Using real-time analytics, your conversations with customers can be more relevant and have a more personal touch. When you’re engaged in conversation, you may not be aware of it, but your brain is taking in and using massive amounts of information.

First, you recall previous conversations and everything you already know about the other person. You are also analyzing and building on what they are saying. Finally, depending on the nature and intensity of the relationship, your responses will be casual or formal in tone (or somewhere in between).

How much better would your conversations be if you could be more aware of the communication nuances as the conversation is occurring? And perhaps more importantly, what if you could do the same with your conversations with customers?

The answer for customer interactions is integrating real-time analytics into your business. By helping you to develop a comprehensive understanding of your customer, analytics will help you determine the next best offer, or at least the right message to send.

Using real-time analytics, your decision will be based on the context and history of interactions between the consumer and your organization – a complaint a few seconds earlier, a product ordered online or a basket abandoned. This dimension adds the immediacy necessary to any meaningful dialogue. SAS^® Real-Time Decision Manager provides the technology and software to enable this. My colleague Andrea Sangalli has discussed this in his post dealing with the power of contextual marketing in real time.

When ‘Right Time’ is ‘Real Time’

The instantaneous nature of this information gathering and analysis is especially important in the connected world in which we operate. The marketer does not have the luxury of being able to take their time in responding to a prospect or customer. Incoming interactions, initiated by the consumer, override outgoing actions and are also much more precise and promising in terms of conversion. Now, it is the consumer who decides what he or she wants, when and by what channel.

For example, a customer logs into his bank account online to look at the interest rate on his saving account, then an associated FAQ page followed by the page that shows him the steps needed to move an account to another bank. This is a strong signal to the bank that it needs to take action. Additionally, he may call the bank’s call center to explore other options.

Predictive analytics will allow call center personnel to offer the bank’s best response to that customer based on his recent queries and previous history. It can also make sure that the incentive is appropriate for the size and importance of the account.

If analytics had been used without this real-time information, a next best offer would have been sent, but perhaps too late because the client would already have contacted the call center and the call handler, unaware of the online search for information, might have missed the opportunity to make the right offer. One customer lost – and how many more in future?

For more on this topic, download our white paper Customer Intelligence in an Era of Data-Driven Marketing. Take a look at chapter 3, which is about campaigns in real time. Happy reading!