What is Data Managment Quality?

A consistent marketing automation strategy will be in vain and all your efforts will be in vain if the quality and context of your data are poor. Quality data can definitely make marketing automation successful and help users manage their lead process and build better quality marketing leads.

Your data must be well separated depending on the purchase prospects and industry domain before being used for your automation process.

According to Wynn White, Chief Marketing Officer at FloQast, the data can be categorized into:

Behavioral data - social and web interactions of customers or prospects, likes, and dislikes.

Historical data - known purchase history, support issues, and requests.

360 view of the customer - buyer interaction across all your channels - web, store, direct sales, etc.

However, marketers should ensure that they standardize data from across the organization before attempting database integration to support their marketing automation activities.

Here is some data compliance that marketers should adhere to:

1. Evaluate the quality of available data and its level of reliability and consistency

Data profiles allow marketers to fully understand the problem with the available data and determine what steps they need to take to fix it. Certain data quality assurance tools automate this process, allowing marketers to incorporate their own rules, so that the data is not only validated for quality but also relevant to the specific marketing needs of the marketer.

2. Converting these rules into a process that converts and corrects data into the same format

Corrected and standard customer records ensure it will be relevant to relevant data through other channels and old data collection systems. This will ensure that customer, product, and related historical data is linked to the right people and that any external data can be added as needed.

3. The above process can then be incorporated into the marketing system to automate data validation and correction at the data extraction point and to continuously audit the data for quality inspection to meet the specified requirements.

Following these steps will ensure that marketing systems, support teams, processes, and users have a high level of consistency, quality, and reliability of data that meets their specific business needs.