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Case Study

FTSE250 Insurer Drives £1m Incremental Revenue


In today’s digital age companies need to harness their consumer data effectively to create and protect customer value. One increasingly common approach to do so is the use of next best action modelling to drive personalised marketing. Companies that use this type of modelling will predict what the best thing for an individual customer might be, spanning the gamut of potential possibilities—product, action, non-action—all whilst accounting for the customer’s browsing or purchase behaviour.

If it sounds complicated, that’s because it is often notoriously complex, which makes NBA a difficult thing to get right even for large companies with many resources at their disposal.

MOJO-CX© specialises in increasing customer lifetime value using data-driven marketing, so NBA algorithms and marketing campaigns are a common delivery for our consultants. They often demonstrate that the companies that can nail the right approach—which usually blends clever data modelling and machine learning with bespoke marketing—consistently reap more value from their customers.

The Challenge:

A FTSE 250 insurer had recently expanded their product portfolio and invested in a NBA algorithm to embed the new products into the market. Ultimately, the insurer wanted to reduce churn and increase market share to maximise customer lifetime value. Insurance is an industry with relatively low margins since there is often little direct control over price, so once a customer is acquired, even on a low price, it was cost effective for the insurer to cross-sell products where appropriate.

Unfortunately for our client, they went to a big four consultancy firm to build out the solution and after 16 months, had no clear indication of the benefit in terms of ROI versus statistically efficient control groups.

MOJO-CX©’s work:

Our work was clear cut. Our client was 16 months overdue on delivering value to the bottom line so we had to identify both the issue, solution and build a quality alternative, quickly.

We split our work into the following streams and spent the next 8 weeks building and deploying:

  • Data Processing and Selection: we looked at how the data was processed and what data was being looked at: a red flag in the former firm’s model was the lack of previous purchase data and use of different marketing channels.  
  • Rebuilding the model: predicting the likelihood that a customer would buy a certain product based on who they are, their previous behaviour and the channel they were most likely to buy from, whether dotcom or outbound marketing.
  • Optimising outcomes for best profit: In our experience, most companies that tried to do NBA get this step wrong with the highest frequency. It isn’t enough to look only at probability; firms need to account for profitability as well. A customer that is 80% likely to buy a product worth £10 is not as valuable as a customer that is 60% likely to buy a product worth £20, and marketing campaigns and overall level of effort can then be adjusted accordingly to optimise marketing return on investment (MROI) and profit.
  • Campaign and Campaign Selection: We ensured that we created statistically robust control groups to ensure that there was no skewing of the results. It was important given the context that the senior leadership team understood that any new results could be fully trusted.
  • Training and Deployment: As we deployed the customer journey orchestration tool and marketing collateral, we ensured that call centre agents were given the correct training on how to use the approach most effectively.

The results:

A whopping £750k in incremental revenue for the insurer; and, since we deployed very quickly our client was able to reap these benefits in year. Outer year benefits are still being realised!

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