The journey to personalisation

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

 

With the abundance of data available to insurers, there is a great opportunity to create personalisation that drives engagement and improves loyalty and revenue. As your approach to data becomes more sophisticated, you’re able to generate real-time insights about your customers – for example, they could have access to a live feed on their policy information which potentially updates once a claim has been settled and the insurer gets more information on what product they need, which could lead to an upsell/cross-sell opportunity. As a result, you’re able to better understand your customer’s behaviours and needs. With this understanding, you can build bespoke loyalty programmes, including elements of gamification (that we commonly see with health insurance) and, in turn, begin to influence customer behaviour for the benefit of both the customer and the book.

People are more willing to share their personal data in return for an improved experience or product, which means they are more open than ever to help you understand and manage their perceptions and expectations.

 

Choosing the right strategy

Personalisation can take a variety of shapes and in general customers already expect some level of personalisation in their customer journey, such as in emails, direct mail and so on. In a recent study conducted by Collinson, only 21% of insurance customers felt that offers or loyalty initiatives were personalised to their interests or preferences and less than a quarter (24%) believe that their insurer treated them like an individual, not just a number. 31% of customers reported receiving communications that were not personalised to them at all (e.g. saying ‘Dear customer’). Other studies suggest that 48% of customers will spend more when their customer experience and/or journey is personalised.

In order to execute a personalisation strategy, you need different levels of data manipulation and modelling depending how sophisticated your strategy needs to be.

Great personalisation is only as good as the information and insights that can be obtained, analysed, and acted upon, which means it’s crucial to use data in the smartest way that benefits the customer.

If you’re reviewing or implementing personalisation in your business, the different types for consideration are:

  • Segmentation: This is the most prevalent type of personalisation, in this type customers are put into buckets according to some general categories, for example industry, department, geography, purchase type, etc. Segmentation helps increase the matching of communications and offers to fit the general needs of each individual segment, but it is still limited as it will still need to appeal the entire segment.
  • Persona based personalisation: A persona is a digital data representation of a customer type, where their needs, desires and characteristics are all mapped out. This will create a segment by each persona type. Again this type of personalisation is limited as you cannot have infinite number of personas, normally you will have 10 or so personas to make the table executable from the systems perspective.

Example of persona with an insurance offer

  • Personalisation based on customer journey: Key input for this type of personalisation is a journey map, where all the steps are illustrated and dependencies are identified. This map then needs to be divided by each persona characteristics and needs, to reflect what they would like to see in their customer journey.
  • Individual specific personalisation or individualisation: In this type, the main difference compared to the previous versions is the clarity to step away from a ‘one size fits all’ approach and embark on a journey towards true personalisation or individualisation. This means that the customer journey or, in the case of insurance, the insurance cover and policy will be targeted to each individual need, the unique circumstances, interest and fundamentally the customer expectation in order to design a policy for each individual. In order to achieve this in practice you will need the ability to optimise in real time based on actions, preferences or – in insurance - is based on an individually calculated risk profile which get us closer to real-time underwriting. Individualisation needs very powerful tools for data processing and data science becomes an essential building block in order to achieve.

For insurers that get personalisation right, the benefits include increased customer lifetime value and subsequently, profitability - a board-level objective for most insurers at present.

 

Beyond segmentation – driving relevancy through AI and machine learning

As you grow increasingly sophisticated in your segmentation approach, it becomes more likely that artificial intelligence (AI) – specifically machine learning – will play a vital role in your communications, for example in helping to refine loyalty campaigns and even beyond by combatting insurance claim fraud.

Our journey: in focus

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 certain 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 algorithms are linked together to decide which are the best products, relevant offers or loyalty rewards to promote and to 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, and at the touch of a button, we now send a hundred thousand variations of one-to-one personalised communications and offers in just 15 minutes. This would otherwise simply be beyond human capability. By replacing the more repetitive and mechanical efforts, it has also enabled the team to focus on more strategic, creative and emotional activities, like creating appealing customer content.

 

A move to segments of one

Not only has the level of personalisation increased in the content, but the sophistication of the offer and the timing have also been individualised. 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. Looking ahead, AI will help to make us smarter in the fight against fraud and organised 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 personalised and contextually-relevant communications and experiences for our clients’ customers.

 

 

 


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