It’s why an analytics-driven approach is so essential. It enables insurers to pinpoint high-value customers across the customer journey - and tailor marketing and customer service interactions with them for optimal results: increased profit and improved retention.
Analytics-driven intelligence, powered by machine learning, allows relevant offers to be tailored to every customer. Not just at the right time, but through the right channel, at the right price. Fuelling new products and services, helping develop a deeper understanding of customers, and driving revenue from smarter upselling and cross-selling.
If you’d like to see – not hear – how we’re helping insurers embed machine learning across the value chain, then email email@example.com to find out about our pop-up workshop experience!
Discover how to navigate through the five stages of the insurance customer journey, using applied machine learning to achieve greater success.
Machine-learning delivers breakthrough value from data across the customer journey.
Identify high-value prospects and boost conversion rates by personalising web-quote journeys.
Modern consumers like to shop around. And they expect the same seamless purchasing journeys from insurers that they experience elsewhere. To deliver, firms must use their acquisition data to identify high-value customers and tailor individual quote journeys to their needs.
Increase customers’ average product holdings by predicting their insurance needs and marketing preferences.
Predictive analytics helps identify the products and services customers want. But it can’t show which marketing actions will increase product holdings. By moving up the maturity curve to prescriptive analytics, insurers can discover which channel, approach and/or incentive will help grow ancillary revenues.
Harness the power of every customer interaction to predict wants and needs along the policy journey.
Machine learning helps insurers empower their call-centres and add intelligence across other channels. This improves customer experiences and enables more relevant products and services to be targeted across the policy lifecycle.
Use predictive analytics to identify opportunities for reducing claims losses and increasing automation.
As the ‘shop window’ of insurance, claims handling brings opportunities for enhancing insurers’ brands. For example, by using predictive analytics to bundle services – and integrating these insights into the existing supplier network – insurers can cut costs, reduce handling times and deliver seamless claims journeys.
Use AI to understand how high-value customers will react to different pre-renewal strategies.
By experimenting with price changes, outbound contact methods, approaches and incentives, insurers can use AI to decide which pre-renewal strategy to apply to individual customers to increase the chance of renewal.
Analytics Practice Lead
Max leads Accenture's Insurance Analytics practice across Europe, bringing over 18 years' experience in Insurance business and technology transformation including implementing advance analytics on both local and global/group level. His primary focus today is helping Insurers deal with the impact of disruptive trends and technologies as well as maximizing the value from advance analytics.
Lab Stream Lead
Steve Watson has been with Accenture for over 12 years. During his career with Accenture, he has worked across Insurance as well as Banking in core SI Delivery, Advisory and Sales related roles.
Senior Managing Director – Insurance Lead for Europe,
Africa and Latin America
Daniele Presutti is a Senior Managing Director for Accenture, the Insurance Lead for Europe, Africa and Latin America, the Global Life Insurance Lead, and the Client Service Group Lead for Insurance in Italy, Central Europe and Greece. He also has extensive experience in developing and implementing transformational business and IT strategies. Daniele joined Accenture in 1987.