Driving Customer Personalisation And Relevancy Through Ai And Machine Learning

Dan Cantorna, Director Of Data Innovation, Collinson

Dan Cantorna, Director Of Data Innovation, Collinson

In today’s competitive marketplace, customer expectation is higher than ever. For brands to cut through the noise, they need to deliver hyper-personalized and contextually-relevant communications to customers.

Collinson provides travel, assistance, and insurance products to differentiate our clients’ value propositions, and loyalty solutions that help them win deeper, more valuable customer relationships. We help clients to drive richer engagement and deliver smarter customer experiences that improve loyalty across the travel, financial services, and retail sectors. Artificial intelligence (AI)—specifically machine learning—plays a vital role in refining loyalty campaigns we run on behalf of our clients and helping to combat insurance claim fraud.

Prior to building our in-house AI capability, it took a dedicated team of four within our loyalty division a whole month to produce, execute, and issue a client communications campaign with 60 variations to it. Now a ‘smart engagement’ content selection engine ensures each customer gets a unique communication at the right time, based on specific criteria like their data, preferences and previous purchases.

Rather than using rules to decide which offers, promotions, and products to share with each individual customer, a series of machine learning

We help clients to drive richer engagement and deliver smarter customer experiences that improve loyalty across the travel, financial services, and retail sectors

algorithms are linked together to decide which are the best products, relevant offers or loyalty rewards to promote. This also helps determine the best time to capture their attention and cut through the competitive noise—all done via the right channel and with minimum human effort.

Machine learning has streamlined the entire process at the touch of a button, we now send hundred thousand variations of one-to- one personalized communications and deliver in 15 minutes. This would otherwise simply be beyond human capability. By replacing more repetitive and mechanical efforts, it has also enabled the team to focus on more strategic, creative, and emotional activities, like generating appealing customer content.

Not only has the level of personalization increased in the content, but the sophistication of the offer and the timing have also been individualized. This has had a profound effect on campaign performance. It helps our clients drive loyalty with their customers and achieve a deeper, more valuable relationship.

In our insurance division, we have used AI to protect our clients against fraud. AI and machine learning are predicting which claims are more likely to be fraudulent by identifying anomalies within them, as well as within processes and patterns that wouldn’t be recognized manually. We have also developed an in-house user interface (UI), allowing fraud teams to visualize relationships between fraudulent claims and identify fraud rings operating as organised fraud entities. Looking ahead, AI will help to make us smarter in the fight against fraud and organized crime, and help to reduce the cost of fraudulent claims, which would otherwise impact the overall loss ratio. We can then allocate those resources to improve the precision of our underwriting models.

By embracing the power of AI and machine learning across the business, we can analyse large amounts of data and reveal correlations and relationships in a very short space of time. This puts us in a better position to improve our processes, use data effectively to build a robust view of our customers, and provide more highly personalized and contextually-relevant communications and experiences for our clients’ customers.