Last week I attended the virtual launch of ‘Protocol to Manage Relationships Today’. I wrote a piece in this book about relationship management in the age of big data and artificial intelligence. In it I advocate a new paradigm on Customer Relationship Management (CRM) systems. I call it data-driven relationship management. Put simply: basing your efforts on empirical rather than anecdotal evidence.

Tea with the Queen

In the book – which is about protocol, etiquette and optimal use of networks – I show how a data-driven CRM could define the guestlist for a tea party with the Queen of England. Although Covid-19 is now restricting everyone’s guest list, the benefits of using empirical over anecdotal data are still the same.

  • The information used to inform relationship management decisions is much more varied and not found in one place;
  • The insights obtained from the data are not only used to reflect what happened in the past, they also prescribe what should be done in future.

CRM maturity scan

You can check your organisation’s chances of success in applying data-driven relationship through a CRM maturity scan. Ask yourself two questions:

  1. What is your ability to collect valuable data?
  2. What is your ability to extract the right information from that data (and act on it accordingly)?
Figure: The four levels of data-driven relationship management

Your score puts you into one of four categories which I describe as Beginners, Academics, Practitioners and Experts.

This simple scan is the starting point for a discussion on how to accelerate your organisation’s transition to data-driven relationship management. Read our case on how data improved a Life Science relationship management network.

Interested to learn more about data-driven relationship management?

Leave your contact details and download ‘Relationship Management in the Age of Big Data and Artificial Intelligence’

    Rik van der Woerdt

    Rik is Amsterdam Data Collective’s Managing Director. As a consultant, he has worked on strategy and risk management projects in a range of industries. Specialising in quantitative strategy and communicating complex statistical analysis to a broad range of business stakeholders, Rik enjoys establishing the interface between model design and application.

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