Customer service is often treated as a numbers game. Organisations want to increase the number of customers they serve and the number of cases resolved while reducing costs and increasing efficiency. So they introduce new digital self-service channels for customers.
There is, of course, nothing inherently wrong with this approach – and self-service is key to a successful customer experience strategy. Businesses need to be profitable, and customers expect brands to offer convenient ways of resolving issues. But when you focus entirely on the number of customers you serve, channels you offer and calls you deflect, you can lose sight of the bigger picture. Yes, you’ve ‘served’ more customers – but have you delivered meaningful outcomes for them?
Quality vs quantity
Take Chatbots, for example. They can be a great self-service tool, and 69% of customers now say they’re willing to interact with them. Great, right? Well, sure – until you consider the fact that from 2017 to 2021, consumer satisfaction with bots actually decreased by 10%.
What’s more, introducing Chatbots without an effective strategy can also damage the quality of interactions on your other channels, too.
Imagine a customer has used your website Chatbot. They’ve spent 5 minutes answering a host of questions, only to be told they need to call in anyway. So they ring up and begin the interaction already frustrated. Then, the agent asks them to repeat everything they’ve already told the Chatbot. Not exactly a recipe for a meaningful customer outcome.
Thankfully, there is a solution: data integration.
Delivering meaning with data
Problems with omni-channel experiences typically arise when brands don’t make full use of the flood of data pouring into the organisation every day. In the example above, the customer frustration occurred less because they had to use a Chatbot and then call an agent, and more because they had to repeat themselves. The 2 experiences were disjointed because the business wasn’t tapping into the data from the first interaction – and the overall customer satisfaction suffered as a result.
Now imagine the same situation, but this time with data integration. Now, your customers spend a couple of minutes engaging with a Chatbot using AI and machine learning to anticipate their needs, because it integrates with your CRM, purchasing and other relevant systems. They still need to call an agent to resolve their issue, but they’re already less frustrated because the Chatbot experience felt more tailored (with fewer standardised intro questions). When the agent answers, he or she can immediately see what the customer said to the bot and dive straight into resolving the issue.
As a result, the customer gets what they were looking for in a seamless end-to-end experience, and presto: a meaningful outcome is delivered.
Using historical data is only half the story. To really drive meaningful experiences, you need to combine the historical (what customers have done before) with both the present (what customers are doing now) and the future (what they’re likely to do).
To achieve this magic combination, you need to bring disparate journeys and data sources together in one place, so you have a real-time view of what your customers are doing at any given time. You can then combine this with AI and machine learning to determine customers’ Next Best Action and eliminate guesswork from customer experience optimisation.
Meaningful, not happy
These data-driven experiences won’t always result in happy customers. Sometimes packages are lost or a product is faulty. Even when you provide a replacement product promptly, the customer might not feel happy. But it’s a meaningful and fair outcome. The customer knows you listened and that you value their business. And that’s a foundation for a customer service approach that delivers loyalty and operational efficiency.
To find out more on how Engage Hub can help you achieve a data-driven, unified view of your customer service, read our latest whitepaper on measuring, visualising and optimising contact centre performance.