Working in a call center poses quite a few challenges. The rewards can be fantastic as well. After all, it's a thriving industry for good reason. But it might seem like there's no real way to maximize the benefits while neutralizing threats. But in fact there are some solid methods which have proven themselves time and time again. One simply needs to focus on a few aspects of the job in order to start increasing one's results.
Ask anyone working at a call center what the core of their business is and the answers will vary. Some people might bring up conversion rates with callers. Others might talk about products. These products might be physical or support oriented. But in the end they're referring to the exchange of money for goods and services. But there's something behind all of these ideas. And that's the concept of raw data. Ultimately data itself is the main focus of any call center. Of course it might not seem that way from the perspective of any one person within the system. This is somewhat like not being able to see the forest for the trees. But it's also why methods exist to structure data in a useful way.
When people think of data they tend to imagine it as a neat and tidy spreadsheet. But that's really just the end of one branch along a larger tree. In the metaphor of a tree, data as a whole is more like the dirt a tree grows in. Data is messy, unorganized and more potential than thing unto itself. However, when tended in the right way data can grow into something wonderful.
The methods one uses to turn raw data into something useful is called data wrangling. In the context of a call center one can imagine just how much information is passed around in an average month. This could provide a huge amount of predictive information. One could notice trends, compare strategies which work against those which fail, and it goes on and on. But one can't compare data of different types with each other. And unorganized data is just that. It's a giant mess of different and incompatible data types. By wrangling the data one essentially groups it together in a useful form.
Once data is in the proper format one can use it for a number of different applications related to a call center. One of the most important is to simply examine examples of success and failure. Of course every type of call center will have different metrics related to this issue. But consider an example of a call center which sells physical objects.
Properly cleaned data can be easily matched against several variables. One would first examine successful sales of the company inventory. The next step would involve looking for shared variables. What are some variables shared by the majority of successful calls? Likewise, what shared variables occur in failed calls? Now one can bring it together by considering which variables are absent in one and present in the other.
When taken together all of this information can form a valuable script to use in the call center. Basically, one simply needs to chart out the common variables within the now perfectly curated data. A solid database program is obviously useful in this process as well. But no matter how one handles it, a script using these variables will help every employee do their job better. The results should show up fairly quickly.
But what makes this such a powerful technique is that one can repeat the process indefinitely. Once the new scripts are in place one can take in new data and once again go through the variables. This can show even more room for improvement. Eventually this can be refined to a point where all employees have a script which will maximize overall success rate.
Publish Date: March 5, 2020 7:10 AM