Useful Customer Segmentation Methods for Banks
Customer Segmentation is the division of a specific market into buckets of customers who share similar characteristics. However, gone are the days that big financial institutions can afford to segment their customers by merely using demographic criteria, including age and income, or firmographic attributes such as industry or company size. With fierce competition from fintechs and non-bank competitors, understanding the customers only by who they are is no longer enough for incumbents. Instead, understanding what consumers do and how they behave is imperative to better engage with them. That’s why many marketers refer now to “buyer persona” instead of “target market.”
Behavioral segmentation has become the most popular segmentation method for marketers: according to research from the Marketing News portal, conducted with 800 marketing professionals from over 20 industries, 91 percent of the respondents said that behavior is the most effective method of segmentation.
By extracting insights from customers’ actions, behavioral segmentation allows brands to mold every touchpoint with their customers in mind and make the experience relevant to them, eliminating friction points along the customer journey.
The Four “Ps” Of Behavioral Segmentation
Gary DeAsi, Director of Demand Generation at Pointillist, identified four “Ps” of behavioral segmentation and their benefits:
- Prediction: Foreseeing consumer’s future behavior through historical data analysis allows companies to have more control over their activities.
- Personalization: Different products or services can be catered to different segments of consumers, using the most appropriate channels.
- Prioritization: Allows companies to focus on those segments that will bring more profitability and positive outcomes to the business.
- Performance: Enables businesses to track and quantify the value of customer segments throughout a given period.
There are many different ways of behavioral segmentation, including:
- Purchasing behavior: Helps understand how customers from different segments behave during the buying process.
- Usage: Observes the frequency or the number of products or services purchased, allowing to predict customers’ loyalty, churn, and ultimately, their lifetime value. High usage, however, doesn’t necessarily translate into loyalty: a consumer visiting a retail branch too often, for example, might be a flag that they are only doing so because they are not finding the help they need in other channels (digital or telephone).
- Customer journey stage: Allows to address the consumer according to the stage they are situated in the marketing funnel, i.e. awareness, interest, consideration, intent, evaluation, and purchase stages.
Other Useful Customer Segmentations Methods
- Psychographic segmentation: This is a method used to divide consumers into sub-groups based on shared beliefs and motivations, irrespective of demographic criteria. A millennial consumer can be an early adopter and confident user of online banking and cellphone banking apps, but still be a conservative investor who does not feel equipped to manage their financial investments.
- Life stage segmentation: Though life stage segmentation is frequently confused with age, it focuses more on the milestones of a customer’s life. Using another banking example: graduating from college is a critical moment, especially as the student debt soars across North America, impacting different demographics and age groups. Similarly, a newly married couple might look for loans to buy their first home. Understanding these milestones help banks cater products to these niches.
- Attitude segmentation: Examples of attitude segmentation are client’s satisfaction level and trust in banks. Legacy banks usually have stronger brands than new entrants, but consumers’ trust in these big financial institutions have been shaken since the world financial crisis in 2008, and satisfaction is definitely not on its peak. On top of that, many consumers believe that legacy-free financial brands are more trustworthy and ethical compared to banks.
There is no doubt that understanding who the customers are is still critical for banks and businesses in general. Techniques such as developing customer personas and ideal customer profiles are relevant for understanding target customers and, consequently, for effectively mapping the customer journey. However, banks have to get more granular to deliver meaningful products and services to their clients.
Personalizing the customer experience is vital if businesses want to thrive today, and banks have a lot to learn from other industries. The streaming giant Netflix uses algorithms to recommend films and series to viewers and create engagement and loyalty. Likewise, Amazon, on the pursuit of its vision statement – “to become the most customer-centric company on earth” – uses recommendation engines to suggest products to consumers based on their behavioral interests, taking advantage of big data to learn the user’s friction points and create the ultimate buying experience.
Leveraging the breadth and depth of customer data through advanced analytics techniques, including artificial intelligence and machine learning, is crucial to make better sense of this vast pool of data that banks have on their consumers, enabling them to be more agile and efficient in translating this information into actionable insights that support personalized experiences to their clients.