Modern marketing relies heavily on customer segmentation. Businesses from all industries collect data from a variety of sources to better understand their consumers and segment them into different criteria-based groups so that they may be targeted more precisely. This is because this is one of the most effective strategies to raise your marketing efforts and improve the efficacy of your campaigns.
You may use a variety of customer segmentation approaches to do this, ranging from manually segmenting clients based on a few established criteria to utilizing complex AI-based systems.
There is no segmentation at all in the most basic kind of consumer segmentation. As you can expect, this is almost always a major error. In the golden era of customization, treating all of your consumers as if they were, all the same is a certain way to lose customers to the competition. This may seem like a no-brainer, but you’d be shocked how few firms segment their clients at some point in their marketing efforts, particularly in email marketing.
This isn’t to say that the no-segmentation method isn’t useful. There are several occasions in which this path can be taken without serious consequences. For example, if you operate a firm in a highly specialized area, segmenting the audience may not be necessary because the audience is too tiny and extremely interested in your offerings. When you first start your firm and have a small number of consumers that don’t require segmentation, having no segmentation can be beneficial.
- Not segmenting your consumers is never a smart idea in the long term, as the requirement to divide clients into groups develops as your business expands.
Humans handle the whole process of segmenting clients, which is where proper segmentation begins. There are numerous approaches to this activity, ranging from spreadsheets to BI systems. The division is usually based on criteria established by the experts themselves. Some of these factors are self-evident (age and gender), whereas others are more specific.
You may target the generated groups differently if you use those criteria to execute a simple segmentation. However, it has several flaws:
- It must be updated regularly to remain relevant.
- There are only a few criteria that may be employed.
- Scaling is almost impossible.
All of this adds up to a time-consuming endeavor that isn’t producing the greatest outcomes. As with the no segmentation technique, there will be times when manual segmentation will be enough for your organization. Small organizations or those with readily segregated groups may employ it with confidence, knowing that the technique will serve them for a longer time due to the more solid segmentation criteria.
This is where the magic occurs, as machine learning begins to influence the consumer segmentation process at this level. This method is mostly driven by machine learning. Because datasets are clustered according to patterns, AI-based algorithms may be used to split clients into extremely precise categories (which range from the obvious to the fairly obscure).
The outcomes of this method are quite varied, as they are determined by the machine learning algorithms used. In this regard, hierarchical aggregation and k-means are the most extensively used methods for consumer segmentation since they can function with little or no human intervention. Both strategies, for the most part, may produce highly-specific groupings, which can be both a benefit and a problem.
On the one hand, having more clearly defined groups can help you gain a better knowledge of your clients, which can help you improve your marketing strategy. However, because the algorithms work on all data, you may waste a lot of time generating a lot of groups that don’t provide any helpful information. Furthermore, you’ll only get a limited number of clusters as a consequence, which may leave some actionable changes on the table.
Overall, automatic segmentation is a terrific way to create more specific groupings based on variables that you may not be aware of. Its scalability makes it a good choice for larger businesses with a huge audience and a lot of data that can’t be managed manually.