Serving your customer in the best possible, most efficient way should always be the major goal of any organisation. The demands for a faster and better customer experience have changed over the years with a major acceleration happening in the past 3-4 years. Customers today are hyper connected and expect personalised service.
Major market disruptions changed the way we operate today. Uber changed the Taxi industry, Amazon and Ebay changed the way we buy our products and Google is changing many aspects of our lives with technology that we did not even dream of a decade ago.
Customer analytics must change if companies want to meet the new demands from customers and changed market conditions. The are numerous examples where new technology and real time customer insights have increased the Customer Experience and produced more personalised offers with much higher conversion rates than previous offerings. The classic example is the “Customer who bought this …. also purchased that …”. In this example additional offers are created in real time that are based on customer segmentation, purchase history and customer profiling. The technologies and algorithms that are being used by companies are becoming increasingly more sophisticated and more personalised.
With cloud computing it is increasingly more affordable to run large computing clusters that are capable of processing customer insights in real time and integrating directly into the operational systems that service customers.
Adopting the cost efficiencies of cloud offerings allows organisations to automatically scale the processing requirements at any time, making it now affordable to collect, store and process data closer to the customer-business interactions.
The diagram above shows the main processing components of a modern Data Lake. Whilst this does not seem to be revolutionary the high level of latency in the processing steps is vastly different to the traditional Data Warehouse based analytics models.
Data is collected in its raw format and stored on low cost storage facilities. At the same time customer transactions are pushed into streaming storage such as Kafka or Kinesis which initiates real time processing and analytics.
Cluster technology such as Hadoop MapReduce, Apache Spark or Apache Storm allow the process streaming of hundreds and thousands of customer transactions and/or enquiries in parallel within seconds. These clusters correlate information in real time with all the data available in the Data Lake in order to provide the personalised offers we mentioned earlier.
As a result, organisations achieve massive benefit by bringing customer insights closer to the actual customer interaction. In a recent project that we implemented in the Airline industry it was possible to make booking and boarding information available to front line staff such as cabin crews via wearable technology. Within seconds, a boarding event would alert the cabin crew when passengers with special requirements, eg. wheelchair or VIP customers, entered the gate allowing them to act early and provide a better and more efficient, personalised service.
Let Fusion Professionals work with you to help move your organisation towards a customer focused “Real Time” data insights landscape.
With 30 years in the IT industry he is an Expert in Enterprise Software and Data Architecture, Data Governance frameworks and modern analytics platforms for Big Data and Data Lakes.
Opmerkingen