In my last blog post (BIG Data in the Contact Center – HUGE Potential to Increase Customer Loyalty, Part 1) I started a discussion about the huge potential Big Data offers for contact centers. I explained how contact centers are constantly gathering data from customer contacts. However, analyzing and utilizing all this data has been very difficult in the past due to technological constraints. Now we have Big Data which gives us the possibility to utilize all the data to gain deeper understanding of our customers. In the first part of this blog post I promised to give you an example of the huge potential Big Data offers to contact centers.

Big Data for deeper customer understanding  

Big data on customer insights can help businesses to know their customers much better and to understand their needs in more detail. Big data in contact centers also enables service agents to anticipate the customer behaviors and predict the best next actions to improve customer experience.

Here is a simple example for contact centers. Firstly, we already know from where the data can be aggregated: from private channel communications, public channel data in Social and transactional data sources in systems like CRM and ERP.

Secondly, we need to know how to make the data available. Most of the data is already in a text and number format. Therefore, it is relatively easy to aggregate the relevant data sets from their different sources for making the analysis. However, the biggest piece of the private channel Big Data – the phone conversations – are typically stored in a contact center as audio files. This is a fine format for coaching agents and monitoring service quality. To make the most out of this treasure, one needs to use call transcription technologies to do the Speech-to-Text conversion. Done – there are several commercial products available both on premise and from the cloud.

Thirdly, the massive amount of data needs to be made usable – preferably in real time. This means that it needs to be stored in Big Data storage with required capacity and performance. Done – in-memory database technologies (such as SAP HANA) are available both on premise and from the cloud.

Analyze and act on customer insights

The final step is to analyze and bring the data to the users to act on it. There are several aspects from which a contact center manager, supervisor and the service rep wants to look at the data. Therefore, it is critical that the analysis and visualization on the data can be done in real-time. For example, in addition to the service level metrics, a contact center manager wants to see the sentiment of today’s private channel interactions, the key topics discussed, the reasons why these topics where discussed and how are they connected or disconnected with the Social channel conversations. Drill down from overall to individual customer satisfaction gives the manager concrete evidence on best and not so best practices. The manager also wants to see the trends, identify patterns and make predictions on what the near future will look like if certain or no changes are made. On the other hand, the service rep wants to get all the relevant data about the customer before, during and after the interaction that helps him do his job – deliver an engaging customer experience. Done – all the analysis and visualization tools are commercially available, for example, real-time sentiment analysis on unstructured text and CTI connectors for screen pop-ups.

In contact centers the use of Big Data comes with a huge potential for improved efficiency in customer service which in turn provides an opportunity to achieve superior customer satisfaction. As I mentioned in my last blog post, contact centers generate a lot of valuable data which is possible to utilize effective with the help of Big Data technology. Think about the possibility of knowing how many of the calls and emails received in your contact center today had a positive versus negative sentiment. Big Data offers possibilities to discover new insights.