In the last two posts, we discussed how analytics-driven complaint management can help 'put out fires' and satisfy the regulators (i.e., detect, manage, monitor, report and remedy customer complaints).
Now let's talk about what we can do to predict and even prevent complaints, because after all, as some wise man out there probably said, 'The best complaint is no complaint at all.'
The Complaint Journey
Most valued customers don't go straight to complaining. There is usually some sort of journey leading up to a complaint. It could begin with browsing a company's website, calling an automated system and navigating through the maze of press 1 for this and press 2 for that, followed by a phone call to a live service representative, and then repeat.
Here's an example of a common complaint journey:
No doubt, this customer thinks their problem is unique and they may be feeling they are getting the run-around by the time they reach the formal complaint stage. But the reality is that they are probably not the only one.
That's why analyzing customer journeys, tracing them across multiple interaction channels, provides powerful insight into common complaint paths. For example, identifying the service scenarios with the highest complaint risk lets us know where to look for trouble. This gives us a powerful tool to identify possible dissatisfaction at the earliest stages, even before it becomes a complaint.
It's simple, really. Early warning alerts for corrective action prevent customer complaints.
Real Time Guidance: What Are Friends For?
Here's how it works.
A customer calls up the contact center regarding a money transfer, for example. The customer journey solution instantly identifies that this customer made two prior inquires in the last month regarding a money transfer. It also notes that he sent an email with the subject "money transfer" earlier the same morning.
The customer service representative who takes the customer's call immediately receives a pop-up desktop alert noting a 70% chance this customer will complain. To preempt this possibility, the agent also receives real-time guidance on what can be offered to the customer to address his possible money transfer issue.
It might look something like this:
Well, OK. Maybe not exactly. But just think of Sheldon's algorithm as providing agent guidance and Howard as providing real-time responsiveness based on some urgent interaction analytics (i.e., monitoring the conversation, identifying a risk of imminent caller complaint, and providing an option for preemption).
And if the service rep is still not able to turn the customer's frown upside down? Then the rep is prompted to log the interaction as a complaint and the system automatically slaps on a risk score, a complaint ID, and adds it to the Customer Complaint team's queue.
The Secret Payoff
We find we've improved our business. Our customers have great experiences and no real reason to complain in the first place.
And isn't that really the best payoff of all?