Targeting a segmented group of consumers in a campaign is essential to its effectiveness.
If our message reaches the right people, an ad is more likely to generate more conversions, lower customer acquisition cost (CAC) and a higher return on investment (ROI).
To achieve this, we must study the behavioral data of the consumer or potential customer of the brand and separate them into clusters, i.e. groups
What is clustering?
Clustering is the categorization of consumer information to generate relevant segmentations that allow us to create more effective personalized advertising campaigns
These are groups of people with similar characteristics: tastes, needs or motivations.
Let’s look at a simple example of a cluster or segmentation:
● Demographics: men and women aged 35 to 50, with children up to 3-5 years old living in the area.
● Interest: children’s healthy eating.
● Behaviors: children go to school during the day, one parent picks them up and they shop afterwards.
● Recurrence: they go at least twice a week in the time slot from 17h to 19h to the point of sale.
● Financial: high monthly family income.
● Launch of a new food product for children.
With these few data we already have the necessary information to create a communication campaign or specific digital advertising based on offers, cross-selling or loyalty campaigns among others, aimed at this specific audience and during very specific periods.
The main goal of the clustering process is to find groups that are different from the others, and that their members are similar to each other.
What advantages does clustering offer to retail businesses?
One of the great advantages of clustering is its flexibility.
When we tracking the performance of a campaign, we realize that conversion is low, its CAC (Customer Acquisition Cost) high and its ROI (Return on Investment) low, we could adapt the cluster to new objectives in order to optimize its performance.
Other benefits of clustering or segmenting consumers are:
● Bringing value to the user and optimizing the shopping experience.
● Helping the customer solve their problems and needs.
● Anticipate the future demands and needs of our target audience.
● Adapt to their consumption habits.
● Automate and manage communication at our point of sale in real time.
Clustering applied to business intelligence
In the area of Business Intelligence, the clustering technique can be used to organize different types of data such as products, customers or stores:
1. Customer segmentation.
This type of technique is used to better understand customers, based on the behavior of consumers when making a purchase and is very useful for sales or marketing departments.
2. Product segmentation
Similar to customer segmentation, product segmentation distinguishes between products that have similar characteristics such as brand or usage. This allows cross-selling strategies to be generated.
3. Store segmentation
Like the two previous ones, store segmentation discriminates between stores based on two main references: revenue and location.
How to apply clustering to digital point-of-sale marketing
That digital marketing is data-driven is no secret to anyone. Nor is it a secret that points of sale have data or cookies off that platforms likeLadorian help you identify and analyze to learn about the behavior of customers and potential consumers.
Through the insights generated by the information generated by the Ladorian Ids Software, we can collect different data in a cluster, generating a high quality group for a given campaign that will allow us to personalize communication, improve the customer experience and increase sales.
Do you think that segmenting your audience can help you increase sales? If so, Ladorian can help you, contact us!