Statens Pensjonskasse

Customer segmentation based on behaviour data

Cost efficiency is vital to Statens pensjonskasse (SPK) and a high degree of self-service an important mechansism to achieve that. But, pension schemes are complex in nature, making people uneasy and administrations confused. At the same time SPK are in the midst of implementing a new public pension scheme, as part of the pension reform. How do you make customers more self-serviced in this situation?

Behaviour data for impoved segmentation

Customer segmentation based on behaviour data is the quickest way to identify business potential. By quantifying and capturing real behaviour data, old truths and hypotheses can easily be rejected or confirmed. Patterns of past behaviour provides the most reliable way of predicting future behaviour. Behaviour data enables identification of segments for which a behaviour change will have the greatest business impact.

Cost efficiency is vital to Statens pensjonskasse (SPK) and a high degree of self‑service an important mechansism to achieve that. But, pension schemes are complex in nature, making people uneasy and administrations confused. At the same time SPK are in the midst of implementing a new public pension scheme, as part of the pension reform. How do you make customers more self‑serviced in this situation?

SPK provides pensions, insurance and mortgage loans to employees in the government sector, educational sector and public institutions. SPK manage pension funds for 1300 public institutions and more than 1 million employees.

What is the actual customer behaviour and why do the customers do what they do? In order to change their behaviour, current behaviour must be mapped, the underlying causes understood, as well as the drivers for and the barriers against self‑service.

Traditional segmentation models, based on demographic variables and product subscriptions did not provide the right clues. We wanted to challenge old truths about the segments by adding behaviour data to the mix. Hence, we defined and quantified key behaviour, such as degree of self‑service, channel preference and levels of engagement, by extracting data from SPK's business systems.

A cluster analysis on these data provided new insights and a completely new segmentation model, enabling us to pin‑point high potential segments, such as segments high on engagement, but low on self‑service. Through qualititive reseach and analysis, we deep‑dived into their drivers and barriers.

All this enabled us to be accurate and specific when defining the measures and list of actions for execution. We developed new service concepts, tailored to each new segment, which addressed their particular needs and SPK's internal efficiency.

“

Too many companies have stereotypical views of who their customers really are. Using behaviour data is an effective way of killing old myhts.

”

Sarah Fjeld Oueslati, Lead Data & Behaviour Scientist, Mindshift

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