Data Storytelling and Visualisation: A Practical Approach
The insurance industry often has a reputation as boring and fear-inducing. However, many companies have learned that new technology can help them tell their stories more creatively and become more customer friendly, which ultimately becomes a win-win for everyone.
With the number of policies increasing every year, there is an increasing demand for communicating with customers clearly, and engagingly. Generally, insurance companies have been looking for ways to transfer data from their systems to consumers' screens.
As a futurist, I believe that along with storytelling, data visualisation is emerging as an effective form of communication that helps businesses connect with their customers. Here is what you need to know about combining storytelling with data visualisation in the insurance industry.
The Value of Analytics to Overcome Data Challenges
Today's insurance industry is complex, with various products, premiums, and risks. Consumers' wants and needs also change, which is why businesses in the industry need to be able to make data-driven decisions that are in the best interest of shareholders.
An insurance agency's primary job is to maximise profits for its clients by providing adequate yet affordable coverage. However, a business's bottom line is directly determined by its ability to analyse risk and provide the appropriate level of coverage for each individual. A comprehensive data visualisation solution powered by AI and machine learning tools can help business owners do this, allowing them to make informed decisions based on data collected from clients, risks, and claims. This ensures that companies don't over- or under-insure themselves or their clients.
For example, CCC Intelligent Solutions provides cloud-based software and data services that help insurers, automotive manufacturers, collision repairers, and other businesses run their operations more efficiently. The company's AI-enabled platform transforms the insurance claims process by digitising and automating it for improved customer experience. In their case, the company uses AI to automatically analyse photos taken at accident scenes and verify policies.
With this data, CCC's AI can assess damages and provide insurers with timely estimates for approval.
Big data is an increasingly important resource for organisations of all types and sizes – and one that requires a well-planned, careful approach to ensure business value. As a result, a growing number of analytics applications are available on the market that helps companies cope with the explosion in data volume and speeds. If we take the insurance industry as a reference, this PwC report explains that many of the companies in this industry face the following challenges:
Deficiencies in the service
The insurance ecosystem has seen a lot of change in recent years, allowing for a new way of doing services. For example, the old supply chain model was often inter-branch, and communication relied on email and phone, leading to delays in service.
Many of these organisations still rely on outdated technological means that no longer respond efficiently to current needs. However, thanks to significant technological advances in recent years, it is possible to find much more efficient, fast, and sophisticated solutions that adapt to any challenge, including offering a quicker and more accessible service to users. In short, with digital tools, insurers can now collaborate with customers to provide faster access to services and products.
Organisations, in general, are challenged today to build the right technology foundation and scale their analytics capabilities across business units to derive the right data-driven insights. A successful analytics program requires a strong data foundation.
Nowadays, an effective business transformation includes metrics that are constantly evolving. From descriptive to predictive, from customer-focused to customer-centric, from simple and rapid analysis to sophisticated and automated, from local experience to the global ecosystem, from tactical impact to strategic focus, and from outdated technologies to digital ecosystems. The goal is to understand these metrics as the integration of people, processes, technology, and data to deliver value to customers.
Utilising data for practical purposes
In nearly every market, insurers are experiencing high premiums for their models and projections, and their prices continue to increase. Therefore, it is necessary to have a practical analysis and communication strategy that allows you to transmit your Unique Selling Proposition (USP) through data that supports the value of your products and services.
Many organisations have realised how technology can help insurance companies improve underwriting decisions. The focus has been on using big data, machine learning, and predictive data analytics for insurance companies to make more intelligent risk selections, win more business, and save money.
By harnessing the power of modern technology, insurance companies are reaping all the benefits of better underwriting methods, like being more efficient and reducing the risk of human error. Fortunately, there is a wide variety of tools to avoid these issues. Data experts can take all these data sources and turn them into a powerful, personalised solution that manages risk, monitors compliance, and many more issues within any organisation.
The goal is to ensure enough information is available with the proper timing to support proactive decision-making before claims arise. Pressing issues at today’s businesses include improving performance across all lines of business (LOBs), creating greater sharing of analytics and insights between functions, and delivering a better customer experience.
Underwriting can be inefficient in many organisations
Underwriters will always be looking for ways to improve their process, whether by automating more manual steps or enhancing the flow of information between departments.
In many companies, underwriting was a long, painful process filled with manual work and inefficient communication across departments. Ideally, for companies where underwriting processes are carried out, they should be done in minutes, thanks to automation and better collaboration between departments.
The difference between these two scenarios is not necessarily automation itself—it is how automation is leveraged within an organisation.
When carriers pay attention to underwriting efficiency, they want to speed up the work. As we move beyond paper, this is the logical next step. The quote-to-bind process (converting prospects into customers) can be faster by shifting responsibility to the next person.
As you accelerate an inefficient process, you reinforce it further and exacerbate downstream inefficiencies. Undoubtedly, many underwriting processes remain relatively ineffective due to a lack of effective automation, excessive manual effort, and a lack of coordination across systems.
It is still common for underwriters to toggle between dozens of different systems to reference guidelines and find other information. Internal and external communication flows can be convoluted. Data re-entry during manual submissions can introduce new errors that need to be reconciled.
Underwriters should have access to all relevant data as they work through a quote or policy bind request—including relevant documents such as driver information, vehicle information, and inspection results from third parties like TLDs (third-party loss data companies).
Underwriters need intelligent workflows that collaborate efficiently with other business functions within the carrier organisation, such as risk or reinsurance specialists or policy issuance teams. They also need access to real-time data from across the enterprise—not just from their carrier but from competitors—so they can make informed decisions about whether a risk should be accepted, rejected, or placed on hold for further analysis.
Why is Data Storytelling Important?
Data storytelling and visualisations are an essential part of insurance. However, it is more than just telling a story with numbers—it is about influencing compellingly with the correct data to create a connection and commitment with your audience and potential customers.
Stories are powerful because they capture attention, evoke emotion, and motivate action. Data storytelling can accomplish all three objectives by framing information in a narrative format, making it easier for people to understand and feel related.
Data storytelling is about communicating complex data in a way that is easy to understand. Data visualisations are representations of data, typically in graphs or charts. They help people see trends and patterns in data that they might not be able to see otherwise.
As we have discussed, the insurance industry has been slow in embracing data storytelling. There are many reasons for this, but the main reason is that insurers tend to be very conservative and risk-averse. This is one of the main reasons they must adopt the latest trends in technology as part of their operations if they wish to grow and reach new markets.
This conservative attitude is changing fast, mainly because the best companies and organisations know they must change and adopt new paradigms to improve. As a result, there is a lot of buzz around data visualisation and storytelling in insurance. This article from Harvard Business Review emphasises that data visualisation is not just a technical process but an essential component of any industry operations in recent years. Companies use it to explore large amounts of data to make better business decisions.
Data storytelling is a relatively new concept in the field of data communication. It aims at bridging the gap between numbers, facts, and figures by using them as part of an interactive story that can be easily understood by anyone who wants to learn about a specific topic.
It is not only about communicating information but also about making it enjoyable for people to consume it. This can be done through visualisation resources such as graphs, charts, maps, or infographics that convey complex information in an easy-to-understand format.
In summary, data storytelling presents data to comprehend it in a narrative that drives action. While traditional data visualisation focuses on presentation and providing an overview, data storytelling focuses on the story. It tells a specific story that can be explained with charts and graphs, but it also goes deeper into the story to have meaning.
Data visualisation is also a way to tell data stories through images but uses different techniques and principles than those used in data storytelling. The two methods can be blended for a more immersive experience that gets results.
The Benefits of Data storytelling
Data storytelling offers a variety of benefits that can help your organisation:
It increases engagement
Data storytelling can help you engage your audience and make the information memorable. A good story will keep people's attention, which is essential for learning.
It helps people understand complex concepts
If you have a complex concept or idea that you want to share with people, it is essential to explain it in a way that's easy to understand. Data storytelling can help you break down complicated concepts into smaller pieces that are easier to understand. This makes it easier for people to understand your message and remember what they have learned.
It creates trust with your audience
When you create a narrative backed by quantitative data, you can convey stories of realities we face daily. It is about conveying this data in a way that connects with the audience in a meaningful way and not just leaving it as a mere number or statistic that does not produce any emotional reaction.
Translating that data into actual situations and solutions for your potential customers and stakeholders is best.
For example, there are currently 10 million stateless people worldwide, according to the United Nations High Commissioner for Refugees (UNHCR). If we transmit this information in this way, many people understand the problem but do not know what lies behind this reality.
To this end, UNHCR created a global campaign called IBelong to share the real stories of families and individuals living in these circumstances worldwide. In this campaign, they describe people with names, and ages, who are people like you or me but face a series of challenges that make them live in very disadvantaged conditions daily. They put a face to the issue of statelessness, and by 2018 the campaign had generated important advances, such as 166,000 people had obtained a nationality legally or adding 20 States to the signing of international Statelessness Conventions.
It allows you to persuade or influence others
If you want people to do something with your data, you need a strong narrative that makes sense and resonates with them. If they understand how their lives will be impacted, they’ll be more likely to take action on what they learn from you.
The Benefits of Data Visualisation
On the other hand, data visualisation also has several specific benefits that are important to mention:
It is faster to process data that is visualised
The human brain processes image 60,000 times faster than text, which means it can interpret data much more quickly when presented visually. This is especially important when giving large amounts of data or complex concepts with many variables at once—as is often the case when offering financial metrics or other business data sets. By using charts and graphs instead of tables and lists, you can communicate important information more quickly and effectively than simply listing out numbers on paper without context.
It is easier to take action when data is visualised
Data visualisation can be used to show the relationship between different data sets. It allows you to see if they are related and, if so, how they are related. In this way, it helps you understand the connection between various factors and events that might otherwise be difficult to grasp. As a result, it helps companies see what is going on in their business processes to make informed decisions about improving them and increasing profits or productivity.
It enhances productivity and sales
Data visualisation increases productivity because when data is presented in an easy-to-understand manner, it makes it easier for employees to understand what they need to do next. For example, suppose an employee needs to complete a project by a specific date, and you present them with a visual description of the project timeline. In that case, they will know exactly what needs to be done and when.
In terms of sales, data visualisation is a powerful tool for business. It helps companies better understand their customers' needs, increase sales, and engage in more effective marketing campaigns. Visualisation tools allow you to display your products or services to let people see the benefits and features that make your offer stand out from the competition.
It is possible to share meaningful insights about your business or organisation with relevant stakeholders in various formats (such as interactive, easy-to-read infographics, charts, etc.) We can easily elaborate with the help of AI and machine learning for more effective data collection and analysis. By showing your stakeholders what makes your offer unique, you can demonstrate why they should partner with you or purchase from you instead of someone else.
Finally, we cannot overlook the importance of a quality data analysis strategy that allows you to identify trends and patterns that reveal relevant insights and increase efficiency to support decision-making. Once we have the correct information from this preliminary analysis, we can leverage data visualisation tools to gather and convey the relevant conclusions to all stakeholders.
Both elements allow businesses to track important metrics that could help them solve issues, optimise processes, and ultimately obtain better results. For instance, companies can track customer behaviour over time to gain insight into what types of messaging resonate with their audience and how they interact with content on social media platforms like Facebook or Twitter. Marketers can then use this information to adjust their messages to speak directly to their target audience's interests and concerns.
Examples of Data Storytelling and Visualisation Worth Studying
There are many excellent examples of how effective data storytelling and visualisation strategies can bring great results to companies in all industries. Let's take a look at some case studies in various sectors:
How Data Storytelling and Visualisation Works for the Public
How can we translate the data we manage into helpful information and communicate it to the public?
The following examples illustrate the significance of these techniques and how they can be achieved in today's information-rich world.
"The Origins of Coffee in Asia" by the Kontinentalist
Kontinentalist is a data-driven design studio based in Singapore that focuses on telling stories about Asia through infographics, animations, and other design formats. In 2021, they published an article entitled "The Origins of Coffee in Asia," where they use several elements such as card visualisation, dynamic maps, and charts to display content on qualitative and quantitative data on the origin and use of coffee throughout the Asian continent. The cards are interactive, where the user can click and learn the most relevant facts about this popular beverage.
The design of the formats is straightforward to use, simple, modern and generates a dynamic engagement that allows you to learn and memorise the information very quickly. It is also accompanied by a 100% informative and entertaining article that will enable you to learn about many cultural aspects of Asia.
"Can You Live on the Minimum Wage" by The New York Times
Another excellent example of how data storytelling and visualisation work perfectly for all types of industries is this article by the New York Times entitled “Can You Live on the Minimum Wage?”
The article included a calculator allowing users to input their salary and see how they would fare if they earned minimum wage.
The article is well-written, but the interactivity of the graphics they presented makes this story powerful. Visitors could make their own decisions and see what it would mean for them if they earned minimum wage.
Data Storytelling and Visualisation for Internal Purposes
On another note, with the rise of FinTech, changing consumer behaviour and advanced technologies are two aspects that are becoming more relevant for many enterprises and organisations.
Taking the insurance industry as a framework, many FinTech startups offer solutions based on innovations such as risk-free underwriting for instant purchase and insurance technology to redefine the customer experience.
An example of this we have the work of Zipari, a leading FinTech company based in New York that provides analytics and customer engagement solutions for the health insurance industry. Zipari's technologies help insurers gather, organise, analyse and act on data critical to effective customer engagement—improving outreach while increasing ROI.
Groundspeed Analytics is another company that uses machine learning and artificial intelligence to help insurance companies make sense of—and act on—the vast piles of data they collect daily.
By analysing data and using it to increase revenues, gain better insights into customers and automate repetitive processes, Groundspeed Analytics helps companies reach new heights.
We can see how using more sophisticated software to obtain, process, analyse and present data through modern visualisation tools can effectively transmit insights from any business. At the same time, directors and their work teams can use this to optimise resources, determine patterns, and even predict possible outcomes of current efforts and how they can be improved to achieve the desired objectives.
Finally, Ethos Life provides families with a software platform that allows them to easily and quickly enrol in the most suited life insurance plans for their needs, providing instant access to coverage.
The insurtech company’s service model turns customer lifestyle information into quantifiable data points and then uses that to develop a strong picture of a customer’s health risk.
For customers, this means they can purchase insurance policies and activate them quickly, all while using their preferred device.
These cases of data analytics—shown here as examples of combining visualisations, interactivity, productivity, and classic storytelling—demonstrate the importance of a clear message supported by analysis.
Data Visualisation Best Practices for Effective Storytelling
Although data is helpful in many forms, including raw, granular records of transactions, it is most powerful when presented to enable others to gain insights from its analysis. Such presentations can take many forms depending on the nature and subject matter of data being revealed or communicated.
Data storytelling can be an eye-opener for organisations and individuals to communicate with others about what they do and don't know. The following tips will help organisations share, communicate and encourage understanding business data.
Take into account the audience
Remember that communication is a two-way street—it is not enough to ensure you get the data out there in a digestible, attractive format. It would help if you allowed your audience to ask questions and understand what you're trying to communicate. Every organisation and person will have a different way of doing this, but for your work to be practical, it needs to be understood by your colleagues and the target audience.
Provide the most information with the least effort
Is the hype around big data over? No, not really.
The human eye still works best compared to any automated analytical technique used by software today. Therefore, you need creative visualisation techniques to present your data interestingly focused on communicating insights. For example, share your stories visually, use visual analysis to refine your insights, and keep track of changes in patterns over time.
Simplicity is the key
Data analytics storytelling provides limitless business opportunities. Directors get reams of information on key performance areas and individual departments. Still, in practical terms, they need a fast, accurate snapshot of what's working and what isn't – and a story to present to their stakeholders.
Some helpful tips that allow you to transmit the analysis of your data most simply include:
- Streamline the cognitive process (take into account saturation and data-ink ratio, meaning use visual elements such as saturation, font size, and positioning to present your data).
- Small data sets do not need a graph to analyse the results.
- Simple bar charts are one of the most influential and popular visualisation forms, as they present information very quickly, including several categories within the same topic.
- Choose a waterfall chart over "food charts" (pie charts, doughnut charts, etc.) to present your information quickly, as it is difficult for the human eye to see the correlation between charted data and numbers.
The insurance data strategy process is full of complexity and inconsistency, where numbers are regarded as facts. For example, suppose that a person wants to get life insurance. Will it be difficult for them to understand all the technical details in the policy? When the company sends them a policy document and all the clauses, will they be confused or not know where to start? No matter the situation, an image tells a thousand words, and a well-designed visualisation can clearly show the data and its relationship with each other.
More insurance companies are now using data storytelling and visualisation to establish a stronger relationship with their customers. In a way, data visualisation serves as a bridge between your customers and your insurance company. It presents a clear perspective on how you have helped someone in the past and conveys your empathy for them.