Customer 360 Analytics

Using our performance management reference model we will help define the key measures of success for your business. The framework will focus on the following:

• What happened? What are the Key Performance Indicators (KPIs) for your organisation that define success?
• Why did it happen? Identify leading indicators which influence the success of your KPIs
• What is happening now? Understand how to monitor current business activity
• What’s the next best option? Understand where decision services can be used to decide what decision should be made
• What will happen? Predictive analytics that will help understand what is likely to happen

We will interview stakeholders to identify needs for different types of users such as key report viewers, casual report viewers, data explorers and super users. For each user type we can identify:

• Information requirements
• Responsibilities
• Challenges they face
• Capabilities they require

The Performance Measurement framework and user needs analysis will be used to develop a customer insights platform to help derive insight from your data. The insights platform is a smart analytical framework that takes the cleansed, conformed data from the data management platform and makes the insight actionable.

The consolidated view of the customer created by the data management platform allows us to build a Customer event stream to give us a longitudinal view of customer behaviour. This capability gives us the capability to create a chronological series of events based on every engagement the customer has with your organisation. The goal of the event stream is to enable the development of insights that will allow more effective communication with a customer

The capabilities of the Customer event stream becomes even more interesting when advanced analytics are applied to create predictive consumer attributes. Examples include:

• Customer segmentation: Segmenting your customer base by value, behaviour or socio-demographic means
• Classification: Predicting customer behaviour based on propensity scores, such as churn scores, cross-selling scores, and so on

The insight found from the customer insights platform will allow us to identify key points within existing business processes where decision services could be deployed to improve business process performance. For example, improved x-sell effectiveness or reduced customer churn

The decision services can either be:

• Business rule driven decisions i.e. the service is embedded within a business process
• Event driven actions i.e. the business process is triggered by the decision service monitoring incoming events

An experiment framework allows us to understand if the decisions are working by deploying a Test-Measure-Learn cycle:

1. Build tests: For a given decision service, design and build a testing framework to understand if the decision service is optimised. A typical way of doing this is to use A/B testing where two experiments are conducted and results analysed to understand which one was the most successful
2. Measure metrics: Agree what metrics will be used as a barometer of success and measure the tests using these metrics
3. Learn: Use the results to learn what worked and design further tests to optimise further