How does data analytics boost customer loyalty in the insurance industry?

The world is still in recovery mode amid the evolving global health crisis. Interest rates are lower than ever to encourage loans and investments, however, this has also resulted in minimal returns for paid-in premiums. 

The insurance sector is seeing an unprecedented decline in product penetration growth—especially in mature markets. The latest statistics have revealed that the fee structure for financial advisors presents a conflict of interest that can harm consumers, especially in Europe, the US, and Australia. 

This has industry analysts and experts hunting for new solutions. The only viable solution seems to be adopting a more customer-centric approach to insurance.

The path to achieving that involves big data. Big data can provide the insurance industry with plentiful, unprocessed, raw information, and data analytics platforms can process it into the insightful data that is needed to incite change. 

Does having a loyal customer base solve the challenges facing the insurance industry? 

Data shows that customers who are loyal to their insurers cost less to serve, stay longer, buy more, and recommend their insurance provider to family and friends. The survey, however, also reveals that insurance companies find it hard to build customer loyalty. 

The same set of data processed through different filters allows us to understand that the main reason behind this is the lack of interaction between insurance providers and their customers. 

Enhance brand image and improve customer satisfaction

Data analytics provide insurance companies with the insights they need to build all-inclusive policies. This means that customers can get tailor-made policies that fit all their requirements instead of purchasing multiple plans from the same provider. 

Data analytics can also help insurance companies optimise their communication channels to have better interactions with their clients. 

Knowing the different demographics that form your customer base and noting their preferred mode of communication, whether it is through a website, in-person, phone call, or video chat can help build relatability. 

In short, interaction is the pillar of building customer loyalty and data analytics is its facilitator.   

Solve fraud issues and keep premiums low

Fraud is one of the biggest issues faced by the insurance industry. Statistics suggest that at least one in ten of every claim filed is fraudulent. Considering the number of policyholders these companies serve, this can reach a staggering number. 

The result of this is an increased premium for the rest of your client base. With data analytics, however, it is possible to find these cases, resolve them, and prosecute the culprits swiftly before it causes widespread effects.  

Big data solutions such as social network analysis and telemetrics can be used to achieve this. 

Leveraging these solutions pays off because keeping your premiums low is a guaranteed way to keep customers satisfied and loyal. 

Accelerate settlement cases and streamline customer payouts

Data analytics can also be used to speed up settlement cases. The main reason lawsuits and claims take a long time to settle is because of the large amount of analysis that needs to be done. 

Data analytics allow firms to check the claim, analyse it, and access the customer’s claim history instantaneously. This can increase the speed with which a firm can give customers their payouts.  

Innovations and solutions geared at improving customer interactions 

The rise of online aggregators or comparison sites, to use the more popular term, has led to an even greater decline in the interactions between customers and service providers.

Technology has, however, allowed many companies to redefine their image and service as insurance providers in order to build meaningful relationships with their clients and, in turn, develop loyalty. 

These changes and innovations would not have been possible without the insights gleaned from data analytics and the advances made in data modelling techniques. 

Advances that include:

• Insurance companies investing in wearable devices that can help improve worker safety. 
• Funding smart home technology to minimise flood damage.
• Gamifying safe driving to stimulate consumer engagement. 
• Stolen vehicle tracking and recovery services. 

Building insurance companies that are customer-centric with data analytics 

With the information gathered from social media, mobile data browsing history, and purchasing history, companies in the insurance industry can gain a more personal understanding of their clients. 

Equipped with this information, they can find swift solutions to process and service issues, providing the customer with an easier and more wholesome experience. 

When coupled with the ability to build insurance plans and policies tailor-made for individual customers, we can confidently say that big data and data analytics together can help increase customer satisfaction and build loyalty.

Passenger data analytics forging the way forward for the aviation industry

Airports have the task of bringing in more passengers into their terminals while meeting several challenges like terminal congestion, rising passenger costs and difficulty in funding infrastructure to accommodate crowds. These factors are a simple recipe for deficient facilities, poor service and unhappy passengers.

Compounding the issue is the regressive effect of the pandemic. Aviation is undoubtedly one of the industries hit hardest by the pandemic. More than 40 major airlines have grounded their fleets while other airline carriers have suspended over 90% of their flights. International flights have been restricted due to border and entry restrictions. So we see a serious decline in passengers passing through airports, which cuts deeply into revenue and productive efficiency.

Now more than ever, passenger data analytics is urgently needed. Below are a few ways that this strand of analytics is revolutionising the traditional approach towards the passenger experience.

Airports are taking action by making use of passenger data analytics with highly encouraging results. Passenger data analytics offers new tools and processes to help airports make more effective decisions that improve airport performance, make better use of terminals, generate revenue and enhance the passenger experience from curb to gate.

On average, a 6-hour flight generates over 240TB of data, providing a rich body of information to work with. If this data is efficiently inspected and analysed, it will go a long way in streamlining passenger activity and improving safety, a huge boon considering recent events.

Predictive analytics is revolutionising the game

Investing in technologies to improve the rate of passenger processing is especially useful, considering the immense pressure stakeholders are putting on international airport terminals to improve processing rates.

Besides, storing and analysing the collected data in batches is a difficult, time-consuming task that could be made much simpler with sequential analysis. Passenger data analytics presents a method for airport managers to discover which variables provide a better understanding of passenger processing times and identify problematic passenger profiles without the hassle.

Airlines require the development of passenger data analytics as it is an effective and holistic forecasting model to regularly assess the impact of operations, like increasing aircraft available seats, adjusting fares and introducing new routes. Forecasts and predictive analytics also take account of actual statistical trends and results borne from the constant influx of data collected. The ability to capture detailed passenger information is beginning to change the way airport managers think, react and plan, essentially revolutionising the industry.

Sensor technologies to streamline passenger flows

Sensor technologies can systematically track passenger movement within an airport, and the data can be linked to a range of other information sources such as airline passenger data, schedules and points of sale. The combined data is fed into a centralised information database, accessible by airport management. This data can be accessed and then utilised to optimise space and flow.

Controlling passenger terminal flow patterns can vary significantly based on season and location in the airport. Of course, this congestion, confusion and delay can contribute negatively to the overall passenger experience, so solutions that could optimise traffic flow will be useful.

A new solution passenger analytics and information-based management—is emerging as a promising tool to help airport managers face these challenges. It is a solution that makes use of cost-effective sensor technologies to optimise the space airports already have and make more informed, strategic decisions. This analytics platform is used when mapping outflows, placements and structures of the whole airport to ensure an optimal experience and efficiency.

Forging the way forward with passenger data analytics

Passenger analytics changes this dynamic through a combination of sensor tracking technology, predictive modelling and new management practices. The innovative combination of information, planning and coordination can fundamentally change how today’s airports are managed.

Real-time access to passenger data analytics in the 21st-century is what powers organisations to form a strategy that will help them navigate the ongoing pandemic that has cut down the passenger count. With analytics producing informed decisions, airports can appropriately price tickets, arrange optimal seating according to protocol and hygiene measures to ensure there is some sort of revenue streaming in during these abysmal times.

Investing in data analytics installation and administration

Passenger data analytics will play a critical role in coping with the challenges airline carriers face in the future. With airline carriers constantly collecting data, they can continue making informed decisions as they adapt and grow in the post-pandemic climate by leveraging passenger data analytics to stay above ground.

Visit our website for more information on how your industry can leverage passenger data analytics to supercharge your bottom line.

How consumer analytics is changing business models

Consumer analytics is changing and improving business models, which are essentially the design of business operations covering different areas, like financing methods, customer interaction and product/service development. It attempts to answer questions like “What are we providing?” and “How do we do it properly?”. With the granular insights from consumer analytics, there is a lot of potential for organisations to fundamentally transform their business model to increase profitability, reduce costs and lower chances of failure. In this blog, I am going to explain how data analytics transforms business models and how that affects the customer-organisation dynamic.

Organisations make better, smarter decisions

Organisations are beset with challenges from intense competition to government regulation. Businesses constantly have to meet all regulations without breaking the law, while still making a profit in a competitive market. This is where we find the value of consumer analytics. Data analytics paves the way for techniques that were previously difficult to accomplish, like dynamic pricing. Setting a price that covers (fixed and variable) costs while still tempting the consumer is a trying task, but analytics can analyse the demand curves for different consumers to help organisations set the right price for each customer.

Furthermore, inventory managed more intelligently can minimise stock management costs. Consumer analytics can analyse and predict consumer demand, allowing organisations to smartly manage their inventory to minimise costs and be more responsive to consumer usage. Organisations can make smarter decisions on current stock levels, when to stock and the total quantity to stock.

Analytics delivers a deeper understanding of consumers

If there is one challenge most B2C companies have, it is staying relevant to their customers. Whether it is a lack of new products or friction in the customer journey, organisations often have difficulty building a strong relationship with their customers. However, consumer analytics goes a long way in addressing this problem by giving a deeper and better perspective on how customers think and behave. Sentiment analysis allows organisations to analyse customer feedback provided on different mediums, like customer calls to decide if the overall sentiment is negative and positive. A deeper understanding of consumers helps companies ignore the tone of the vocal minority and make intelligent adjustments to products and services where needed.

Another important aspect of understanding consumers is the customer journey. Customers go through several steps before deciding to make a purchase, if there is friction in the process, then the customer will leave without purchasing anything. Understanding the customer journey is crucial for business success. Fortunately, consumer analytics is one of the best ways to better understand the journey, highlight flaws and fix them.

Consumer analytics can study touchpoints in the customer journey and identify key patterns that affect sales. Once these patterns are identified, organisations can better understand the customer journey and address pain points that are turning customers away. With consumer analytics, organisations can tackle the weak points in a customer’s journey and pave the way for smoother customer transactions.

In fact, we will see the entire business chain transform from a linear supply chain to a data-sharing model where suppliers, organisations and customers understand the value of data and cooperate with one another to meet their own objectives.

Transforming an organisation’s relationship with data

Organisations often store data in silos, however with consumer analytics, organisations will be forced to change their data storage methods, given that analytics will encourage organisations to move data away from information silos into data lakes while machine learning algorithms can run on these data lakes to reveal unexpected findings. Storing data in a data lake also allows organisations to make better use of real-time streaming.

For example, instead of drawing up a report to better understand what happened and make a decision, organisations can use data lakes to empower their employees, so they can make more informed decisions while working. The importance of data lakes becomes even more important when you consider the potential sources of data, which includes IoT and mobile networks.

Consumer analytics is the future

Consumer analytics is not just transforming the way businesses operate, it is paving the way for brand new business models that will transform business-consumer relationships. The conventional linear business relationship between organisations and consumers is changing and giving way to a two-way dichotomy, where both parties cooperate for mutual benefit. We are seeing an age where the customer journey is personalised thanks to certain practices, like dynamic pricing that were not possible without analytics. All these factors and more are sure to transform business models.

How to become more customer-centric with customer analytics

Customer analytics is the key to transforming from product-centric marketing and customer-centric marketing. Customers are no longer making purchasing decisions based on a product, they are making them based on their purchasing experience. Research shows that 72% of millennial customers do not spend money on products, they spend money on experiences.

Hence, it is important for marketers to shift from product-centric marketing to customer-centric marketing. The key to a successful transition is customer-centric analytics. In this blog post, I will explain why customer analytics is necessary to make this transition.

What do we mean by product-centric and customer-centric?

First, it is important to establish what we mean by customer-centric. Customer-centric marketing means creating marketing messages that resonate with your customers. It is a contrast to product-centric marketing where the message is all about explaining how the product is awesome, the way it benefits the customer and why they must own it. Customer-centric experience goes beyond the product to encapsulate the entire experience from a point of first contact to post-purchase. Compounding this service is omnichannel marketing, where customers use digital and physical channels to complete a sale and expect consistent, high-quality customer service across the board.

To accomplish this level of service, customer analytics is necessary because conventional analytics systems are not enough to get the job done. Conventional analytics is not suited to the job for several reasons. The customer journey happens across several channels both online and offline. Hence, marketers need an analytics platform that integrates data from different sources effortlessly. Certain tools like descriptive analytics, while useful are insufficient for marketers’ needs. Descriptive analytics can describe what happened in the past, but cannot predict what will happen.

How to use customer analytics to utilise customer-centric marketing

Customer analytics is the best tool for the job because it allows marketers to track and analyse the different ways (or different combinations of marketing channels) customers interact with an organisation. Marketers can identify the key points in a customer’s journey in relation to business goals. Customer journey analytics allows marketers to analyse data from the interactions of millions of customers.

One of the key components of customer-centric marketing is mapping out the customer journey. The customer journey spans across several channels like emails, social media and TV ads. Furthermore, each buyer has their own journey and will interact with the brand differently. Customer analytics allows marketers to map out the journey of all these individual customers and then highlight key points in the journey depending on any metric. For example, customer acquisition or repeat purchases.

These tools empower marketers with clear, precise data revealing points in the customer journey that make customers leave. If marketers want to accomplish certain goals like higher customer retention, they can survey the customer journey to discover why customers came back by comparing their journeys against those who did not return.

Customer analytics can predict journeys for customers – bolstered by the fact that machine learning technology is incorporated into the analytics platform, meaning it can study the journeys of millions of customers to predict the route of customers in the future. The information is invaluable in the hands of marketers. They can use the information to experiment with new marketing funnels. With these tools, marketers can find the best ways to optimise their marketing funnel to find out what will work and what won’t, making it a vital tool for most industries.

Marketers have to answer several complex questions and customer analytics can help them get answers to these questions. How to add value for each customer? What percentage of customers take a certain path? Which customers take a certain path? These questions can be answered through data analytics.

Shifting from product-centric to customer-centric

A customer-centric journey is crucial for many brands to survive. However, the insight needed to provide a customer-focused journey can only be found in customer analytics. Analytics is necessary for marketing to contribute substantially to sales. Marketers can measure sales against KPIs and measure customer interaction in real-time. If marketers use customer analytics properly, this will lead to a more loyal following for brands, which guarantees healthy sales for years to come.