Diversify the offer depending on the Customer group
The customers belong to very diverse groups with different needs and habits. Some of the criteria for the division are simple and have long been used in marketing. For example, customers are divided by gender and age. However, such simple divisions are often insufficient in terms of business objectives. The more you know about the consumers, the more information you collect about them, the more sophisticated and precise the criteria for segmentation can be applied by your company.
As the number of variables to be taken into account increases, the labour intensity and complexity of the segmentation process also rises. However, these problems can be solved using machine learning methods. Clarifying algorithms can analyse customers for tens or even hundreds of features and distinguish natural segments (clusters).
Such segmentation may be behavioural, i.e. it may primarily take into account customer behaviour (both purchasing and other behaviour recorded by the company). This allows to achieve clearer and more business-oriented segments compared to the simple demographics-based segmentation still used in many organisations. Understanding the characteristics of customers belonging to particular behavioural segments helps both in making strategic decisions and in differentiating the offer and marketing communication, which can thus be more relevant and effective.
A huge advantage of solutions based on data analysis and machine learning is their scalability and applicability even for very large customer databases (going into millions). This makes it possible to assign each customer known to the company to the appropriate behavioural segment. On this basis, actions can be taken that are suitable to the segment profile.
- Possibility to focus strategically on the most attractive, profitable, developmental customer segments
- More effective communication by adapting the language and content to the customer segment
- Possibility to identify the segment to which a specific customer belongs
- Better understanding of similarities and differences between different customer segments
Clients: Leading marketer from retail industry, Marketer from retail industry, Marketer from retail industry