By Dan Somers, CEO of Warwick Analytics (www.warwickanalytics.com)
We all want to improve customer retention. If we keep customers happy they stay longer, take up more products, and tell more people about the experience. Here are 3 ways in which AI is helping banks improve their customer retention.
Dealing with incoming queries and questions more efficiently
AI Interaction analytics examines the specific language used by customers, in web chat or email for example, so you can manage the interaction appropriately and efficiently.
It removes the need for manual review of each incoming query and enables you to handle them effectively from the outset.
The analytics can facilitate a much smoother omni-channel experience for the customer by: identifying which channels your customers are best suited to – and which work best for specific types of interaction; understanding the causes of channel failure and what drives customers to switch; and reducing customer effort by delivering service in the customer’s preferred channel first-time.
As a recent example, at one bank we were able to reduce the maximum time to respond to a customer from 3 weeks to 5 days. The solution used AI and machine learning to automatically analyse and prioritise all customer emails in near real time and routed high-priority cases to a dedicated work queue for fast action.
In a utopia state, every customer knows what channel to use for what transaction, every interaction is handled correctly first time, there is no failure. With AI, companies can analyse customer frustrations and the failure within each channel e.g. containment and the root cause of switch. That’s going to give you the right insight to reduce friction and improve customer retention.
Automatically identify customer intent and emotion
AI analytics can identify terms that suggest dissatisfaction or potential complaints. Models can be automatically generated to auto-tag the customers’ intent, sentiment and also emotional intent e.g. if they’re considering switching to another bank or expressing some other actionable emotion.
When different people are voicing different issues, they will use different words and sentiments. Current analytics typically identifies just the keywords used, but this runs the danger of failing to miss the entire context behind the communication. Often a customer will merely imply a sentiment or intention instead of explicitly expressing it with specific keywords. For example a customer at a bank might say ‘by the time they called back, the bank was closed’. The keyword would be flagged as ‘closed’, when in fact the main issue was the call back. There are also other limitations with using just keywords such as sarcasm, context, comparatives and local dialect/slang. The overarching message can often be missed and so the alternative is to analyse text data using ‘concepts’ instead of ‘keywords’. This can be done effectively with AI.
AI analytics can automatically and accurately ‘tag’ feedback using actionable concepts along with the intent of the customer. The impact of this is huge.
A lot of the time the customer is clearly telling you how they are feeling or what their intent is – you just need the right analytics to pick these sentiments up and the need for any surveys is removed.
Being able to isolate those comments where your customers are really telling you things is really powerful. No one likes to hear bad news, but if you let it speak to you, you can follow these pathways, see which are the big issues and tackle things in the priority order to be able to drive the quickest improvements.
There’s always a lot of low hanging fruit when you can effectively and accurately identify multiple intents within the customer experience. Because when you know what you didn’t know before, then you can go after it quickly.
Fast tracking customer complaints and issues as they arrive
With AI interaction analytics, you can send complaints straight to the relevant team for a faster resolution. We’ve helped banks reduce resolution time by up to 3 days which really boosts customer retention.
Complaint handling is traditionally laborious, slow and inaccurate. Much research has identified direct correlations between the speed of handling a complaint and the emerging satisfaction and also the associated compensation costs.
The key aspects to improve customer retention with complaint handling are speed of problem resolution; taking a proactive approach; and the communication of next steps in the process.
When it comes to speed of response, dealing with specific complaints assigned with certain criteria can improve response rates dramatically. However, doing this manually simply involves using more and more case handlers. Routing complaints automatically and prioritising by issue and category is also difficult due to the nature of complaints i.e. unsolicited, long and sometimes multi-topical. As a result, manual classification is often impossible within an acceptable time frame for the unhappy customer.
By using the latest AI however, it is possible to automatically classify unstructured data such as text and provide an early warning for issues that need resolving fastest. This can lead to better and quicker outcomes at a much lower cost. Banks can handle complaints through a multitude of channels, whether they come through email, over the phone, logged in an online support portal, or through live chat. When you fully utilise AI analytics it can organise all your complaints into highly accurate clusters and categories making certain that they are dealt with expeditiously.