As we move into a digitized era, customer service and experience are becoming more and more essential. The innovation of internal processes in the face of fierce competition from competitors is necessary to Big Data and with Big Data data must come simplification and efficiency for the delivery of superior customer experience.
Machine Learning technologies are expected to evolve within retail business, where a 27% annual growth is expected up until 2020. The importance of evolving processes for data insight to enhance customer experience is now too important to ignore. The pressure’s on retail enterprises to simplify their strategy to deliver a faster, efficient service to stay ahead of competition. Take a coffee shop for example, the customer base who want coffee every morning to kickstart their day is huge. If customers aren’t going to be served quickly, they will go elsewhere (to another competitor).
Nowadays, innovating technologies now allows for the board of directors to use Big Data to leverage improvements to the customer experience. So, how will business use innovated AI technologies to simplify their processes to promote innovation yet simplify customer experience?
Businesses need to learn to develop innovating systems that use deep-learning algorithms to fine- tune the customer experience holistically and ensure the correct content is delivered to the customer. Here are a few pointers to look out for:
- The Facilitation of Self-Service
One of the more successful cases of using ML innovation is the use of self-service interfaces. In particular within retail, where the prospect of using chat-bots or virtual assistants, for example, to perform administration activities like cashing in a bank cheque or virtual assistant to answer question and perform tasks. Self-service must be responsive upon input and be intuitive to understand basic customer needs and behaviours. This will help customers become favourable to self-service as they see they can access the content that they need.
- Perform Analytics to Understand Customer Behaviour
Nowadays, you see many technologies that use deep-learning algorithms to analyse and understand customer behaviours. This is to ensure that the trends of a customer journey are analysed and used as a means to deliver what they need to see through predictive analytics. This can be thought of as customer and relationship engagement which will ensure customer expectations can be handled and be active to manage any corrections to retain customer base. An example, a user of the Uber app uses it to call a cab driver to take them to a particular location. Uber uses this information to analyse usage patterns. If the usage is low, deliver promotions for users to retain them. Or, optimize your favourable locations and rather than expecting the customer to input their destination, it will be optimized for them through identification of user behaviour on the app.
- Deliver Efficient Customer Support
When businesses are inundated with internal issues and incidents which are affecting the end customer, customers are prone to moving on to a more reliable company. This could be due to a lack of resource experience or resource personnel to drive the resolution strategy immediately. With the use of deep-learning algorithms within ML technologies, these systems can allow for a better experience by learning resolutions behind key issues and to enact them at an appropriate time. ML algorithms can identify the root cause and immediately action the resolution without the need for user input.
