The challenge isn't about how to collect more data, but how to unlock the potential of what’s already there to power sales. In this article, we explore how user-driven sales applications can transform existing data into useful, actionable sales insights that can empower producers to sell better.
Insurance sales teams are no strangers to data. Billions of data points on customer needs, behaviors, demographics, and more already exist within insurance organizations. Sales data, like the number of deals closed per person, call activity, and cross-selling rates, are also easily collected and shared thanks to digital and cloud technologies.
However, the ability to gather, manage, and store data is not enough. Sales teams are awash with data but starved for insights. Spreadsheets with endless rows and columns of figures just provide a baffling amount of data and no clear path forward. Sales teams need to know what they should do next without spending all their time analyzing endless reports to try and derive sales strategies.
What additional insurance coverage is this customer most likely to need? Who are our most valuable customers, and which ones are most at risk of attrition? Am I talking to my customers about the products that are most likely to interest them? When is the best time to call a customer? These are just some of the questions, critical to sales outcomes that data analysis must answer.
In this first blog of our two-part series, we discuss common barriers preventing insurance organizations from moving beyond collecting, storing, and managing data. We explore how sales teams can use data more effectively and turn data into valuable insights. In the second part, we will address how a personalized approach to producer experiences can improve productivity and optimize sales resources.
Data enablement and access to the right data
Data silos are a defining feature of large insurance organizations. These disconnected data islands persist for several reasons, including the high cost of dismantling them and the advantages they afford in data security, privacy, and compliance with regulations. The downsides of data silos are that they lead to incomplete data sets and hamper information sharing. Critically, data silos in insurance organizations make it harder for sales teams to conduct meaningful data analysis.
Fortunately, there are ways to address this problem. Simon-Kucher's data teams have used data-lake and lake-house approaches to connect vertical storehouses of data in financial services organizations without compromising security, accountability, or compliance.
It is important to note that the ability to make accurate sales recommendations depends less on having access to a large amount of data and more on having access to the right kind of data. With the right data, model architecture, evaluation, and validation processes, advanced analytics and AI engines can be trained, fine-tuned, and optimized to make accurate recommendations for insurance sales.
Avoiding zombie dashboards
Insurance companies must take a user-centric approach in the design and development of sales analytics dashboards. Presenting outdated, unreliable, too much, or too little information can be counterproductive, as this will confuse sales teams. As users disengage, insurers are left with abandoned or "zombie" dashboards. Sales teams should be focusing their time on bringing in sales, not analyzing data.
To ensure usability and engagement, information must be presented in a way that resonates with core human drivers like accomplishment, ownership, social influence, and meaning. For example, one large insurance company introduced progress bars on its sales platform to let users track their daily and weekly performance. The platform also displayed how far away each user was from their next monetary incentive, along with recommendations on how to get there. Users subsequently started logging in more often, as they were motivated to see how much in sales they made that day and what they can do to reach the next incentive. These small, personalized additions were enough to improve engagement on the digital sales tool.
Training and improving applications
Digital tools and software applications must be dynamic and continuously adapt to sustain user engagement. Sales acceleration applications should incorporate high-functioning features to support continuous improvement, including tracking the average time spent on the tool, scrolls, clicks, and viewing behaviors. A/B tests can also be used to help insurance organizations identify ways to optimize the user experience and the output from insurance providers.
The business of insurance sales
Finally, the process of translating data into actional insights must align with the nuanced nature of insurance sales. Insurance products are complicated, and customers have unique needs due to a variety of factors, including their financial situation, risk exposure, and life stage.
Sales analytics must account for these and other challenges to make meaningful sales recommendations. For example, if a provider wants to optimize its product recommendations, data analysis might reveal that they will have a better chance of selling home insurance to someone in Chicago, where home ownership is high. Similarly, the model can predict that a provider in New York will have a better chance of upselling renters’ insurance.
While these recommendations may seem obvious, most insurance dashboards do not offer suggestions like these, even at this high level. Instead, users must figure this out themselves from existing reports or rely on anecdotal information about their market.
A strong grasp of what drives successful insurance sales is important to guide and define data analysis, transformation, and interpretation. Luckily, insurance providers already have all the data necessary to gain these insights. Often, all they need is help to make sense out of it.