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What a Customer looks like

Thu Jun 01 2017 23:02:13 GMT+0200 (CEST), Administrator

What does a customer look like?

In my previous post I shed some light on the key requirements for the Next Gen marketing automation platform.

One requirement is that it is actually a CDP, which means it serves as a unified customer database.

It was mentioned the data model is schemaless, which is a technical term for not having a fixed structure.

Most marketing platforms provide you with a fixed structure to describe a customer. Typically a list of properties such as email, firstname, lastname etc. A lot of platforms even allow you to add a number of custom fields.

Few platforms allow for data types that are not effectively a number or a string of text, while some have implemented an e-commerce oriented approach, which allows for storing order details on customer level.

Imagine you own a car dealership. Your customers buy cars from you and sometimes they have their cars serviced by your mechanics team. When a car is serviced, some parts will be replaced, either because they are broken or because they need to be changed at certain intervals.

As a marketer for the car dealership, you should expect your marketing platform to support a data model where customers can have any number of cars, and a car can have any number of services, and a service can have any number of products.

Your customer contains data in at least 3 nested levels and some would argue why you would need that?

Ask them why not! You have a CDP right? The whole idea is to make data available to you - the marketer. It's your data, you are supposed to and should be able to use it. Think of the after sales flows you will be able to make easily having data at hand.

So what does a customer look like? It is totally up to you to decide - and your decision should be based on what data you have available and/or plan to collect.

The platform is agnostic. It doesn't know about entities such as cars, car service or products used to service the car. It will allow you to define them though, and make you able to use them for querying, decision making, personalize content based on them.

In my next article I will dig a little deeper into the data model and introduce you to the api of FlowStack. The goal is to make everything a little more clear - and also inspire you to start moving data from wherever it is living right now, into your future marketing automation platform.

Please like or share this article, and if you haven't done already: visit FlowStack and subscribe to updates on the development of The Next Gen Marketing Automation Platform.


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