Data normalization. It’s not exactly what gets marketers excited to get out of bed in the morning. But if lead generation, reporting, and measuring ROI are important to your marketing team, then data normalization matters. A lot.
In this blog post, we’ll review why data normalization is so critical to marketing strategies and goals. But first, let’s define what data normalization is.
What is data normalization?
At a basic level, data normalization is the process of creating relativity and context within your marketing database by grouping similar values into one common value. Any data field can be standardized. General examples include job title, job function, company name, industry, state, country, etc.
While this may sound simple enough, data collection can complicate things. Here are three common normalization problems:
For many organizations, most prospect and customer data is collected through web forms. This means two prospects who both have a job title of “Director of Sales” could fill out the form “Dir of Sales” and “DOS” respectively. Without normalization processes in place, the data will not reflect this commonality.
Collecting business cards at trade shows is commonplace for marketers. Fortunately, there are plenty of data extraction technologies that can pull and convert information from “hard” sources into “soft” (digital sources). Still, even if teams preach about manual due diligence, once these records are uploaded, normalization issues can easily arise after inputting these data sets into a larger database.
Manual or “batch uploads”:
Finally, sales reps do their own outbound prospecting. If they connect with a qualified lead, they’ll need to manually enter prospect data. Not only is this inefficient, but it also leaves plenty of opportunities for human error in a number of critical fields.
Why should data normalization be top-of-mind for B2B marketers?
Data management processes hinder standardization. For the skeptics who ask “does this really matter?” the answer is yes; perhaps now more than ever.
Once collected, lead and account data interacts with a number of technologies that automate sales and marketing strategies and reporting. Two common examples are a CRM and marketing automation platform.
In fact, 56% of enterprise software decision makers have implemented a sales force automation solution (CRM) and 53% have implemented an enterprise marketing solution. And the adoption is of these technologies is growing; an additional 20% have plans to invest in SFA and enterprise marketing automation solutions in 2017 (source).
There’s a reason for the rampant market adoption. The strategies and capabilities that marketing automation and CRM enact have proven to be successful:
- 67% of B2B marketers say that lead nurturing increases qualified sales opportunities throughout the funnel by at least 10%.
- Even better, 15% say the opportunities have increased by 30% or more (source).
- Similarly, almost half of CRM users have said customer satisfaction was significantly impacted by their CRM (source).
Unfortunately, when companies decide to pour resources into sales and marketing software, they base purchase decisions on outcomes, rather than enabling capabilities. Although inconsistent data isn’t the sole cause, it’s worth noting 85% of B2B marketers using marketing automation platforms feel they are not realizing the software’s full potential (source).
To illustrate the point further, let’s pretend you’re in charge of marketing at a company whose software helps improve customer service. You sell into a number of industries but predominantly serve the financial service professionals. Your primary user is an account manager and the buyer persona is the head of account management. Using this fictional scenario, here are common capabilities within sales and marketing technologies that depend on standardized data sets.
1. Real-time personalization:
Successful data-driven marketers cite “personalizing the customer experience” as the most important objective within their strategy (source). Marketing automation helps to realize this goal through capabilities that trigger nurture campaigns based on a lead’s behavior and demographic and firmographic details.
Going back to our scenario, let’s pretend that, because of regulations, your content strategy and messaging drastically differs while engaging prospects at commercial credit unions vs. investment banks. The only problem is, when leads fill out forms, they use a variety of abbreviations to refer to their industry. For example, one person could write CU and another could write Credit Union. We know they mean the same thing, but a marketing automation platform cannot intuitively connect this commonality unless specifically told to.
2. Lead scoring & routing:
Studies show companies using lead scoring had a 77% boost in lead generation ROI over those not using scoring (source). Again, marketing automation and CRM help facilitate this process, but only if they have usable data points to score and route leads.
Let’s check back in with the fictional marketing team to see how this plays out. Because the head of account management is the decision-maker, you score and route them differently than an account manager. Inconsistent inputs can erroneously score and route leads to the wrong reps. Not only will sales become frustrated, they won’t be able to effectively prioritize lead follow up.
To reiterate, the over-arching goal of data normalization is making data more usable. And as marketing owns more of the revenue funnel, proving ROI from campaigns and investments becomes that much more important. In fact, 93% of CMOs say they’re under more pressure to deliver measurable ROI.
To make matters worse, half of all B2B marketing executives find it difficult to attribute marketing activity directly to revenue results, which impedes the ability to make budgeting decisions (source). Consistent data creates relativity and reliability.
If your sales and marketing initiatives are impacted by inconsistent data, contact ZoomInfo today to learn how we can help you clean up and manage your B2B contact database.