Why? Here are five reasons:
Data quantity - MCE can cope number in cambodia with millions of data records.
Structured data - it supports a fully relational data model.
Data queries - SQL allows for precise data queries and accurate segmentation.
Programmatic support - getting technical here, but programmatic support is advanced.
Making data actionable - there are multiple ways to use your data in MCE.
For the full explanation, keep reading or click on the reasons above to skip to that section.
1. Data quantity
First of all, any Marketing Automation Platform (MAP) that can't store all the data you want to use will limit your ambitions when it comes to marketing automation.
The way I like to look at data in a marketing automation context is to see it as a pyramid whereby your raw data is the base, the middle section is the data after it has been cleaned and categorised, and the tip is the insight gained from the analysis of the data. So, if you can't store all your raw data (the base), then eventually this will limit the insight you get (the tip).
Marketing Cloud Engage is built on a tried and tested relational database model that has been proven, over the years, to effectively support the processing of millions of data records. It can cope with millions of data records with relative ease and within minutes, unlike competing MAPs that, in worst-case scenarios, can take many hours to process data.
2. Structured Data
When building a data model to support your marketing operations you need to know that it is able to reflect your business needs. Moreover, you need to know that it can also be adapted to the way your marketing team works.
In this regard, Marketing Cloud Engage leaves the competition in the dust. The reason? Well, Marketing Cloud supports a fully relational data model.
This means that you can build related tables of data that can be reasoned over to get the exact data you need at the time you need it. Or more simply put, you can ask sophisticated questions of your data and it will return answers that make sense.
For example, I can create a table to contain records of all purchases made, and when I need to, I can ask the system to return me all purchases made by a particular customer - even though the purchase records do not contain any specific information about that customer. We can do this because we create a table for the customer data and then use the unique id of the customer to link it to the purchases table.
The alternative to this is to have a flat customer record that contains their purchase information, but this makes no sense as you do not know how many purchases a customer might make so, therefore, you don't know how many fields you need to add to the customer table.