Depending on the type of industry you are in, you may call it the service desk, the help desk, the call center, or even the support desk.
When it comes down to it, it is a consolidated (or virtually connected) team of employees working for one or multiple organizations to provide support services or general sales and services to the organizations’ customer base and potential customer base.
The call center, as many refer to it as, is a major data entry point for many organizations and large corporations. Fortune 500 companies may handle thousands of calls a day. Each call provides new data for the company. All that data will be used in some way by someone so that they (the company) can understand their customers’ needs and wants.
To perform good analytics on call center data you want and you need good quality data.
For a call center to function at it’s highest potential and to provide good solid customer service they want and must have good quality data at their disposal…
Depending on the type of industry you are in, you may call it the service desk, the help desk, the call center, or even the support desk.
When it comes down to it, it is a consolidated (or virtually connected) team of employees working for one or multiple organizations to provide support services or general sales and services to the organizations’ customer base and potential customer base.
The call center, as many refer to it as, is a major data entry point for many organizations and large corporations. Fortune 500 companies may handle thousands of calls a day. Each call provides new data for the company. All that data will be used in some way by someone so that they (the company) can understand their customers’ needs and wants.
To perform good analytics on call center data you want and you need good quality data.
For a call center to function at it’s highest potential and to provide good solid customer service they want and must have good quality data at their disposal.
“Personal Experience:
Loading...After calling our television provider and explaining to them that the PVR is not functioning normally and spending an hour on phone until they finally agreed upon handing the headset over to a manager and then sending a new PVR to our home.
After receiving it, we had to call back initialize the PVR code. Of course it’s easier then it sounds, because they had no record of us receiving the PVR? What’s up with that! So we went through the explanation about getting the new PVR and had it initialized, they stated it would take 2 hours.
2 hours pass and nothing.
Call back… wait until the morning, sir.
Loading...Morning… we have 1 viewing package from the 4 we pay for. Apparently somewhere in their systems a call center operative indicated we canceled our subscriptions. I need my TSN, so no, I was not happy.
And they didn’t fix this when we tried to initialize it the first time?”
I once read years ago that the number one cause of data quality issues are caused by employees. Case in point. The above personal experience is just an example of how untrained employees in a call centre environment can affect your data quality, your reporting and your decision making.
Looking at the above, some reporting systems will show the knowledge worker: 1 lost customer, one gained customers, or even a winback situation, it may not even show the cause of the change, faulty equipment? All because someone didn’t record the information properly.
It’s not good enough to train call center staff to enter data and read from scripts they must be intuitive and be able to make decisions faster and better, they must know the business. They must understand what it means to enter quality data into their front-line applications and how it will impact the company. They must understand that their data entry does affect your data, your data warehouse, your customer service and ultimately your bottom line.
Entering the term ‘Death Star’ for the sales channel, might be funny for a young CSR, but let’s face it… it could blow up in the company’s face!
You talk to anyone who has dealt with call centers and you will know that the majority of people end up frustrated to no end. The majority of call centers are stepping stones for many employees who want to move on. Therefore, keeping a motivated staff, who cares about their work is fundamentally a difficult task for any call center manager.
For many data quality analysts, I would imagine looking at the data from a call centre is like being sentenced to the 9th layer of Hell, it’s just not a fun place to be. Why? Because lets face it, trying to correct bad data from the front-line can be a cumbersome task, you have multiple systems to work through, lineage to deal with, and when you want data corrected or to set up preventative safeguards, there’s no one to call.
Do you have a ‘Death Star’ situation in your company? Think of the following to help you, the data quality analyst, out.
1) Contact: Move up the lineage line, at every upstream system, make contact with someone. Tell them your story and your issues. When they have problems they’ll let you know. And better yet, they may let you in the sandbox, and introduce you to their sources, or call-center managers.
2) Prevention is the Key: One of those contacts will be useful and they may be able to correct the data or put in place the corrective measures needed to stop further death star situations.
3) Squeal Like a Stuck Pig: Pass the cost of making data corrections up the line, straight to the VP if you are able to. The eye-opening costs will force the VP to take action and talk to the VP of the offending data sources.
4) Educate the Young: Start a data quality education initiative targeting call centre employees and new call center employees. Ensure that new employees understand what it means to provide good data. Talk to the call center managers and have them incorporate data quality into their training packages. They may not be young age-wise, but they are young in terms of being employed with your company.
5) Mind Meld: Find someone with like minds in the offending department to become your champion of data quality. Use them to help preach the benefits of data quality, and consistent quality data entry. Remember, two minds are better than one.
6) Recognize the Best: As mentioned in a previous blog, implement a data quality recognition program.
