Predictive Behavioural Analytics

What if you understood why people do what they do and could anticipate what each and every customer is likely to do next?

Predictive Behavioural Analytics is a brand new way of combining data science with behavioural science to understand and predict human behaviour. It does this by identifying what people really care about and why - what matters enough that it influences the actions that they take. 

These insights are then used to build a library of events that are known to subtly 'nudge' customer behaviour. By combining this with an understanding of a person's current mindset, it's possible to predict what decision they will probably take next - when one of those events occur, and to act accordingly. The big difference with predictive behavioural analytics is its ability to do this at scale, at speed and frequently.

Neuroscience research has shown that all human decisions contain some emotion (which is not to say that all human decisions are emotional). However, our approach considers a range of factors that contribute to how we evaluate a situation and make a decision:

• The task and the context (what stimulated us to act, what we want to do)
• What we need (rational criteria like price, convenience)
• What we want (emotional criteria like increased happiness, less fear)
• Internal biases (such as behavioural or unconscious biases)
• External influences (like recommendations, star ratings and so on)
• Personal experience (what we expect, based on upon our prior history)

Predictive Behavioural Analytics puts the 'why' into the '5 Ws' (who, what, where, when, why) and can play a role at both the strategic and tactical level; 

For leaders and strategists, it turns a Customer Experience (CX) vision into a practical, disciplined business strategy, with directly quantifiable results on the top and bottom line, as well as the ability to balance the cost and rewards of competing strategies,

For those with operational responsibility, it can be embedded within operational systems, providing automated decision-making on the basis of the optimal next best action to take at the individual customer level. Furthermore, it allows an organisation to become proactive - anticipating customer behaviour (rather than just responding to it).

For customers, the result is a customer experience that feels more 'human' and in touch with their needs - one for which they will pay more, be more loyal to, and recommend.

Whilst human behaviour is too complex and messy to ever model perfectly, this new approach gives organisations a new and pragmatic tool for thinking about their relationship with all their customers and for making better decisions.