What is personalised pricing?
Personalised offers – and personalised pricing, specifically – can be seen everywhere. Whether we are conscious of it or not, we are receiving ‘personalised’ offers on many products and services on a regular basis.
E-commerce likely springs to mind as a relatively clear example of where this takes place. The personalised offer we receive, which is in part based on our observed behaviour, will likely include a personalised price but might also include a personalised promotional or delivery mechanic designed to positively influence our likelihood to take up the offer. Subscription-based services are another example of personalised pricing in our everyday lives.
A markedly different distinction for personalised pricing exists in sectors like retail banking and insurance which involve a new element in personalised pricing: risk. As consumers of products and services in this sector, such as personal loans or credit cards, we all have different risk profiles, and service providers need to accurately account for this and set pricing appropriately in order to be profitable.
Customer Data and Personalised Pricing: Winners and Losers?
Understanding grouped and individual customer risk profiles is a critical activity for many organisations and one that will often result in differences in specific offers and pricing. In order to do so, access to relevant customer data is paramount. But as an individual, is it in my best interest to allow these organisations to access and use my personal data? Will I be rewarded or penalised for doing so?
At a general societal level, these are serious considerations. With products or services which include a risk-based component in their pricing, as outlined above, are we in danger of ensuring that certain ‘risky’ socioeconomic groups are effectively deemed ineligible for some offers or end up facing prohibitive and ultimately unaffordable pricing?
The Journey to Personalised Pricing
Many organisations are yet to transition to offering personalised pricing, but with often clear differences in customer risk profiles and their willingness to pay, this approach is likely not sustainable.
However, the journey towards offering personalised pricing can include several potential barriers. To begin with, data infrastructure considerations might not be trivial in some cases; this may include data housing and organisation and having the right skillset in your organisation to manage this.
Beyond this, unlike at an aggregated group level, determining price elasticity and designing appropriate pricing at an individual level, based on very granular predictive models, is not straightforward.
