Why do you need cluster analysis?
Cluster analysis provides an alternative to analysing each customer individually, which is impractical and oversimplifies the data with a one-size-fits-all approach. Cluster analysis enables the effective communication of results and decision-making at a strategic level while still considering the unique characteristics of each customer group.
What is cluster analysis?
Cluster analysis uses general business data, marketing and sales activity data, and mathematical algorithms to match patterns and determine the best-fit criteria for future marketing and sales actions.
Simply put, the algorithms review customer data, catch similarities, identify meaningful, naturally occurring groups within a dataset, and distinguish these as clusters.
The process is not based on any predetermined thresholds or rules. Rather, the data itself reveals the customer prototypes that inherently exist within the customer population.
The Advantages of Cluster Analysis
Compared with predetermined rule-based segmentation, the three main advantages of the analytical cluster segmentation approach are:
- Practicality
It would be practically impossible to use predetermined rules to segment customers over many dimensions accurately - Homogeneity
In cluster analysis, variances within each resulting group are minimal, whereas rule-based segmentation typically groups customers who are actually very different from one another - Dynamic clustering
The cluster definitions change every time the clustering algorithm runs, ensuring that the groups always accurately reflect the current state of the data
How to use cluster analysis in marketing.
Customer segmentation:Dividing customers into distinct groups based on common characteristics such as demographics, behaviour, and spending habits.
- Basket analysis:
Identifying patterns in customer purchases to inform product recommendations and cross-selling opportunities - Churn prediction:
Grouping customers based on their likelihood to leave or cancel a service allows for targeted retention efforts - Direct marketing:
Segmenting customers into groups for personalised direct marketing campaigns - Ad targeting:
Grouping customers based on their online behaviour, interests, and demographics for more effective ad targeting - Lead scoring:
Prioritising leads based on their likelihood to convert into customers allows for the more efficient use of sales resources - SEO:
Cluster analysis can be applied to SEO data to identify patterns and make informed decisions for website optimisation
Make your marketing efforts predictive
If you don’t research and segment your market and customer base, time and effort may be wasted on activities that simply don’t resonate with your potential customers. With cluster data analytics, we can help you determine which marketing strategies and actions have the highest probability of succeeding, giving you an unfair share of the market.
