Data Livens Up Financial Relationships at Old Mutual
Old Mutual uses data strategy to break away from life insurance’s “buy and die” relationship reputation and deliver significant ROI.
The company has made "putting customers at the centre of everything we do" one of its top five strategic priorities to help drive growth, according to its website. As general manager of customer engagement for the Old Mutual life insurance brand, David O'Brien oversees the firm's data warehouse, CRM, and digital domains. For him, customer experience and data strategy are essential to meet this corporate growth objective. He shares his insights about data successes and challenges with Customer Strategist.
Customer Strategist: Why is customer engagement so important to driving business value for Old Mutual, and what do you do to drive engagement?
David O'Brien: Customer engagement is crucial to creating value. Life insurance is a low-touch product—at its worst, "buy and die" are the only two potential customer interactions. So our team proactively engages with customers to generate an ongoing conversation. Once we have created a conversation, we believe that we can then build and deepen relationships that ultimately transcend the default transactional relationship.
For example, we are able to segment customers according to loyalty and value, and we have a program of appropriate engagement activities ranging from birthday text messages to personal calls from senior executives. The greetings are segmented to match the cost to the customer lifetime value. We strive to touch our customers in some format a minimum of four times per year.
CS: How does data play a role in the success of your department, and Old Mutual at large?
DOB: Data is our currency. Missing or poor data means no contact, which means we don't get past first base with customers. Incorrect data means we can upset the very customer we are trying to engage. Neither is desirable.
However, my team has to continually remind our organization of the value of data, and the cost of poor data quality. Data quality is often the first casualty in operational efficiency, and we operate extended value chains in some of our processes. We therefore have programs to acquire and improve data quality, operationally and centrally. Our service colleagues have responded well to specific processes to improve the data, but it is an ongoing journey.
CS: Is Big Data an executive-level issue at Old Mutual?
DOB: It is currently a C-suite issue due to internal inertia. The expansion of my role is intended to address that. Big Data requires significant information technology investment, and the wider organization remains skeptical about the business case for our market, at this stage. I am hoping that through engagement with the CEO, colleagues, and internal stakeholders I can build a community of advocates.
CS: How does your team build internal buy-in for customer centricity and data strategy?
DOB: My team and I work very hard to ensure that we are seen as a shared service to our customer-facing units. We have a formal process of engaging internally as the relationship matures. We first attempt to understand the key business levers of our internal business units. We then match our services to those levers to get to the use case as quickly as possible. We formally contract with each client business unit, and as we have no revenue targets, their success is our success—we are completely aligned. Unfortunately the initial phase of landing the data sources, and then improving the data quality, takes time and is not immediately value adding. We need to maintain the engagement and focus on the case to maintain the momentum.
We originally grew from within one business unit, and we are now missionaries tasked with sharing the customer joy wider in the group. Customer centricity is a key strategic goal of the Old Mutual group. And data analytics is the only practical way to manage large-scale relationships.
CS: You have a background as an actuary, yet you are in charge of a "soft skills" department. How does your analytical background help you deliver value to customers and the company?
DOB: A computer can never create warmth in a human relationship. However, creating and managing 4 million ongoing relationships is a human impossibility. You therefore need investment and C-suite support. My actuarial training has assisted me in identifying the levers of value creation from the many activities that my creative team generates. One of our initial activities was designed to influence persistency, for example. I focused on this, given the significant financial impact it has on an insurance business' reserves and revenue.
This value attribution has in turn allowed us to build investment cases that have helped us acquire the resources to continue our journey. We are a continual rolling experiment. Once we prove the value of an activity, we industrialize it. If there is no value, then we close it and move on to the next activity.
CS: What data-driven initiatives do you manage?
DOB: We recently introduced Net Promoter Score to the group, with adaptations for our specific market needs. That database has been instrumental in improving the customer experience and reducing cost to serve. In addition, we are still investing and building our expanded warehouse. We have been able to acquire external and internal data sets that assist us in gaining a better understanding of our customers and our intermediaries. We then build analytical models to predict the value in a particular area. Everything we do is a data-driven initiative.
CS: What results have you seen from your data-driven programs?
DOB: We have been able to quintuple our rate of leads closure by improving the quality of the leads distributed, and also by analyzing the process value chain from a sale through the closed loop system. We have also significantly improved our customer retention by understanding the drivers of attrition and responding with focused interventions from my team and our customer-facing colleagues.
CS: How do you plan to expand data-driven programs that drive real value?
DOB: We create the investment case for the initial platform, and then build from there through iterative cycles of demonstrable value creation for my business unit partners. As per the point above, the current important levers tend to be business-unit specific. Some examples include retention through term and at maturity, retirement fund preservation, Net Promoter Score, leads closure, data quality, cross-sells, and up-sells. The absence of any real progress is an opportunity to start building from scratch, subject to resources and the business unit priorities. My area plays an important role sharing best practices within our South African businesses, but also increasingly from our colleagues operating in other African territories.
CS: What advice do you have for other executives about infusing data strategy into the customer experience?
DOB: Test and learn. Keep an eye on the value. Your data will never be perfect. It is better to get value from imperfect data than to strive for impossible perfection. Engaging with customers and the data is the best way to establish data quality issues and prioritize what needs addressing.