Often times segmentation of visitors into meaningful cohorts is not as easy as it seems. Segments can be build from various data points, starting with very obvious data like the visitor time, days, weeks or any other calendar based data. E.g. all visitors from within the last 30 days, or visitors from the last 24 hours.
When it comes to additional visitor criteria one can divide generally in anything technical, which is possible to gain about the visitor and any data, which may be added through third party systems.
One key element to visitor segmentation lies within the identification of the visitor. If one and the same visitors may not be identified again, it’s not possible to build meaningful segments. So what kind of segments should one aim to build, in order to create campaigns for certain audiences?
Types of segments
When it comes to creating a new campaign, one can distinguish between different audiences, in order to gain the highest conversion rate for each segment. Since different visitors are behaving differently and different segments should be addressed personalized to their need, it’s important to know, what kind of segments are available.
It always depends on the industry and business, which categories are being used as segments, basically they may be broken down into four types and various subsequent subtypes:
- New vs. returning visitor
- Casual vs. Power User
- Demographic and Geographic
- Age (Seniors, Baby-Boomers, Generation X/Y/Z)
- Role (Buyer, User, etc.)
- Job (Position, Role in Company)
- Geographic (Country, State/Province/ City, Rural, Urban)
- Psychographic (based on a certain Lifestyle)
- Browser Type
- Device Type (Mobile, Tablet, Desktop)
- Source (Referral, Search Engine, Campaign, Newsletter, …)
These categories may be extended or broken down even more, it will depend on how detailed one will target a specific audience and how much traffic overall is available.
Segments, may be generic for every kind of business, nonetheless it will be more usable and meaningful, once industry specific criteria is being added:
In this example a publishing house is chosen as industry. Different visitors should be addressed with different feedback surveys in order to get more knowledge about their need. There could be two groups, which would be addressed completely different:
- Casual Users, who read once in a while and sometimes have an interested in payed longer articles
- Power Users, who read and skip a lot of articles, return very often during one day, nonetheless lengthy articles are not important
In this example a fashion ecommerce company is chosen as an industry. Special Deals should be shown differently for certain visitors, based on the gender – nonetheless the online shop does not have a way of identifying non-logged in users according to their gender – and therefore for their preferred kind of clothes.
A simple multiple choice survey is being shown to the visitor, asking for the preferred type of clothes.
After the survey campaign has been shown the visitor and any feedback was being given, afterwards due to the answer specific offers may be shown to the user, since the preferences have been updated for this non-logged-in customer.
Based on these audiences different feedback campaigns could be created. Imagine to ask the group of the casual readers if a day pass for payed articles would be of interest and if they would be willing to pay an amount of x money for it, while targeting the power users with a different questionnaire about their willingness to order a yearly or monthly subscription and if they would be willing to do so with a certain discount. Both campaigns would lead to diametrical results and showing both campaigns to the same visitors, without segmenting before would make the analysis harder.
Hot and cold visitor data
Some tools and DMPs also data for single users into hot and cold data, for example all of the data being available for a certain user, which was collected before the user will enter a website could be defined as cold data, and data which is fresh and based on the current behavior or external influences like the weather, date, season, etc. would be classified as hot data. Combining both data pools and using it to segment an audience is key wo a personalized feedback campaign.