Are there clear winners of the race to great customer experience among leading insurance companies? According to a recent Forrester study, probably not. In this article we explore ways for insurers to step out of the middle ground of lukewarm CX and increase customer lifetime value through advanced customer analytics models.
The European Customer Experience according to Forrester’s CX Index
According to the most recent Forrester CX Index study, presented at Forrester CX EMEA 2020, the margins for customer experience between different brands in the insurance sector are incredibly narrow. In the UK in the past five years, the difference between the best and worst performing companies is a mere nine points. Results were similar for all four European markets, presented during the talk – UK, Spain, Italy and France.
Ease and effectiveness were found to have significantly more importance to customers than emotion when looking at key factors that drive a good insurance user experience. What exactly creates a loyal customer then?
Research found that on the continent people are mostly motivated by a responsive insurance agent, while UK customers value good educational communication and competitive premiums.
While insurers do a better job at making their experience an emotionally gratifying one than banks, for example, it is still largely a mediocre and indistinguishable CX experience for customers. It is interesting to note, that while we might assume the experiences that are human to human far batter in emotion, the opposite is actually true. Chat, mobile and website were found to provide the best customer service experience.
Insurers might already know what their top drivers of CX are, however 2021 will bring an entirely new set of statistics as a consequence of the COVID-19 pandemic. As a result, insurers might find it difficult to improve their experience. Of course, the low touch point engagement model they operate combined with the channel complexity and price sensitivity would make it even more difficult to improve that experience.
There might not be much that insurers can do to improve their channel complexity. With agents, banks and others in the middle, you simply don’t have control over the entire experience. Ensuring your product remains competitively priced is another reason insurers might not have the means to invest in CX or they haven’t delved into it enough to receive a lot of the benefits.
How to improve customer experience in insurance
- Educating their agents
- Investing in productivity tools
- CRM tools that improve communication
Demonstrating product innovation and advocating for customers was another tactic suggested by Forrester’s research team. Especially in the UK, customers are increasingly not feeling respected by their insurers. Agents are selling them cheap packages and then raising the price after a few years, which leads to mistrust and low CX ratings. While the FCA is working towards outlawing some of these particles now would be the perfect time for brands to ban them themselves and step up for their clients.
A key question when reviewing this information would regard the efficiency of optimising CX. Is that process going to actually gain us money or just make a dip in our budget? It is even possible you have already done some CX research and you haven’t seen the full benefits because you haven’t been evaluating your feedback.
A great example for that is the case with Nokia. They were a market leader until the appearance of the iPhone and Android phones quickly taking over. The device share of Nokia on the mobile phone market continued to decline until they eventually sold their business in 2013. What never changed in the years before that sale were their CX benchmarks. Their customer satisfaction ratings remained at a healthy 70%.
While that is an extreme example, any company might be in a situation where they are improving CX benchmarks, not the actual quality of feedback from their customers in terms of revenue growth. That is because Beacon metrics, such as NPS®, don’t answer the question ‘are our efforts to provide the best customer service experience contributing to revenue or are they just drying up our capital”.
Focusing on Devotees as a key driver of revenue
Whose good experience leads to healthy business? Forrester analysts introduced the concept of “Devotees”: a core group of customers who fit what you do and whose buy-in translates into value for the business and also whose relationship is attractive to other customers.
Every business has such a group, even insurers.
WHY ARE DEVOTEES IMPORTANT FOR YOUR BUSINESS
- They are willing to forgive you
- They are willing to pay a premium to do business with you
- They will keep their business with you.
How to recognise them?
- They have much higher NPS score than the average
- They have much higher revenue per customer
HOW TO QUANTIFY HOW MUCH DEVOTEES ARE WORTH TO US?
Identify who are your most valuable customers with Dynamic Customer Lifetime value.
While for a lot of the sectors customer lifetime value (CLV) is relatively easy to calculate, this is not the case for insurance. An insurance lifetime value model needs to incorporate variables which are far outside the scope of standard calculation, such as claims paid, costs of reinsurance, contingent commissions, various risk & pricing factors, etc.
Adopting а data-rich approach will create a link between CX and Underwriting. Having the most important risk rating factors embedded in the calculation of CLV will guarantee that you will focus your CX efforts on those customers, which are better aligned with the underwriting strategy for each product line.
Another aspect that needs to be included in the CLV calculation methodology would be the potential of the customer to buy additional products or the likelihood to recommend you to family or friends. Analytics models can help you answer these questions.
Last, standard models are static – they calculate customer lifetime value against a fixed point in future – usually 6 to 24 months. Customer behaviour is much more dynamic, and the environment. For example, if you thought you knew the expected revenue per user in the beginning of 2020 when the COVID-19 pandemic was rolling through Europe, your calculations became obsolete in a matter of weeks.
Using data analytics to develop a CLV model can reflect “shock events“ and recalculate the CLV real-time will give companies the opportunity to take actions to maintain or increase the lifetime value of affected customers. For example, in insurance, this could be an increase of reinsurance costs, or external events like COVID-19 or other important variable in the dynamic CLV model event. You need an algorithm that will give you a dynamic CLV updated daily to make better decisions faster.
Knowing the satisfaction score of each one customer with the highest value is fundamental to address their specific needs. Customers with similar NPS scores – e.g. two Detractors – may have different drivers of satisfaction/dissatisfaction.
Furthermore, they may have different lifetime values. Linking NPS scores with CLV will give you a very important knowledge – what will be the financial impact if you uplift a customer from one NPS group into another – for example passives into promoters. Using data analytics and developing a simulation tool will help you evaluate the financial impact of any CX program or remedy action you are planning to implement. Being able to focus on the actions with the highest ROI will help you build a self-financing CX strategy.
A deeper look into customers’ needs
When understanding how to improve their customer service experience, many companies’ go to is an open-ended survey. While helpful, those answers won’t do much more than aide to identify which topics contribute to a positive NPS score and which don’t.
Instead of reconfirming already known truths, consider undertaking a Root Cause Analysis – a 2-step model that allows CX and Insights Managers to uncover the deep, underlying reasons for customer satisfaction or complaints.
STEP 1: MAIN DRIVERS OF NPS
First, we examine the link between NPS and the topics mentioned by customers in their open-ended survey answers in order to estimate the contribution of each topic to monthly NPS. We use a multiple regression model at respondent level (i.e. each observation corresponds to an individual survey response). The dependent variable is the NPS and the explanatory variables are binary variables indicating the mentions for each topic with sentiment.
The contribution of each topic to NPS for a given month is obtained by multiplying the estimated regression coefficient with the share of mentions for that month (i.e. by fitting the estimated regression equation with the average values for the month). Separate models can be estimated based on any type of metadata attached to the data set (Segments, Product Lines etc.).
STEP 2: DRIVERS OF DRIVERS ANALYSIS
Analysis is based on partial correlations which indicate the correlation between two variables eliminating the influence of any confounding factors. Results from this model are represented in a tree-like graph where NPS is at the root of the tree and the topics are at the nodes. The closer a topic is to the root, the stronger the correlation with NPS. The thicker the edges, the stronger the correlation between the topics and NPS or the topics themselves.
The Drivers of Drivers Analysis helps our customers go even more deeper in understanding not only how drivers impact NPS, but also how drivers affect each other, in order to better focus CX improvement efforts.
For example, when examining the key drivers of NPS we may find that “reason for claim refusal” is a key element which drives satisfaction down. Such a finding doesn’t tell us much how to improve this critical touchpoint.
On a second look, however, we find a strong correlation between “agent expertise” and “reason for refusal”. And just like that, we have a clear action mapped in front of us. If we improve the knowledge of the agents, so they can explain the reasons for claim refusal in a clear and understandable way, customer won’t feel so let down and NPS scores will improve.