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rg1 continually updates data from various sources to adjust audience segments in real-time.


Its dynamic, rules-based decisioning has the flexibility to move with a customer or group of customers as they proceed throughout an omnichannel customer journey.

The core value of segmentation with rgOne is that the platform creates and activates dynamic segments using both rules and machine learning to deliver relevant, personalized experiences.

rgOne configuresdynamic rules and modelsto build and apply segmentsuniversally, optimizing themdynamically throughout anongoing customer journey.

This approach is central for brands to take a customer-centric view of which customers they’retargeting—independent ofchannel.

Types of Segmentation







Behavioral Segmentation allows companies to tailor their marketing efforts to specific groups of people, based on their buying habits, usage patterns, brand loyalty, and other relevant behaviors.

Behavioral Segmentation

Marketers must put in considerable effort to cultivate customer admiration and loyalty to their brand, which in turn leads to a constant buying cycle.

For example, airlines target frequent flyers. These passengers are often segmented into different groups based on their behavior, such as their frequency of travel, their loyalty to a particular airline, and their willingness to pay for certain amenities.

A prime example of behavioral segmentation is brand loyalty, which results in a consistent purchasing pattern classified as a behavioural trait.

To target customers with significant brand loyalty, a companie might launch rewards programs aimed at improving this behavior and capturing new loyal customers.

For example, airlines may offer special rewards programs, such as frequent flyer miles or access to exclusive lounges, to customers who fly frequently or who have a history of brand loyalty.

A psychographic segmentation delves into the intrinsic traits of your target audience. Such traits may include personalities, hobbies, values, beliefs, consciousness, and more.

Psychographic Segmentation

Understanding your target audience on a deep, psychographic level is crucial, as it allows you to cater to their unique needs and demands.

One company that excels at psychographic segmentation is Starbucks. Though not everyone loves coffee, Starbucks manages to please nearly everyone with its diverse offerings.

Marketers can gather this information through techniques such as focus groups, interviews, surveys, audience testing, and case studies.

For example, consider the lifestyle of someone who lives in a small beach town and makes a living surfing versus someone who works in corporate America in a big city. Both have vastly different preferences and requirements, and marketers must recognize these differences in order to succeed.

For the sophisticated coffee drinkers who care about quality and bean sources, Starbucks offers a mix of exotic beans from various regions worldwide. And for those who don't like coffee, but enjoy an evening hanging out at Starbucks with their coffee-drinking friends, they can opt for frappuccinos, lemonades, teas, and juices.

This strategy involves dividing a market into smaller regions based on various factors such as country, state, city, climate, population density, etc.

Geographic Segmentation

Geographic segmentation can help businesses save time and resources by targeting specific regions with the right marketing messages.

It allows them to create customized products and services that meet the unique needs of customers in different regions

For instance, a clothing brand may use it to target customers living in colder regions with heavy winter clothing, whereas those living in warmer areas will be targeted with light summer clothing.

Another example is a fast-food chain that targets customers in different regions with different menus, based on the preference of the local population.

rg1's dynamic segmentation capabilities are diverse, ranging from basic demographic data such as age, gender, and location to more advanced data points such as behavioral data, engagement metrics, and purchase history.

How does it work?

By analyzing patterns in the underlying data, machine learning algorithms discover commonalities that may otherwise go unnoticed and that, in turn, may be used to build segments to drive a campaign.

Demographic segmentation is an extensively used strategy because it allows marketers to cater to individual requirements based on at least one demographic element.

Demographic Segmentation

Age is perhaps the most apparent variable, as consumer preferences and decision-making processes tend to change throughout different stages of life.

Gender identity is another large variable when it comes to demographic segmentation.

For instance, media consumption habits differ significantly between each generation. Therefore, it is crucial to identify your target age range and the medium they use to get information, ensuring that your custom message reaches them effectively.

for example, many apparel companies cater to a mixture of age groups, offering different labels, advertising, and styles for every segment, including men, women, and kids of all ages.