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Quick Steps to Keep your Data Clean

by on Oct 20, 2014 in Big Data Analytics, Data Science, Data Visualization

“Dedupe and clean up your CRM before you connect your marketing automation system. Be sure to have the CRM enforce data quality through dupe blockers, ISO Country Picklists, and required fields” Josh Hill, Marketo Practice Lead

Data is the foundation of CRM and marketing automation systems. Keeping your marketing automation data clean, forms an integral part of managing this powerful system.

The Impact of Dirty Data is Massive

What happens if your data is not clean?

  • 25% of your marketing automation data goes bad each year
  • 10%-20% of databases have errors that hampers your job efficiency

As per 2011 Gartner study, poor data quality lowered labor productivity by 20%. That’s 20% loss of your time, which is wasted in fixing the bad data. But, the actual loss is the revenue loss because of the wrong targeting. Dirty data silently intrudes your organization, creating inefficiency, frustration and reduced user adoption. It affects your reports, workflows and automated processes, which further affects the working of different departments. A colossal amount of time and money is invested into marketing automation and CRM implementation, which is affected with the dirty data.

Impact of Inaccurate Data

Why you should invest in keeping your Data Clean?

  • Good data can help in better segmentation of leads and clients, so that you invest your time on right people at right time
  • It eradicates the duplicate records and avoids duplicate sending of mails
  • Provides accurate, consistent and repeatable reports
  • Removes bad and old leads to stay below pricing verges

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Wondering how you can keep your data clean? By following these simple quick steps you can fix your bad data

  • Standardize your database: Design your workflows and access levels to avoid human errors. You can design the system by implementing some CRM tools like pick lists for standard values, ‘read only’ fields, and include the contact’s role and title. Clear data and standard fields reduces the chances of human errors.
  • Keep one field for one purpose: Do not let sales or leads to use the description or comments field repeatedly for entering unstructured and unorganized data like call notes and history of lead. This can result in entering a variety of data and can clutter your database. Instead create a process to record and timestamp the history.
  • Use automation for accurate data values: Use marketing automation platform to fix data entry levels like misspellings. You can also have a standardized field entry process like your field is company size, only a number must be entered there to represent the employees in a company.
  • Block duplicates: Duplicate data costs money. It could be upwards of $100 per duplicate word. Many marketing automation platforms charge as per the number of records, which makes you pay for the same data multiple times. Make settings in your marketing automation platform that prevents the creation of duplicates. You can apply the settings to your CRM as well.
  • Automate Data Appending: Filling empty fields is very critical to segment your database efficiently. Use third party vendors that plug into your marketing automation platform, and enrich your bad data.

Keeping data clean is no rocket science. The best approach to beat bad data is to have an effective knowledge of data quality, and a reliable data quality vendor who can cleanse and enrich your data.

Grazitti’s Data Management Services

With our deep expertise in Marketing Automation platform, we have helped several clients like Cloudwords, Marketo, SeeControl and many more to achieve accurate consumer data enabling them to achieve best out of their marketing campaigns. To know more about how we can help you to optimize your data efforts, just drop a line at info@grazitti.com

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