Go deeper with your customer segmentation

Customer segmentation, in simple terms, is dividing your customers into smaller groups based on shared characteristics. Doing so allows you to deliver more personalized messages and offers, identify high-value customers and serve them in a manner that increases customer satisfaction and loyalty.

Client data is like a natural resource that you can mine for business value. Like the CEO who challenged his leaders to find “veins of gold” within their departments that would deliver added business value, you have an opportunity to go deeper with your practice.

From an advisor perspective, more insight on customers improves leads management, conversion rates and presents opportunities to earn greater wallet share.

Many advisors will segment their clients based on assets and revenue — that’s just a first step. Present levels of investable assets hardly qualifies as an insight.

Two ways to segment customers

In market research, there are two popular approaches to customer segmentation.

One way is lifestyle segmentation, based on the demographic of the customer or household within a geographic area — where your customers live. Lifestyle segmentation is typically used to identify location, build profiles of your clients and target marketing campaigns. It can be easy to implement and is only dependent on accessible third-party data like the Canadian census. It also offers scalability across customer groups, marketing channels and platforms within a geographic centre.

The criticism of lifestyle segmentation is that the information can grow dated, it is limited to fixed segments and it cannot be personalized. So, it has limited application in building client journeys or targeting offers. In fact, sending an offer that’s unsuitable to a client or prospective client can do real harm to your reputation.

The second approach is behavioural segmentation, which analyzes your customers’ behaviour and actions providing deeper insight into their motivations and a better understanding of how they make decisions. Third-party data can be included in the analysis, but the group is primarily identified based on the actions your customers take, including purchase habits, loyalty and benefits sought. Each segment is defined by the unique personality profile of your customers in how they act and make decisions.

The proponents of behavioural segmentation point to the individualization and personalization that can be used to target marketing messages and offers to those with buying intent. This is particularly valuable when you are working directly with customers. The depth of information makes it very useful for building customer journey maps and getting a better understanding of the people you serve.

It offers deeper insight into customers’ motivations and decision-making. And because it focuses your attention on people most likely to respond positively, it can optimize marketing spend and business development efforts.

It is more flexible in that you can define custom behaviour-based segments specific to the financial advice business. And it can be updated in real-time based on the customers’ life stage.

A criticism of behavioural segmentation is that it takes more time and resources because it mines your data.  But this is the cost for alignment of behaviour to financial need. Segments also change over time, so you need to regularly monitor and update them.

Which is better?

Behavioural segmentation is often considered better because it focuses on how people behave rather than where they live. That said, when deciding whether to use lifestyle segmentation or behavioural segmentation, consider what you are trying to accomplish. If it is to be used in a broader marketing context, lifestyle segmentation can help. If you are looking to gain a better understanding of how your customers make decisions to create a personalized experience and target offers to those with buying intent, behavioural segmentation will deliver results.

From an advisor perspective, behavioural segmentation has the greatest potential because of its emphasis on personalization.

What is interesting is that the strength of each approach addresses the perceived weaknesses of the other. Given their complementary nature, both models can be used together. It will likely balance your need for scale and increase precision to ensure your business objectives are achieved.