Bharathwaj V

Businesses have hopped onto the 'chat' bandwagon, drawn by the channel's potential to offer fast, easy, high quality customer service at lower costs, and consumer interest in online interactions. These are all the right reasons, but results have been mixed; the majority of companies and their customers aren't thrilled with chat, and, as a result, it's usually less than 2% and even large chat operations are typically less than 10% of a company's agent-led interactions. A few companies though, are transforming their interaction mix with Predictive Solutions that have increased chat to more than 20% of their agent interactions and reduced cost-per-contact. Goes a long way to show that chat is a great service channel on the web, on smart phones and tablets like the iPad, provided it is done right and done smart. We have talked about the haves and the have-nots for chat ... (more)

Experience - Using the :)

We talked about the significance of 'Performance' a while back when we started the three-part blog post titled "Getting chat right with Px ". Today, we revisit the subject to delve deeper into what makes chat work as we talk about 'Experience' and why it's so crucial to a successful chat program. ... (more)

CSAT measures - creating the right impact

Most of the analysis and outputs from CSAT surveys are focused on what needs to change at the contact center. Whether it is an improvement in agent performance or the type of training or “bringing up a center” to the network average, the call center agent directly come under the microscope. However a quick analysis [...] ... (more)

Conversations - A compelling source of customer intelligence

It seems like every subscription business (wireless carriers, cable/internet providers….) is worried about one thing in this down economy – customer retention. Analytics teams in these companies are focused on building “customer attrition” models. Typical attrition models take structured attributes and historical behavior of customers to segment them based on their “propensity” to attrite. The end result of these attrition models is simply a statistical prediction of a customers’ likelihood to remain loyal and/or leave. By applying the model to current customers, a prediction o... (more)

A Practitioner’s View Of N=1, R=G

While I was thinking about how we at 24/7 Customer are helping our customers to serve their end consumers using the N=1, R=G model, I started looking at how we are using it for our own business. Being a global company gives us a lot of opportunity to leverage both those formulae internally, and there [...] ... (more)