10 Nov 5 Hands-On Strategies to Improve Data Quality
Data quality is never an accident. However, most companies discover data issues randomly. According to the 2015 Experian benchmark report, “Most organizations say data quality issues are detected when reported by employees, customers or prospects, and fewer than one in two companies conduct proactive data audits to discover data quality issues.” It’s time to be more proactive and plan for data quality. Here are some hands-on strategies to improve data quality in your organization.
Ideally, use Marketo forms (embedded or native), or a server-side form post for lead generation. Leverage restricted values, field validation, or field pre-population to allow users to update their information on the form. However, do not allow the email address field to change on the form — this would erroneously create a new lead record. Instead create another custom field to capture the updated email address with a prompt to “Update Email” and run a smart campaign on the backend to change the value on the existing record.
Sales teams often create leads or update records with new information. Make sure that they complete the necessary fields to ensure accuracy and consistency. Incomplete date entry may be due to a lack of understanding of how it will be used. For example, the email address may not be as important to the Sales teams as the phone number, but it is critical to Marketing, since it’s used for communication, and serves as a key identifier in the Marketo Lead Database. Conduct training with Sales to increase awareness of the necessary data points, avoid text fields in your CRM, and instead, leverage pick lists. If you don’t want certain fields to be updated, set them to read-only.
CRM synchronization can often be overlooked by both Marketo and Salesforce administrators, however, the implications can be costly. If a net new lead fails to sync, the lead will remain in Marketo only, and will not show up in Sales views and queues. Failed sync for an existing lead will cause any new information to be lost including data field updates (such as additional profiling, product interest, etc.) or Requests for Information (RFIs). Sync failures can be a result of many issues, such as performance slowdown, bandwidth limitations, field-level visibility, non-matching validation rules, etc. In Marketo, you can subscribe to receive automatic failed sync alerts (in your Notifications section), but I also recommend requesting top syncing fields report from your Support Engineer and setting up a smart campaign, and a static list with daily reports that uncover any leads with failed sync from the previous day. Troubleshooting those will help pinpoint and fix the issues quickly.
Duplicate records are typically identified by email address; however, in B2B instances, you can set up an additional control by using the prospect’s full name. Duplicates cost you money, confuse sales, and break marketing automation process. It is best to catch duplicates immediately and sometimes deleting this duplicate record – before it has history and activity associated with it, could be the best solution. To do this, set up trigger alerts to receive notifications automatically. Run weekly reports with the records of existing duplicates and investigate sources and processes that generated duplicates. This way you can both prevent, and fix systemic issues. If you ran your reports and found a massive number of duplicate records, use a simple tool to set up logic for your master records, and perform a one-time merge action quickly.
Data normalization is important because data is collected from various sources and may include a variety of spelling options, e.g. spelling of the United States as U.S. vs. USA. This directly impacts smart lists, scoring, segmentations, etc. As humans, we may see United States, U.S., and USA as the same country, but marketing automation platforms and CRMs see them as three different data points. Therefore, data standardization is crucial to establishing that singular approach to entering a data point. You can set up normalization smart campaigns in Marketo or utilize a third party data normalization tool, such as Data Shield by RingLead. In summary, you have to become the catalyst for this and drive awareness. Implementing data quality processes and strategy will help your organization be more efficient and realize higher revenue. Remember. data quality is not an accident – it takes time and requires a thoughtful approach.
This post was originally published on RingLead Blog.