Post Office Money insurance revenue optimisation case study - Inasight
page-template-default,page,page-id-16198,ajax_fade,page_not_loaded,,transparent_content,qode-theme-ver-13.8,qode-theme-bridge,disabled_footer_top,wpb-js-composer js-comp-ver-5.4.7,vc_responsive

Post Office Money insurance revenue optimisation case study

Post Office Money

“Post Office Management Services (POMS) is the insurance business of the Post Office and a key element of our growth strategy is to understand more about our customers by improving the analysis of our data assets. Improving insight in this way creates an opportunity to improve our customer propositions as well as creating sustainable competitive advantage for the business.

We engaged Inasight to focus initially on our Travel insurance product, to provide key insights and recommendations into pricing strategy and quote-to-sale conversion rates.  Inasight successfully delivered this over a short period of time by interrogating multiple sources of data using their RAMP platform deployed on AWS, undertaking extensive data preparation and then building predictive models and visualisations. Security is a top priority for us, making the robust security measures and techniques that Inasight applied crucial for this work to even be possible.

Business Outcomes

The result was a set of key business recommendations underpinned by exciting discoveries mined from the data, giving us a far deeper understanding of the drivers of success of our business operation and a set of significant optimisation activities.

One aspect I particularly liked was their iterative approach, with time-boxed deliveries and regular feedback during our discovery journey, whilst retaining a high-degree of flexibility and pragmatism. I found the experience to be very professionally managed throughout, with Inasight bringing the skills, experience and thought leadership needed to make this opportunity a reality for us.

I would highly recommend Inasight to any organisation who needs a rapid way to securely uncover the latent value of their data assets, by seeing the opportunity to exploit machine learning and predictive models to advance their business operations.”

David Pope, Head of Business Development, POMS