As an industry, insurance doesn’t have a reputation for moving quickly. Especially when it comes to technology modernisation. However, according to this Bank of England AI survey, 95% of insurers are implementing AI strategies to enhance data analytics, anti-money laundering (AML) capabilities, fraud detection and cybersecurity.
And this makes perfect sense.
AI is well suited to repeatable processes, underpinned by high-quality proprietary data. Up until now, the most common adoption of AI in insurance has focussed on improving decision speed, reducing fraud or loss and enhancing customer experience.
But what if we told you, you could use AI to gain a competitive advantage? What if AI could optimise your customer journeys, build better user experience and ultimately, increase conversions? What if an AI optimised conversion journey could grow your revenue?
In this article, we’ll explore how insurance organisations can utilise AI optimised conversion journeys as part of their digital transformation. The end goal is simple: achieve better outcomes and better results.
Understanding Conversion Journeys
Before we look at how to optimise conversion journeys with AI, we need to understand those journeys and map customer intent. By following the AIDA-R Funnel, we’ll highlight specific AI tools and strategies that can be used to optimise journeys at the awareness, interest, desire, acquisition and retention stage.
A & I: Optimising Awareness and Intent Customer Journeys with AI
For years, insurance organisations have been competing with aggregators for customer acquisition. Comparisons sites make it easy for users to compare policies and find better deals.
Appius have first-hand experience working alongside these highly optimised conversion machines. We know it’s the insurers cost per acquisition (CPA) that takes the hit. Using AI in your conversion journeys not only gives you a competitive advantage in the awareness and intent phase of the funnel, but it builds a brand fit for direct-to-consumer trading.
Aggregators
Large Language Models (LLMs) like ChatGPT, Gemini and Copilot are far more likely to mention or cite insurance organisations than they are aggregators. They look for specific trust signals such as:
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Topical authority
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Digital PR
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UGC/customer reviews
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Accreditations
Insurance brands own their products. They are the most trusted sources of information, with all the above-mentioned authority signals pointing AI crawlers back to the brand. In June 2026, Aviva was mentioned 191 times by ChatGPT. Confused.com was only mentioned 85 times.
The rise of AI Search is opening doors for insurance companies that couldn’t compete against aggregators in traditional search. Being part of that conversation is key to brand awareness, building trust and guiding that customer through the conversion journey.
Want to get your insurance brand mentioned and cited in ChatGPT, Gemini or Copilot? Speak to our accredited SEO and GEO experts to find out how we built an AI audience of over 5,000,000 for a financial services client.
The Search Experience
All of our clients have been receiving cold emails from so-called GEO expert claiming they can get their insurance brand mentioned in ChatGPT.
Firstly, ChatGPT was only launched in October 2022, Claude in 2023, and Google’s Gemini not until 2024. Pop-up experts are keeping up with the algorithms as quickly as the rest of the digital marketing world, even with their fancy new LinkedIn job title.
Secondly, search is still search. It just has a different home now. But this isn’t new – social media has been stealing Google’s share of market. TikTok overtook Google as the most used search engine by Gen Z and millennials in 2022 (note: nobody took the opportunity to become a TEO expert).
Large Language Models (LLMs) use search engines to find your content in the first place. Getting your insurance brand mentioned in LLMs requires a great SEO strategy, combining content topical authority with technical crawlability and supporting your efforts with digital PR (off-page SEO).
PPC and AI
Most of the insurance companies we work with treat PPC advertising in the same way. It’s a bottom-of-funnel conversion engine, built to capture high intent searchers ready to take out a policy.
But insurance search terms are some of the most expensive keywords available. And in 2026, with Google adding AI into the mix, the old PPC strategies simply aren’t as powerful.
Rather than focus on conversion, insurers should consider Google Ads as a demand generation tool. Brands should embrace the audience signal based campaign formats such as Performance Max (not a new format but always evolving) and AI Max for search.
Insurance brands have previously struggled to adopt some of these campaign formats, due to:
Lack of control of creative assets
Over-use of automation
Tricky to get through internal compliance reviews
However, at Google Marketing Live it was announced that new controls are being added to the formats. These controls enable the omni-presence of risk statements and brand toolkit instructions for asset combinations.
With Google Search campaigns struggling to keep up with the growing volume of long, unique queries, utilising AI tools to match query intent is key to winning the keyword bids and keeping the absolute top of page impression share higher than competitors.
Appius has been able to improve the performance of its Google Ads travel insurance client by refining the signals and using all the enhanced functionality available with the performance max campaign settings. This combined effect has generated 35% more purchases from £40 less budget in the 1st half of 2026 vs the same period in 2025.
Get in touch to find out what our Google-accredited PPC experts can do for your insurance organisation.
D & A: Optimising Desire and Acquisition Conversion Journeys with AI
Using AI to get people onto your insurance website is one thing, but with the cost per acquisition rising every year it’s important to curate and experience that converts.
By the time a user lands on a website, they’re aware of who you are and interested in a product you offer. The desire stage is where they will either choose to abandon or convert. How us AI helping increase conversions at the desire stage?
Conversion Rate Optimisation and User Experience
Optimising user experience to improve conversion rate starts with an expert understanding of how users behave. Appius use UX monitoring tools like Lucky Orange, Hotjar and Microsoft Clarity to identify behavioural patterns, sticking points and areas requiring optimisation.
What used to take hours of resource, watching recordings and noting matching behavioural patterns, is now assisted with AI summarisation tools. These tools provide a quick view of user trends, helping generate insights that can be turned into actions.
Split Testing and Personalisation
Split testing is key to conversion rate optimisation (CRO) and building a genuinely useful digital experience. Over the years, Appius has tested almost everything… from font pairings to form positions, CTA copy to imagery. With every experiment, we learn something new.
Experimentation platforms like Optimizely Web Experimentation are using background machine learning to determine statistically significant test results. By focussing traffic on variants most likely to produce a statistically significant result, these dedicated AI tools are also supporting targeting or throttling results more efficiently.
Split testing helps us make a site work harder, optimising every element of the page for to become the ultimate conversion machine. But many digital experience platforms, including Sitefinity and Sitecore, take that a step further by modelling propensity for conversion against certain segments. With this AI personalisation feature, insurance organisations can use the in-built AI to target users at the exact moment they are ready to convert.
Data and Analysis
AI powered tools in platforms such as Sitefinity Insight enable you to dig deeper into your audience data and make it actionable. This level of analysis has multiple uses that support conversion rate optimisation:
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Identify the attribution of key touchpoints that lead to conversions
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Discover potential segments based on common behaviours
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Evaluate large datasets quickly
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Find content that doesn’t resonate, causes customer drop-off
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Create personalised and targeted content for segments to increase conversion and retention
As Sitefinity Partners, Appius has recently been able to use Sitefinity Insight to track conversion journeys for a financial services client from initial website session through to mortgage completion. This data is essential for optimising budgets, enabling us to prioritise channels that deliver the highest quality leads.
AI Chatbots and Virtual Assistants
Live chat isn’t a new feature. But adding AI into the mix creates an entirely different outcome. Previously, website live chats were given fixed responses that were triggered by certain words or phrases. When they didn’t have an answer to a question, they would redirect to a human.
Adding AI into the mix gives the chatbot a level of independence. Rather than fixed response answers, an AI chatbot will refer to its training data to get accurate information and synthesise a response. The result is a conversational chatbot experience that can provide the same accelerator effect that consumers have been enjoying with ChatGPT for the last 4 years.
With low risk ‘retrieval augmented generation’ (RAG) models, Appius have found that it’s very easy to deploy a conversational chatbot that is fed with the right pages from a website or documents, without major technical integration requirements.
Technologies like Google Conversational AI or Microsoft Copilot can be deployed via Google Tag Manager for a public website solution, or inside an intranet or secure members area.
Unsure whether an AI chatbot will work for you?
One US-based insurance company, Lemonade, launched an AI chatbot that now sells 90% of all policies. Their AI chatbot team (Maya and Jim) helped them reach one million paying customers faster than Netflix, Spotify and Amazon. And they did it up to 10X faster than most formidable insurers in the US.
The benefits of implementing an AI chatbot are obvious. Accidents can happen any time and, once the agent is built and trained on your data, customers have 24/7 access support.
AI is also far superior at identifying cross-sell opportunities, using real audience insight to find potential upsells.
Pricing and Product Innovation
Dynamic pricing has changed the way the insurance industry will work forever. Keeping pricing in line with market standards, whilst adapting to in-demand or niche product needs, requires a detailed understanding of the current market conditions.
AI makes this easy, enabling more dynamic and granular pricing models for the quote and buy engines.
R: Optimising Retention Journeys with AI
Acquiring a customer is one thing, but for insurance organisations the goal is to make sure they stay when it’s time to renew their policy.
Email Marketing
Marketing automation has come a long way since AI was integrated into the tools. Previously, audience insights were good guesses with signals such as ‘last order date’ or ‘average order value’ used to predict customer lifetime value.
Today, with the help of AI, marketing automation has become essential to streamlining customer journeys. Rather than actual values and estimations, AI looks at user behaviour signals and how people with shared characteristics usually act when they’re on a site.
The algorithm analyses this data to predict the message most likely to resonate, whether the customer is still investigating product options, looking to change policy or is showing a high churn risk.
As Dotdigital partners working with clients such as Teachers Building Society and CIPHE, we have used a variety of AI tools to improve efficiencies. By predicting churn risk, reinterpreting subject lines, optimising send times and using findings to optimise landing page experiences, we have improved open rates, click rates and conversion rates from the email marketing channel. User clicks on emails have been used to deliver individualised email 'programs' of value content and suggested next steps to deliver value for our clients.
Chatbots
Aviva have been using an AI chatbot to retain customers since May 2021. After an initial roll-out and test phase for their Italian customers, the feature is now available customers all over the world.
Built with ChatGPT, Aviva’s AI chatbot (Vivy) is driving the quote and buy function in a friendly tone. It can help with queries, provide product information, and can generate home insurance quotes all within the chat interaction. In June 2026, Aviva became the first insurance company to offer initial life insurance quotes through their ChatGPT channel.
Today, Vivy handles up to 80% of customer requests without human intervention. It has reduced the call centre workload and is able to provide faster responses. Trained on Aviva’s own training data, Vivy can provide the latest information and resources around the clock, making it the quickest route to a solution.
Summary
In summary, AI is most effective in insurance when it:
- Speeds up decisions
- Improves risk accuracy
- Reduces fraud and cost
- Enhances customer experience
- Optimises conversions
For insurance organisations, these benefits are the difference between being part of the future or staying in the past. The insurance industry is shifting from reactive (claims-paying) to proactive (risk-preventing), with AI at the core of the shift.
Claims can be approved in seconds, with chatbots providing 24/7 support for all aspects of customer engagement. AI agents can get on with the grunt work, meaning insurers can offer more for less.
What we’re excited about is monitoring how consumer behaviour changes overtime. With AI optimised marketing automation streams, churn risk analysis and personalisation based on behavioural patterns, will the journey become so hyper-personalised that customers stay loyal? Or, will they become more likely to jump from company to company as competitive pricing strategies become more dynamic?
Appius are explaining to organisations that 3rd party LLMs and software add-ons are great for operational efficiency, but proprietary AI with a brands own data is the way to make the organisation more efficient.
It is the company that can talk to a customer differently across each stage of an annual purchasing lifecycle aligned to their renewal date, that will maximise the brand relationship and retention opportunities. Real data scientists are needed in internal teams or agency support, to achieve this transformation in a safe and governed way in line with Consumer Duty.
And if the race to acquire a customer is up ended by reducing the need to constantly look for better deals, then what effect will this have on the ags.
I am interested to hear your thoughts on this subject if you have them or any anecdotal evidence of how the AI revolution is changing consumer behaviour.
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