Customer profiling
Why do we profile our customers?
You wouldn’t be the first marketer to make an incorrect assumption about who your customers are or why they are behaving a certain way. Therefore, to move beyond “gutfeel” marketing decisions, customer profiling builds robust evidence and insights into customer truth.
Customer profiling application 1:
Chances are that the top 20% of your customer base return 80% of your profits. Therefore, by gaining an understanding of what sets them apart from the rest of the population, you can focus on marketing to individuals most likely to provide a high customer lifetime value.
By establishing these predictive variables, you can not only improve campaign data selection from external datasets, but you can also find the customers with the potential to buy more from you.
Profile to find lookalikes
Customer profiling application 2:
Not all of your customers will be interested in all of your services. Thus, profiling to uncover the traits of customers buying each type of product can assist in finding opportunities for cross-sell opportunities.
Segmenting a database with the related data points will enable you to focus cross-sell marketing efforts more effectively.
These datapoints might include other recent shopping behaviour, demographics, for example age or income, and their interactions with your business.
Profile to cross-sell
Real-life examples of customer profiling in action:
Market Insights Analyst, Red Energy:
“A strong relationship has been formed between key personnel from Dataphoria and the Red Energy team. Through building a thorough understanding of the Red Energy business, solutions have been developed to answer critical business requirements over 8 years.
A range of services have been provided, including:
- Profiling customers that are churning to competitors to understand the relationship between various geodemographic segmentation variables and those individuals leaving to specific competitors
- Establishment of actionable ways to market to consumers based on key life-stage triggers and projected energy consumption”
Key benefits:
Insights pertaining to the demographic and behavioural make-up of your database provide a deeper understanding of your customers.
Each segment can be indexed to a larger cross-section of the population, highlighting what makes it different from the wider market without statistical bias. These indexes can be seen as a score for difference.
Predictive selection variables might be established for acquisition / cross-sell campaigns. This carries benefits of reduce marketing wastage and improved customer experience. This is because offering more of what they want is known to build customer loyalty and interest.