You need data for customer profiles. They are the quantitative part of the puzzle.
Customer Profiling is mostly segmentation (with a bit more in it). Customer Profiles describe groups of customers based on known facts or data. Because of this, profiles tend to be more numeric; counts of things, percentages, z-scores, averages, standard deviations, comparatives etc.
They tend to include demographics such as genders, age ranges, locations, lifestyle attributes such as income, family size, and behavioural attributes such as what they purchased, basket size, revenue, what they interacted with, or didn’t interact with, where they came from.
Using data to build profiles.
Your profile will be better when you have the capability to include data vanuatu email list 10097 contact leads from lots of different places; web behavioural data, CRM data, business data and third party data. One of the best sources for third party data is the Experian Mosaic Groups which enable even greater insights into customer behaviours and lifestyles. For these to be appended, you need to have access to the customer’s full postal address.
Customer profiling uses many different pieces of data. They don’t describe customers; they show you exactly what customers are doing.
This is the what. This provides you with the capability to predict what customers will do next, it’ll provide you with the capability to convert prospects into customers.
Most often, customer profiles are going to be used to try to identify new prospects or up-sell to existing customers. By understanding the profile of your existing customer, you can begin to look through your prospects to find ‘similar’ profiles. This can all be automated as well.
To generate profiles you’ll need to begin comparing customer segments with one another. This will give you a certain level of profile. For example, you’ll begin to understand how differently genders interact; how different age groups interact; how much they spend; how frequently they spend. You can do that in many different applications, such as Excel and Tableau. Your web analytics platform can even do that for you too, providing you can load all of the data into the platform.OUTPUT_Purchase_Cluster
This remains a relatively 2-dimensional profile, though, and begins to get really complex when you start to try to understand more than 3 or 4 data points (for example age ranges, genders, traffic source, mosaic group by basket size).
For that you need to use some of the more complex analytical tools such as SPSS and get more into clustering analysis.
When to use them?
As you can see, they are based on different things. And the use of each are very different too.
Use Personas to:
help craft your messaging
inform team members and stakeholders of the motivations behind your customers.
Use Profiles to:
target marketing campaigns to customers or
find new prospects
understand the value and behaviours of each different type of customer.
Hopefully, that will have cleared up some of the confusion for you, our Guide to Audience Models is a more in-depth look at the different types of personas and profiles. It also has a handy audience matrix that you can use as a quick reference.
As always we’d love to hear your thoughts so leave us a comment or get in touch if you have any questions.