"These models tend to be less parsimonious and have a lot of configuration overhead and sensitivity to data provenance", Lin explained. "The former means we have to keep careful versioning of our model configurations and parameters so our experiments are repeatable and cleanly organised. The latter means we need to make sure we track the history of our data carefully as it goes through iterations of cleaning, scrubbing, pre-processing, and so on. This would be very time-consuming for all firms, even firms with hundreds or thousands of employees, because it's not a problem that's linearly solvable with increasing number of people you throw at it. It requires careful design, planning, foresight and some good luck in making the right architectural choices."
Machine Learning can enhance current risk based pricing and margin models, offering quicker speed of service alongside greater accuracy. Additionally, it can be used to improve performance in existing quote conversion processes by accounting for multiple factors in the customer journey, including other offers and the multitude of insurance a customer holds. This data is valuable for insurance companies to profile their customer and offer terms suitable to their requirements and circumstances. Machine Learning includes further capabilities to analyse structured and unstructured data, allowing insight into the likelihood of purchase of a policy based on these multitude of factors. Through these criteria, insurers can guarantee that more focus is given to substantial positive leads.
Advanced measurement opportunities have increased marketing visibility like never before. Innovative technologies including AI, Machine Learning, and algorithmic solutions have propelled the marketing world forward, whether in the form of custom packaging or out of the box platform offerings. Knowledge of ML has been growing over recent years, but now we finally have both the scale of data and the ability to put it into use. With developing technologies from companies like Amazon, Google and Microsoft it's never been easier to leverage. The hard part now is determining what to dive into first!
Since last fall, marketing platform Kahuna has been expanding its targeted channels beyond mobile, to include pop-up messaging on web sites and greater capabilities for email and in-app messaging. Now, the Palo Alto, California-based company is expanding again to focus on customer journeys instead of individual messaging campaigns, with the recent launch of Experiences for optimizing messages across a journey. This newest incarnation of its marketing platform, senior vice president for product Mihir Nanavati told me, allows the firm's RevIQ machine learning engine to be applied across all the paths taken by a customer toward the marketer's goal. Previously, he said, the platform focused on a marketer's ability to, say, send a email to a mobile user encouraging them to install a new travel app. Once installed, there might be an in-app message suggesting that the user search for airfare deals.
Customer Experience (CX) is a top priority focal point in data-driven digital business transformation. Customer-centricity is not new, of course. However, in the modern digital business context, the conversation around customer-centricity focuses on steps to measure CX, to optimize CX, and to apply design thinking around CX across the full customer journey. This focus on experience management (measurement, optimization, and design thinking) has broader application in UX (User Experience) and DX (Digital Experience). In addition, we see experience journey management being discussed and applied in other domains, such as Healthcare (Patient Experience) and Human Resources and Human Capital Management (EX: Employee Experience).