Insurance Growth Engine (IGE) has been saved
Insurance Growth Engine (IGE)
Customer interactions in all channels will be spot-on and deliver the best results
The Insurance Growth Engine (IGE) embeds customer analytics in your daily operations to achieve profitable growth.
The rules of the game have changed and the effectiveness of traditional business models in driving sustainable profitability have been seriously reduced. Current focus on volume, premium and products is not sufficient anymore and should be shifted towards driving and managing individual customer lifetime value. Most insurers are struggling to cope with empowered customers that switch more easily. They want to interact 24/7 via their channel of choice and tailored to their personal needs
These trends lead to a huge increase of customer interactions which are fragmented via various channels. The biggest challenge is the desire of customers to be recognized at every touch point and only willing to engage in relevant dialogues and offerings.
So how can insurers with millions of customer contacts per year through various channels be spot-on at every interaction?
Besides the challenging market conditions, there is an explosion of available customer data. Increasing maturity of modelling and decisioning technologies makes it possible to mine this 'big' data and improve business decisions. Granular contextual customer data can be confronted with selected treatments, resulting in personalized offerings. The decisioning technologies supporting this process are self-learning and will improve daily performance, even in unassisted channels.
Pillar 1: Integrated analytics
The IGE is a real time decision engine that facilitates relevant and personalized dialogues between a customer and an Insurer, in every channel, inbound or outbound. The dialogue is based on customer profiles, contextual information, predefined treatments and validated propensity models. All analytical models used should be preferable calibrated and updated on a regular basis, especially the propensity models.
Pillar 2: Customer interaction
Next Best Dialogue
The IGE selects the best personalized treatments based on real-time contact reasons and the customer context. Proposed treatments can be a retention message, cross- and upsell, service message, educate/prevention, data capture and many more. This offering also depends in which phase the customer is in the customer life cycle. After the pre-selection of the best treatments by the IGE, a single next best treatment has to be chosen. This offering improves the customer profitability and leads to satisfied customers because they get a tailored service and offering.
The IGE is self-learning; it learns from every acceptation of rejection of customers (segments) and calibrates the selected offerings for certain customers in certain contexts automatically.
If a customer makes contact with your company, the IGE directly recognizes the individual customer profile and what kind of customer this is, and more important if it's a profitable customer. The behavior of the customer will be also predicted, based on data analytics. This leads to personalized offerings in all channels, e.g. IVR, call center, web, mobile and agent/broker.
The treatments are integrated seamlessly in all channels. This means that if a customer refuses an offering in a certain channel it won't be offered again in another channel; the IGE learns real-time from previous decisions from customers (segments).
Pillar 3: Performance measured on daily basis
The IGE provides KPI-reports such as channel performance, scenario analysis and profitability projection. The daily performance on sales and customer satisfaction can be easily measured by comparison between performance and reference teams. The results are significant higher in terms of value and customer satisfaction. Regular calibration ensures distinctive performance.
The IGE provides daily insights for improvement of treatments and products/services. Marketing or product development can take a daily peek under the hood of the engine and analyze what variable is relevant for what segment and what isn't. By knowing this, one can adjust treatments or products/services in a way that fits better with customer's needs.