If your company is like most organizations, one of your biggest headaches is your database. Underpinning everything the sales and marketing departments do, a database can make or break your growth. Yet databases often get very little attention.

I get it. Analyzing, cleaning and appending your customer relationship management (CRM) or marketing automation data is mind-numbing, daunting and super unsexy. You can either kick the can down the road and face the constant scrutiny from sales or fix it and get on with life. The reality is you don’t have much of a choice if you want to get the most out of account-based marketing (ABM), hybrid ABM or middle-of-the-funnel (MOTF) and bottom-of-the-funnel (BOTF) campaigns.

I wrote about the growing data integrity problem in Forbes in 2017. Now I’m going to share ideas about how to fix it, with a look at the four-step process we use with our clients.

1. Gap Analysis

Before you start “fixing,” you first need to understand what to fix. The place to start is by evaluating your database’s health with an understanding of what and where the gaps are. You should answer questions such as these:

  • How well does the database represent your target markets? We once had a client that wanted to target Fortune 1000 companies but had a database that was 80 percent small and midsize businesses in nonstrategic markets.
  • What percentage of your database is made up of duplicate and dead accounts and contacts? Our company appears in various databases as NBS, New Business Strategies and NBS Consulting Group Inc., not to mention various odd permutations that are clearly the result of typos. Likewise, my partner’s name often incorrectly shows up as, among other things, Jim, Kenneth, James and KJ — which irritates him to no end.
  • How accurate and complete is the account and contact information? You might be surprised at how often basic information is inaccurate. We moved our offices to a new location three years ago, and we still receive marketing materials with the old address.

The other area we like to test is family tree data, which is especially important for B2B clients with “land an expand” sales models. Here the gap analysis should focus on the percentage of accounts with family tree data and when that data was last updated.

Related Article: Clean Up Your Messy Marketing Data

2. Prioritize Cleaning

Data cleansing efforts follow the law of diminishing returns because it is unrealistic to expect to clean all of your data. Some data elements are just more important than others, and you should clean those first.

Evaluate all the data elements that you currently have and prioritize them. Is “industry” or “NAICS code” more important than “employee size”? The best way to address questions like that is by partnering with your inside sales and field sales teams. The outcome should be a prioritized list of data elements.

We also recommend that the team agree on rules for standardization or normalization. Simple little things like not clarifying whether to spell out “Drive” or use the abbreviation “Dr.” can start the slippery slope of bad data. Fortunately, today’s popular CRM systems and systems like LeanData help standardization of certain data elements through matching and fuzzy logic, but they can’t do it all — nor do they make up the rules.

Related Article: Data Ingestion Best Practices

3. Pick Your Partner

You will need a vendor partner to provide the data needed to correct and append your database. The array of choices is staggering — there are list brokers, offshore companies and vendors like ZoomInfo, DiscoverOrg and D&B — and all of them make similar claims The decision is often made more difficult by internal team favorites and differing philosophies.

Learning Opportunities

At one of our client organizations, the chief financial officer felt the expense of partnering with a third party was unnecessary and said that the database should grow organically to reflect how well marketing was doing its job. At a different client, the head of sales believed in subscribing to multiple databases, under the logic that “what you don’t get from one can come from the other.” Neither of those options is cost-effective, and neither one will produce the results you need.

We developed the following best practices from helping more than 100 clients:

  1. Evaluate partners that have depth in your top prioritized data elements — industry, company size, etc.
  2. More doesn’t mean better. It is better to err on the side of data accuracy and currency than on the size of the vendor’s database.
  3. Ask for a random sample of 50 to 100 records that match your data criteria. Test the sample records for accuracy in emails and phone calls, etc.
  4. Dig into the vendor’s data currency processes. Is the data pulled from multiple credible sources? Does it undergo human validation of all data fields a minimum of every 12 months?
  5. Insist on a day-to-day account manager and data specialist to help you implement and monitor the cleaning and appending processes. Compare the vendor’s methodology against best practices, and ask tough questions; your success depends on it.
  6. Negotiate an error threshold on specific data elements. Under the threshold, the vendor commits to correcting the data within an agreed-upon time frame. Over the threshold, the vendor agrees to a financial penalty with remediation or contract termination without penalty.
  7. Read the contract very carefully so you know what data you own, what you’re paying for and how to terminate the agreement. In a recent project, during a review of one vendor’s terms and conditions, we found that upon contract expiration or termination, all data secured from that vendor had to be deleted from the client’s database. That was a deal-breaker.

Related Article: Ignore This Unexpectedly Bad Data Advice

4. Append

Together with your data vendor, define a work plan and timeline to implement the clean and append. Agree on how the database is to be cleansed and on the process flow for the append, which typically happens in your marketing automation or inbound marketing system. Also identify any other corporate systems that will need to change to ensure the integrity of the database.

Because no company can afford to stop marketing and sales during a clean, tag each record as it is cleaned. Once a majority of the database is cleansed, launch the append to begin adding missing data to existing and new records.

With a solid data foundation in place, you can now look at how you can more effectively support personalization and segmentation and leverage intent data and ABM orchestration to improve conversion or evolve into a customer data platform (CDP) to drive more relevant customer experiences.

Just get on with it.

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