To run an optimized, data-driven Demand Generation engine, enterprise marketers need a single source of truth when looking for audience insight. Building and maintaining data hygiene practices enables the kind of trustworthy, consistent reporting that is key to data-driven decision-making.
B2B companies are learning the hard way that dirty data comes at a cost: A recent study found that 50% of IT budgets are allocated to data “rehabilitation” efforts. So, what do we mean by clean data? We mean it is accurate, updated, and uniform. Clean data is free of duplicate, outdated, incorrect, or misplaced entries. When analyzed for patterns and segmentation, clean data tells a clear, actionable story about your audience. And this can drive benefits like improved employee efficiency, revenue growth, and sales conversion.
Here are some rules for how to keep your data clean.
Data Should Be Useful
It is tempting to want to gather and analyze all of the data available on your audience. Realistically, though, collecting too much data can be a disservice to your Demand Generation efforts. Simply put, data doesn’t matter if it doesn’t light up insight that enables optimization. The data you collect should align with your business objectives in the sense that it helps you make better decisions and optimize performance around a common goal.
So, when it comes to collecting data, fewer fields are often better than more. If a data point isn’t going to contribute to insight on converting your target audience and driving revenue growth, leave it out. Capture what matters and filter out the rest.
Data Should Be Complete
To make better decisions, enterprise marketers need complete data on the audience they are trying to target. Once you know what insight you are hoping to collect, make sure that you are setting up your data collection to fill all of the fields you need. Avoid this step and you may end up with significant gaps in your data sets, which will hinder attempts to further fine-tune your nurture strategies.
Data Should Be Consistent
Agreeing on conventions for data entry ensures that your data tells a full, consistent story. This means establishing and maintaining a consistent taxonomy to ensure uniformity between records: For example, any use of numbers, cases, salutations, abbreviations, should be standardized. Through careful design of the rules that dictate how your data is captured, you can ensure that your data is accurate and dependable.
Human error is responsible for a significant portion of “dirty” data and contributes heavily to inconsistent reporting. In fact, a recent study found that human error was the biggest challenge in C-level executives’ efforts to establish data accuracy. To counteract the effect of human error in an efficient manner, look for cleansing systems that offer tools that identify duplicate records. Avoid manual entry where possible, and where it must occur, minimize the impact of human error through the use of constraints like drop-down menus.
Data Should Be Well-Defined
Equally important to establishing conventions for data entry is ensuring that marketing and sales align behind a common definition of the key elements of their Demand Generation program. Then, these common definitions must be built into all of the platforms being used to collect and store data.
Take buyer stages, for example. If marketing and sales have different definitions of MQLs, Marketo might tell you that there are 100 of them in the current funnel while Salesforce might claim there are only 50. When this happens, cross-functional stakeholders will find themselves without a single source of truth. Stakeholders will start pulling data from disparate systems and sources, conducting manual data pulls that they use to create their own reports, many of which may contradict each other or tell slightly different stories.
In addition to creating multiple sources of truth, this hampers efforts at reporting and optimization through generating a general sense of doubt about the validity of the data itself.
Data Should Be Updated and Audited Often
Too many B2B organizations have a tendency to encourage the collection of data without establishing best practices for keeping it updated. In a market where B2B data decays at a rate as high as 70% per year, dirty data can be a deadly blow to your marketing efforts.
Build regular updates and audits into your data hygiene practices. With around 30% of employees changing jobs every year, marketers that don’t regularly conduct audits on their databases and remove bounced emails run the risk of nurturing nonexistent leads. Clean up databases by removing contacts that no longer align with your target audience or whose email accounts are bouncing.
In addition, pay close attention to the rules governing your data collection. When creating a data warehouse, set a clear owner and set a schedule to ensure that the rules governing data collection meet your current needs.
For a clear example of what this looks like, imagine that you’ve been tracking leads from social platforms and currently use Instagram, Twitter, and Facebook. You would need a procedure in place to allow for the addition of new platforms should you decide to start using Linkedin.
Data Should Be Governed by SLAs with SOPs
None of these rules matter if marketing and sales don’t agree to follow them. For that reason, establishing clear SLAs (service-level agreements) with SOPs (standard operating procedures) is key to maintaining clean data.
Imagine that marketing closes an opportunity in Salesforce and passes it to sales. Best-in-class SLAs will hold sales accountable for keeping marketing in the loop about what happens with this opportunity. It is easy for sales to fall into a pattern where they fail to do this. In addition to enforcing best practices, SLAs will serve as a reminder that when marketing has a full picture of the activities that actually impact revenue, sales will have better, more sales-ready leads to work with.
Keeping data clean requires vigilance and consistent collaboration between marketing and sales. Following these simple rules, your organization will be set up to make data-driven decisions that tie directly back to increased revenue.