Customer Segmentation is being increasingly recognized by leading marketers to be a vital component of the financial marketing landscape. Every Financial Services brand has its own unique marketing objectives and needs. With respect to these needs, marketers are focused on capturing customers from specific sectors in the industry. These sectors include banking, insurance, securities, etc.

 Financial marketers have certain target audiences within these sectors to whom they want to market to. To cater to these audiences, they need to duly sort them into groups or segments based on their shared attributes.

Types of Customer Segmentation:

Normal Segmentation- 

 Segments created here are based on certain customer attributes. These include:

• Visitors belonging to a particular demography
• Prospects from a particular geography
• Visitors from a certain industry
• Customers who have purchased certain products or services
• Customers whose email click and response rates are pretty high
• Visitors who are spending more than 2 mins on the site
• Visitors who downloaded an ebook from the website

 The above segments are a set of rules that segregate users into specific buckets. Financial marketers target each bucket with personalized offers through various online channels such as programmatic ads, browser push notifications, e-mail, etc.

Predictive or Intelligent Segmentation-

This type of segmentation incorporates the use of Artificial Intelligence (AI). Using advanced AI-based algorithms, financial marketers can automatically segment customers based on a certain factor such as buying propensity. This is done using various industry variables and parameters that aid the algorithms in learning from a customer’s past history. Analyzing a customer’s historical data enables the algorithms to accurately sort users into the right buckets or segments.

For example, users with the highest propensity to buy a certain product are categorized into a segment called excellent leads. Users with medium buying propensity can be sorted into another segment called mediocre leads. Users who have zero propensity to buy the product can be put in a segment called bad leads. After these algorithms are set in place, returning website users are sorted accurately into these segments. This happens in real-time as and when a returning user visits the website.