A World of Customer Profiling

Customers are perhaps the indisputable foundation of any business, no customers no business. As simple as it may sound, the concept of acquiring, retaining and gaining more customers is still a critical problem many businesses still struggle.

To keep the customer happy, businesses would need to understand their customers’ habits. Indeed, that is where customer profiling comes into their picture. The truth is this: even though most businesses claim to be “customer first”, they aren’t taking steps to actually be there for their customers.

Customer profiling is a way of creating a portrait of your customers to help you make design decisions concerning your service. In this process. Your customers are broken down into groups sharing similar goals and characteristics to understand them and create a service or product they actually want.

Traditional Customer Profiling

In its traditional sense, most businesses carry out customer profiling by creating personas to represent customer groups. The most popular method of creating these groups is using the following 4 categories.

Geographic for customer location areas, Psychographic for customers interest, values and opinions, Behavioural for customers buying habits and Demographic for quantifiable features of a customer population with typical features like age, gender, income.

An example of this would be a local airline that has kept records of its Customer travels and booking over the years of their operation and wants to profile their customers. Their sales and marketing team conducts simple analysis and grouping of their customers on their database and comes up with personas to represent customer groups they made so they can generate leads and provide a great customer experience.

With a daily increase of customer records, all the customers eventually don’t fit the created personas, spiralling to individual experience and guesswork to find customer leads and customer offers.

More Data, Less Guesswork

With Machine learning, intelligent profiles are made to take the guesswork out of creating customer personas. Instead of relying on instinct, individual experience, or preconceptions about a product’s primary audience, sales staff can access the collected experience of the company to build an accurate picture of their customers.

This is because machine learning models assume the true realities that data captured is mostly raw and rich and can give more authentic results in a shorter period of time, almost immediately while the business operations are being conducted. With multiple personas made from multiple data sources, decision-making process and customer acquisition costs are significantly reduced.

Our local airline can implement Machine Learning technology that would continuously create intelligent profiles from multiple data sources and provide predictions of flight bookings, travels so they can provide personalized offers to them.

Today, customers are hard to impress, and much harder to keep. However, when you do build a great customer experience that delights customers, and work with them to highlight their successes, these meticulous customers can become your secret to growth. Companies that learn from their customers will in turn be successful, but they have to put the customer first, learn about them and dedicate themselves to their success.