Customer Decision Platform – Part 2

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by

aeadmin
in , , ,

Data vs Information vs Insights vs Decisions

Much has been discussed and demonstrated about the importance of data. Ranging from life saving treatments, through medical data, to increasing “engagement” in social media by exploiting  predictors derived from addiction. 

At a high level, Data is defined in 3 stages:

  1. Raw structured and unstructured Data
  2. Information
  3. Insights

Let’s go into detail. Specifically, the lifecycle which transforms raw data to insights. Remember, insights are the fuel for decision making. Indeed, understanding this lifecycle is fundamental in completing successful decisioning platform projects.

Data management is challenging in most organisations. The older the legacy systems – the bigger the challenge. Source data travels through many staging and transformation steps before being considered “Business-friendly”. The 3 stages describes a simple model of a complex reality. As Data management capabilities become more user friendly, I expect data infrastructures to be built this way.

  • Data is raw and unprocessed facts.  Example: Customer lives in South-East  London, postcode SE17 
  • Information is data that has been processed, aggregated, and organised into a more human-friendly format. Example: Customer has purchased 6 rechargeable batteries within a 1-year period
  • Insight is gained by analysing data and information in order to understand the context of a particular situation and draw conclusions. Example: Customer has 60 % probability to churn based on the recent service downtime. 
  • Decisions are then made from many insights.

The last point is a core principle in Decision logic frameworks. Decisions should be made on insights. Not on raw data (I.e., customers data of birth) and not on information (i.e., number of times a customer has topped up within 2 months). This is an important principle which unfortunately, is lost in most decisioning projects. 

This brings us to the challenge. How to ensure that

  1. Insights can be added and updated rapidly
    1. An insight does not last forever – lifespans vary from months to days
  2. Insights can be added with minimum technical support

These challenges will be touched on later. 

Customer Data Platforms operate on the basis of delivering insights. However in a moment in time a customer may have multiple strong insights which a Marketer could act on. So which insight should be acted upon? This is the core task of Next Best Action. Insight arbitration.