Now a days, data has become the base of all business activities, getting more vital than ever before. However, to make the most out of data in marketing automation operations, we must ensure industry best practices to sort out and use appropriate data.
In this article, we will dive in to find out how junk data can be harmful and what to do to set up clear rules for data where we can clean and standardize the data to increase the efficiency of business data-reliant teams.
Data Standardization Process
It would be significant to follow the automated data standardization process for cleansing data, which is time efficient and effortless. It is a best practice to build data standardization programs to automate the data cleaning process. It automatically checks for any unclean/junk data in the contact fields and normalizes them daily.
It is advisable to have a particular set of picklist values for the essential fields such as Country, State, Industry, Job role, etc and no other variation, thus resulting in an easier segmentation process.
Lead Scoring
Lead scoring is one of the crucial approaches that most of the marketing and sales team use. It is used to determine the willingness of a lead or potential customer to make a purchase.
Eloqua, uses its lead scoring model that gives scores based on the specific values present on the contact record.
To assign an accurate score to the leads, clean and standardized data should be present in the contact field values.
Data Personalization
Today, field marketers compete to provide an exclusive experience for their customers. Personalized marketing produces a better outcome in making ROI when compared to the generic marketing campaign to the needs of their customers.
Add personalization fields such as Firstname, Lastname, etc, to the marketing emails to grab the customer’s attention. Hence, access to clean and normalized data is essential. Integrating and implementing it in marketing automation software takes a significant load off and increases performance.
Data plays an important role in the firm and decision-making of any organization that knows how to leverage it. In addition, clean data is crucial for task automation which would be rather tedious to do manually. Managing the clean data by following standardized and automated approaches would increase the efficiency of all other processes heavily reliant on data.
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