While customers may think they want lots of options and choices, too much choice may be unhelpful.
Identifying what customers really need and focusing on that is the best approach; but how do we do that? Feedback fatigue is quick to set in. People are getting tired of messages asking them to rate their experience.
Consumers who do respond to requests for feedback tend to be those who are either very happy with their experience or very unhappy. We want to know more about the experience of the average customer.
As a result, reviewing feedback surveys can often provide limited information. Whereas looking at customer behaviour, as they consider a purchase for instance, can often provide more meaningful insight.
Insight from behaviour
Customer data used to simply be what was held in the customer relationship management system.
It ranged from name and address details to purchase history. Digital technology can now collect so much more data about consumer behaviour, identify the most useful bits of information and use it to help improve the customer experience.
On the web: Online, digital technology lets you track customer movements around your website – with cookie permission of course – helping you spot abandoned carts and find out where potential customers decided not to buy.
In store: In physical settings, the Internet of Things (IoT) can tell us a lot about the customer experience. Data from mobile phones, Bluetooth beacons, cameras or footfall sensors can tell us when customers are in store and when they’re stopping to look at products or advertising displays.
On the phone: Call centre solutions can do so much more than just log when and why customers call. If someone phones in, they’ll feel much more positively towards you if you recognise their name and can pull up information about their recent purchases. If agents can see and respond to things affecting the customer, such as a late delivery or a fault with a product batch, consumers will feel heard and looked after.
Automation and customer journey analytics have a role here to anticipate why a customer is calling. For example, if a customer has recently ordered an item, and delivery tracking data shows delivery is delayed, it’s easy to anticipate that may be the reason for the call.
Intelligent sentiment analysis can judge how the customer was feeling when they made the call – and how that changed as the agent handled the call. Sentiment prediction even tells us how the customer is likely to be feeling. This means customer service teams or account managers can get in touch and sort out an issue, before it becomes a major problem.
Beyond customer relationship management
Customer data starts with the information in your customer relationship management system. To turn that data into something great, add customer behaviour data from a wide range of new sources and stir in a healthy amount of data analytics and artificial intelligence.
Combine that with high quality targeted human interaction – putting customers through to the right customer service team first time, for example – and giving customers the best possible experience will be a piece of cake.
It’s easier to surprise and delight customers if you’re able to give them what they want - when they don’t even know they want it.