Lead generation is one of the main challenges for both B2B and B2C companies. But before you can classify leads as cold or warm and implement an effective action plan, you need to be able to work with reliable data. That’s why a good starting point is to first qualify the data in order to generate more leads through segmentation, which should be part of your communication strategy. Your goal is to maximize your company’s revenue in the long term through clear segmentation and smarter lead generation .
Database segmentation criteria
To obtain a relevant and promising segmentation in terms of marketing, it is preferable to use cross-sectional data.
For example, the segment responsible for recruiting SMEs in the construction sector in French-speaking Switzerland is considered a segment that can be used for marketing purposes because it is quite specific.
Segmentation vs Targeting
Segmentation and targeting are often confused. However, they are two different stages in a lead generation strategy.
- Segmentation involves dividing the market argentina telemarketing into subsets using a certain amount of collected data.
- Segmentation, on the other hand, involves identifying segments to be used as part of a marketing strategy.
As a result, segmentation allows you to sample all segments to prioritize which ones to work on with a tool like CRM.
Lead Generation: Why Use Segmentation?
Segmentation has many advantages when it comes to a lead generation strategy.
Above all, segmenting your database allows you to personalize the customer experience and the message being conveyed. In a recent study by EverString and Ascend2 , personalization is the most successful use case for a data-driven approach, according to 41% of marketers.
It is crucial that your customers are understood and treated as individuals. Showing them that you fully understand their challenges, tasks, and training of their thinking will help you convince them to buy your products or services. Understanding b2b sales challenges: managing leads the problems that companies face is an important step in implementing their strategy.
Segmentation allows you to do this because it gives you a very detailed view of your target customers. You will be able to accurately analyze their needs, wants, and behaviors and then send them the right message at the right time and place.
Similarly, segmentation allows you to structure all your data. Both marketing and sales teams get all the information they need to develop an effective strategy through lead scoring. In particular, it allows you to prioritize marketing actions based on RFM (recency, frequency, and monetary value). For segmentation to bear fruit, lead scoring is essential.
Lead segmentation also allows you to improve the effectiveness of your sales and marketing campaigns for your potential customers. You can use lookalike audiences to repeat actions that have yielded good results, thus multiplying their effect. This optimizes the ROI of your campaigns.
How to use segmentation to refine your data and get more leads?
To create relevant and efficient segmentation, it is necessary to qualify the data correctly. To do this, you can use different types of segmentation . However, the steps to be followed remain the same. Only then can you prioritize and activate the levers with the help of the CRM tool.
Different types of segmentation
Generally, there are two main types of segmentation in sales and marketing.
- The first is rule-based segmentation. It consists of dividing the audience into subgroups according to pre-selected criteria. This approach is germany phone number based on the most objective analysis of the available data.
- The second is cluster-based segmentation. It is based on observing and analyzing information without preconceived ideas. Similarities are observed and specific similarities are identified rather than assumed ones.
Depending on the type of data you have available, you can start with an a priori approach to segmentation and then move to an a posteriori approach when your database is sufficiently enriched.
You can also combine these two approaches. In this case, we check whether the a priori assumptions are confirmed by the results of the a posteriori analysis. If necessary, some corrections can be introduced along the way.