Data, but make it customer-centric

Customer centric

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3 methods that lead to your perfect customer

Looking at any data set you collect in your business processes, you’ll realize it revolves around your customer or client. Each piece of information provides insights that are used for the objective of providing the best user experience. Whether we talk about products or services, the goal is the same – to sell them.

But there has been a shift a long time ago from product-centric business models to customer-centric. If you ask yourself why – the answer lies in data showing how consumers interact with companies. It isn’t anymore about just products or services doing their job, it’s the value and experience consumers get from using them. And to get to that, everything starts with them – your customers.

Companies have invested so much time and money in understanding consumer behavior and who uses their products and services and how. Basic principles of segmentation have been adapted to discover who brings the most value to the business. But there are also methods for discovering who doesn’t.

Let’s look at one of the industries where we had the challenge of delving deep into data to recognize customer segments by certain attributes and standards. The insurance industry is a highly competitive market, from car insurance, and property to health, the battle for customers is constant. It’s an area where price and service value play a great role. But it’s not only client acquisition that poses a challenge. It is how you determine which customers or clients are valuable to you.

So, let’s dive into a couple of areas where data helps in discovering who the clients are in the insurance industry, how they behave, and how you can determine what level of value they bring.

Insurance users segmentation

From internal data sources, CMRs, sales leads and records, and such, insurance companies have access to data on their past and current customers. If we talk about the B2B segment, which we did in the challenge, the size, profile, and revenue of the company (firmographic attributes) are factors that could divide them into appropriate groups. It’s about understanding clients, which insurance services or products they use, and what’s important to them when they choose them. Even insurance packages or deals are made tailored to clients’ needs and wants, and according to their shared attributes. But it all starts from data that is centered around them. The process starts with data cleansing and analysis to determine what will influence the segmentation and provide satisfactory results. 

Using data science methods and procedures provided us with the means to do the segmentation effectively and more precisely. In the end, this allows the insurance company to target its clients more effectively, and to create more cohesive and straightforward sales, marketing, and product strategies. There is less unnecessary resource expenditure on processes that are now covered by data-driven solutions.

User behavior forecasting

Clients and consumers are not static. Their behavior and attributes change over time, and so does their willingness or need to invest in certain products and services. By completing the first step, market segmentation, the insurance company now has the basis to track future market movements, either by segment or attributes. 

Once a golden customer, could prove to be the exact opposite in the future. His firmographic characteristics could change. Revenue could increase or decrease, the size of the company may change, and it all affects the usage of insurance and the price itself. By implementing forecasting solutions, we can predict how a certain segment will behave based on historical data. We can determine if the segment size will change and where are better opportunities to offer products or services. It can help in predicting which segments could make insurance companies lose money if they don’t shift the focus to profitable ones. Predicting user behavior will allow any company for that matter to be more cost-effective and recognize the needs of the market on time.

Client pruning

Client pruning is closely connected to consumer data and segmentation. How do you determine which clients provide the most value and which don’t if you never delve deep into their data?

Pruning is a technique reserved for recognizing which current clients or customers are profitable and will be profitable in the future and which ones will only invoke costs and lost opportunities. Client pruning helps management answer the question of who they want to serve. It’s used to maximize profitability and center efforts on certain segments. 

It starts with collecting and analyzing data connected to client quality and profile. Types of data used could be the same or similar to the segmentation ones, like company size, revenue, and the number of employees, which is important for insurance services. But grading clients could be done by analyzing factors such as pricing or total client size, timely payment, number of insurance services they use, profitability, last point of contact, number of sales reach-outs and success rates, cross-selling opportunities, and referral capabilities, and internal notes on client behavior. With data science capabilities, clients can be then ranked from those that are profitable or show potential to be profitable to those that mainly generate costs. Implementing a forecasting feature could help in anticipating which client will need to undergo pruning.

Customer-centric is data-centric

Data is a powerful tool. And only by using data science and data engineering methods do we see and utilize its full potential. The market is and will continue to be customer-centric because companies don’t sell products or services, they sell experiences. So, how do you reach that? By undergoing these three steps, segmentation, forecasting, and pruning. By doing so, companies can focus on important customers and adapt their strategies to serve only them. It will create a joint point of customer objectives and company objectives, where each side has its needs met. Such equilibrium will provide for a stable and more effective collaboration and relationship.

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