Behavioral Segmentation: A Targeted Approach To Modern Marketing
Originally published on WebEngage.
When only 22% of businesses are satisfied with their conversion rates and on average, a typical website conversion rate is 2.35% only (Wordstream), you wouldn’t be hard pressed to imagine why you are always on the hunt for conversions.
Audiences are fast embracing banner blindness, are simply ignoring regular TV ads and skipping banner ads on websites. This is why personalization is the new standard. And it will require you to go one step further from audience segmentation based on demographics to segmentation based on behavior.
Behavioral segmentation allows you to dig deeper into your customers’ psyche and convert them. This article shares the methods, tools, and common examples of behavioral segmentation.
How to segment your audience?
Let’s assume that a group of your app users only reinstall the app or visit the website, a week before major holidays. From this, you can infer that these people are driven by occasion or hunt for a bargain. And you can then use this insight to increase your sales during the discount season by re-marketing to these people.
But before you make any conclusions, you need to gather a lot of data. Since you want to cover both existing and prospective customers, here’s where you need to look for the data:
- Sales reports to see when and how much each lead is buying. Are they a returning customer?
- Website usage to see how regular a particular user is on your website. This is extremely important if you have a blog or a widget people can use for free.
- App usage can help find the most frequent users. Using the app in a particular time of the day or on special or designated days is also a telling sign.
- Email tracking to know about people who open, read, and convert from your emails. The ones that do are your top priority.
- Social media usage to identify the top fans and regular users of your services. Connect social media profiles to leads in your CRM to get more precise results.
When you’re looking for data, try to answer these questions:
- Is this person already a customer?
- Where do they stand in the sales funnel?
- What device do they use to view the app or website?
- Do they convert and follow through with the payment?
- How frequently do they use it?
- When do they use it?
- What features do they mostly use?
- How loyal are they?
- How price-sensitive are they?
Once you obtain data to answer all these questions, start looking for patterns.
While you can potentially find any type of correlation in your data, there are 4 main types of customers according to what drives their purchasing decisions:
Types of customers based on their decision-making factors
Traditionally, customers can be divided into 4 categories on the basis of how they interact and purchase from you.
Going beyond typology
These four types are a great basis for behavioral segmentation, but you shouldn’t be confined to them. While you analyze the data, you may find a correlation that’s not described by the four basic categories, and that’s okay.
Your goal is not to sort your customers into either of these 4 types. That’s the base level of behavioral segmentation. Your goal is to find correlation in data.
Amazon doesn’t stick to the four types alone. The company looks for patterns in purchasing behavior and uses that as a basis to offer products that may be interesting to a user.
For instance, the algorithm rules out that someone who is buying an outdoors grill may be more interested in new kitchenware than someone who’s buying a freezer.
This goes beyond the traditional typology but presents you with an opportunity for improvement.
Here’s how you can implement your findings in business.
