuchsberichten unserer Vertriebsteams. Durch die systematische Auswertung dieser Daten lassen sich Muster in den Kundenbedürfnissen erkennen, die regional oder branchenspezifisch unterschiedlich sein k?nnen. So k?nnen wir zum Beispiel frühzeitig feststellen, wenn Kunden in einer bestimmten Region verst?rkt nach speziellen L?sungen suchen. Mit diesen Erkenntnissen passen wir unser Angebot gezielt an und statten unser Vertriebsteam mit den richtigen Tools aus, um optimal auf die Bedürfnisse der Kunden einzugehen und die Zufriedenheit sowie die Bindung nachhaltig zu st?rken.

Data analytics plays a crucial role in ensuring that performance and customer sentiment are consistently monitored. By regularly reviewing the data, we can quickly detect any performance drops or shifts in customer expectations. This is essential because continuous iteration allows us not only to make immediate adjustments but also to establish a stronger long-term strategy. Additionally, data provides insights that help optimize processes in the call center, such as improving scripts, adjusting shift schedules, or even refining the way follow-ups are conducted with customers. And given that customer expectations are constantly evolving, having a data-driven approach is key to staying relevant and competitive in the market.