If Jeff Weiner, the CEO of LinkedIn, believes that “Data really powers everything that we do,” who are we to argue? However, as an experienced contact center professional I am going to put forward the idea that the use and relevance of big data in a contact center really is very specific and only has a few relevant applications.
In fact, the relevance of Big Data in any contact center boils down to the following two points:
- The quality of the data
- How the data is used or processed
Given these points let’s take a look at how exactly Big Data can be relevant to a contact center manager of a medium to large international operation:
The majority of contact centers are suffering from an overload of data coming in from many disparate sources. A survey from the International Customer Management Institute (ICMI) appears to prove this is a sad reality through its questioning of 542 contact center professionals. The fist conclusion we can reach is that yes Big Data does have a place but in order to get good results it is essential to choose good quality data.
It is also important to select the right amount and not over extend systems or inputs and the data needs to be analyzed in a logical way. Taking this into account – some of the areas that can be looked at would be as follows:
The right data at the right time from customer satisfaction surveys, call logs and voice recordings can all be processed to improve contact center operations. The data can be fed into outgoing calls to make sure the customers that have given poor feedback can be contacted as a priority. Conversely, skills based routing can be applied to make sure agents with higher skill sets deal with calls from unhappy customers. As always the right systems are needed to filter the date and route the calls appropriately. A multichannel system is also appropriate in this situation so customers can be rated based on many different sources. Social media and email can often be overlooked.
Customer Feedback Data
Of course it’s not just the client that can be analyzed through the use of Big Data. Feedback can be collected on agent performance and can then be used in feedback sessions and performance reviews. This can result in a more equitable rating of staff performance and an easier way to justify pay raises or freezes. This type of agent analysis allows a large step past the traditional ‘volume of work/calls handled’ model. Lots of new factors from different sources can be combined to give a more accurate picture of the employees’ performance.
Outbound Calls – Preventing Customer Churn
But are there real life examples? Yes there are. I was involved in a project encompassing the use of Big Data in a contact center which utilized some of the points discussed above. In this case the project was an analysis by a bank that determined which credit card clients were most at risk of leaving, and therefore which customers should be called as a priority. After the analysis was completed, the data was fed into the contact center from other parts of the business so that the contact center could do their part and reach-out to the at risk customers. An operation such as this this requires a great deal of communication and coherence between different parts of the business.
An algorithm developed by experts was applied to the data using following variables:
- Credit Card Balance
- Number of other products with the bank
- Time as a customer
- Average monthly payments
The data was assessed against past patterns of customer churn and a profile of a ‘customer most likely to churn’ was developed. The contact center was then instructed to call these particular clients as a priority. These clients were given tailored offers and the level of customer churn was reduced.
Big Data does have a place in the contact center but the results will only be as good as the data you choose and the way it is analysed. And sometimes this data may come from outside the contact center.
This article was originally published by NSAM sister publication Customer Experience Report.