Look Beyond What You See with Surveys
| Published: September 18, 2013 | Comments
“Look beyond what you see.”
When I hear this phrase, I visualize Disney’s The Lion King 1½ when Timon receives sage advice from Rafiki about how he can find Hakuna Matata; the life with no worries. Timon was told to look beyond what he sees, so Timon decided that he needed to head to the pointy rock to get his answer.
Rafiki’s sage advice can also be applied to survey results. Looking beyond what you see is truly the difference between success and failure in any customer experience improvement activity. If you are frustrated by your survey program not being actionable, you lack in the ability to look beyond what you see.
Doing data analysis is something that can be taught to a large amount of the population. Go to class and learn the formulas and techniques and you can become a data analyst. Hooray? Wait a moment. Being a data analyst (even if you are a very good one) does not mean you can convert data into something that is operationally actionable. You must be a great data interpreter. For you to be able to convert data (make it actionable) into something more useful than a report, you must look beyond the numbers. You must look beyond what you see. You must interpret the data.
For the past decade I have heard contact center leaders (executives too) complain about their survey programs not being actionable. You may think this complaint comes from those that are solution and resource starved, but you would be wrong. This complaint comes from a wide variety of contact center folks, even those that are using some of the most sophisticated tools and teams of data analysts.
How is that possible?
While there are several reasons that generate complaints of data not being actionable, there is one common trait that is apparent in them all. They all lack an element of data interpretation in their survey programs.
Those that state their survey data is actionable have significantly greater data interpretation as part of their programs than those that are complaining. Those that are interpreting the data know how to look beyond the numbers; they know how to look beyond what they see. When their data interpreters are given clean data they can convert data into actionable insights that can be leveraged at the front line and in strategic decision making. They can generate conclusions that can be described as Hakuna Matata. People that are using the interpretations they generate do not worry about what it means or what they should do because the messages are very clear.
So how do you look beyond what you see?
If you were to ask those skilled at data interpretation how they do it, you are more than likely to get a wide array and diverse set of responses. You will also receive several. “I do not know, I just do it” answers as well. This makes it extremely difficult to identify and replicate their high performing (and desired) traits from one data interpreter to another.
Your journey does not have a map
Rafiki did not use his stick to draw Timon a map to get his answer and you do not get one either. However, you can start with some very simple techniques that may help your contact center leaders and analysts to improve their data interpretation skills.
To begin this process you must start with a full understanding of the data sources and confirmation that the survey data contains clean data sets. If this foundational element is not confirmed, then any data interpretation will be wrong. You must have somebody with the skills to certify the data as clean before you begin to interpret data.
Once you have clean data, begin the B.D.A. process to assist with improving your data interpretation skills. The B.D.A process is an inquisitive process that helps to put meat on the bones of the data you are reviewing.
To get started, the D.B.A process requires you to ask questions about the data in context of:
efore the data was collected: Ask questions about product, service, customer type, length of relationship, location, support method, etc.
uring the data collection: What were they doing, trying to do, what they wanted, method of data collection, service method, service location, etc.
fter the data was collected: Customer steps, company steps, defection risk, repeat contact rate, service recovery, repurchase, rant, rave, etc.
You can very easily go through this process in your own environment and construct your own D.B.A process elements for your surveys. This process can be replicated and repeated by lines of business, product types, and service methods. It is an easy way to set about for improving the data interpretation skills in everyone.
While this method is not a guarantee that people will in fact improve their data interpretation skills, it is a much better method that getting whacked on the head with Rafiki’s stick.
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