For a Customer Service Mind Set, Are You Asking the Right Questions?

Raw data is often scattered across databases. Collective analysis from different teams comes in as skewed reports. Management wants clear direction and strategy based on a detailed analysis of the data available. An analytic approach is the call of today. Is this a reality zone for you? Are you looking for the best way to begin? Does this seem like a daunting task?

If so, here are a few industry best practices that work and can be easily implemented. Questions define the very outcomes and directions of the research and analysis.

What are you trying to achieve?

Is the result a conclusion or a predictive analysis? Are you finding a new trend or a pattern among patterns? Get clarity in terms of the goal or goals you want to achieve. The best principle is to list the outcomes. Each outcome will have a different story and procedure, and that is fine. Not all outcomes will have the same strategy. As long as the data is there and the questions are correct, the answers will be created.

Data format is not an issue.

We are living in 2018. Stop worrying about the formats. Simply outline where the customer data can come from. Let the IT experts worry about the transportation of the data into searchable formats. Think clearly. The objective is to set the stage for data analysis.

Ask questions beyond the data.

Questions cannot be based on the data alone. This is one of the main disturbances we see in projects. Let me give you an example of a project we carried out recently in Asia. The client wanted to streamline customer service engagements. There were two sides of the equation: the customer service representatives and the account teams they were handling. A mapping needed to take place to adjust and calibrate each customer service agent with quality accounts. Basically, each customer service officer had five to seven corporate-level accounts to handle. Communication was via email, webchat, and phone. All transactions were recorded. Typically, after each engagement, the customer ranked the experience of the communication irrespective of the medium. So we had the customer service ratings of the accounts that the company was handling: 53 accounts and some 566 random server results. We could have kept the questions based upon the data. We were clear about our requirements. Our objective was not to improve the customer experience only. It was too see a deeper pattern that even management was not asking.Instead of simply managing the data and seeing where the gaps were, we went deeper.

Customer service is a perception issue. It is not about data only. Funneling the data is the key. Knowing the perception is a must. Calculating predictive results is essential. So correlate experience with experience. That is not shown in results.

Categorize customer perceptions.

With the customer data, we had their email addresses, phone numbers, and also social media accounts. From there we scraped the data and identified a number of customer likes and dislikes. It included things like sports, food, activities, and professional aspects. The best place to get this information is their LinkedIn profiles.

Again we scraped the data and were able to categorize the various fields and the percentages.

From there we created two surveys and sent them out to customers. This was also based upon their input and ratings given after each interaction. If, for example, the scraped data showed that the customer likes clear communication and he gave the customer service representative a poor grade, we needed to identify whether the rating was because of a communication gap.

The survey was a great success. It was able to substantiate the variance and tell us exactly where the gaps were. We were able to see how to fill the gaps with the right talent.

Instead of handing back the same teams to the customer, we did another round of testing based on a nother survey that evaluated the representatives on clarity, knowledge, focus, troubleshooting, follow-ups, and politeness. If a customer ranked follow-up as highest, we categorized that particular agent as an expert in the follow up skill set.

Asking the right questions defines how to use the data. The data is only the starting point. Learn how to ask the right questions and then use the data to get the patterns and define the solutions.

Emily Cashione is an experienced CRM/HR executive with more than 12 years experience developing consistent growth in training and development and customer life cycle management. She can be reached at