What is Data Enrichment And How Does It Enable Marketing Personalization?

How to use data enrichment to retain customers and grow

CRMs and other customer database systems are usually the foundation used for email marketing automation.

Most databases track essential user information but don't incorporate behavioural data. Adding these behavioural data points can enable better, more valuable automation.

Adding behavioural data points isn't easy but is well-worth it. Most customer activity data is big data that lives in cloud storage like Amazon S3, Azure Blob, etc. So, in order to use this data, it has to be pulled out, structured, formatted, analyzed, modelled, and then the resulting, processed data points are added to the customer database.

Value of Data in Marketing Automation

However, adding just any behavioural data points to the database isn't of much value. The goal is to think of user-level data points that are actionable for your business model. For example - you can add a column that says how many app sessions a given user has had this week. But, the question is - how usable is this data when used in  marketing automation?

Be precise when leveraging data

So, data enrichment should be approached with ruthless precision. Early on, identify what kinds of segmentation your marketing department would want to have in their communication efforts. Then, work with the data team to ensure that these data points are generated.

Data Enrichment Example

Data enrichment can be created through traditional data processing and analysis. In this case, customer metrics are aggregated into actionable data points. An example of such a metric is a customer's cumulative value. This value-based segmentation is very useful, because you can tailor communication and offers based on customer value. High value customers can get entered into some loyalty programs and lower value customers can be nurtured.

Machine Learning and Data Enrichment

Another option is to perform data enrichment by using predictive values generated by machine learning. An example of this type of data enrichment is the probability that a given customer is likely to leave the company (aka attrition / churn likelihood). You can then leverage such information to trigger an email that ensures that the customer is reminded of the value of the company, etc.

How predictive data enrichment can be used

Thank you for reading!

Also, if you would like to learn more about the use of AI in customer retention, don't hesitate to contact us.

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