We have all been there. Racing to the airport, panicking through check-in, a sweaty run to the gate. All to find your plane has been delayed by 10 hours. Queue hours on the phone and days until you get a resolution. But what if you were compensated immediately. The airline sends you e-vouchers for dinner. They even give you some placatory air miles there and then. You would probably feel much more well disposed towards the airline wouldn’t you?
The difference is dealing with customer experience – or CX – in real time rather than retrospectively. It’s all very well finding out how angry a customer is two days after their terrible experience, but if you can deal with it immediately, you have a much better chance of salvaging that relationship.
The problem with many organisations and their approach to CX, is that it is a retrospective exercise. In addition to time delays in picking up on customer issues (or opportunities), customer research – upon which entire marketing strategies are based – is based on historic data. For instance the main tool of CX capture, the pervasive customer feedback survey, only captures a snapshot of a small sample of customers at a static point in time. But customers’ perceptions, behaviours and situations are constantly adapting and evolving in real time. So CX needs to keep pace.
If you want to get a competitive edge, you need to use all available data – collecting interaction data from customers, the touchpoints (website, smart phones), financial and operation systems – to identify problems, trends and opportunities in real time. And you need to be able to respond swiftly. To do this you need to invest in your data and predictive analytics.
So what are some of the main components of predictive analytics in CX?
- Pooling customer data: data gathering is the first step, checking all sources – financial, operational, the customers themselves – and aggregating it, usually in the cloud. Creating data cohesion enables organisations to track customer behaviour across their interactions with the organisation, their transactions and any operational engagement. This enables you to visualise the precise direction of the customer journey and pinpoint when they might be experiencing problems or when they might benefit from another product or service.
- Analysis through algorithm: five years ago, CX was primarily marketing driven, but now it has to be driven by technology, or at least, technology is the key enabler. Analytics – through machine learning algorithms – can help to identify customer trends, track what is affecting customer satisfaction and identify specific moments in customer journeys.
- Move to action: the information that is derived from the data then needs to be acted upon. It’s shared throughout the organisation, through different applications – CRM for example – and employees can be alerted about actions they need to take in order to solve customer problems, personalise experiences and overall, improve CX. The truly digitally ambitious organisations feed these opportunities directly into their agile delivery engine for immediate prioritisation, exploration and action to drive a truly virtuous continuous improvement cycle.
The main takeaway is CX should not be a static exercise, assessing how customers are feeling and behaving at one specific point in the past. It needs to be technology driven, in real time, enabling an immediate response. So if organisations want to get and retain a competitive edge, predictive analytics is the only way forward.