Data normalization. It’s not a far stretch to suggest that the topic isn’t exactly what gets marketers excited to get out of bed in the morning. But if lead generation, reporting, and measuring ROI is important to your marketing team, then data normalization matters. A lot.
Back up, just 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.
And yes, just about any data field can be standardized. General examples include job title, job function, company name, industry, state, country, etc. Sounds simple enough so far, right? Not so fast. The fact is, data normalization gets trickier once you think about how this information is collected.
In this article, we’ll take a look at the nuts and bolts of data normalization, as well as review why data normalization is so critical to marketing strategies and goals.
The Deets on Data Normalization
Yes, the sound of it is somewhat yawn-inducing. We get it. But to dismiss it would be abnormal – especially for someone looking to induce their database with intelligent data for better marketing. So let’s take a closer look, shall we?
When it comes to marketing (and its sales counterpart), there are three common means of data collection that cause the abnormal, or normalization issues:
- Web forms: For many organizations, most prospect and customer data is collected through web forms that have open text fields. 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.
- Live events (trade shows): 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”: Sales reps do their own outbound prospecting outside of marketing activities. If they connect with a qualified lead, they’ll need to input information about the potential buyer manually. Not only is this inefficient, it leaves plenty of opportunity for human errors in a number of critical fields – let alone uniformity within data sets.
Based on the three simple examples, we can agree the various channels and processes by which prospect and company data is gathered hinders its standardization. For the skeptics who ask, “does this really matter?” the answer is unequivocally yes; perhaps now more than ever.
Why Data Normalization Matters (A Lot) for B2B Marketers
Stepping back, let’s consider a typical marketing situation.
After marketing gathers their data, lead and account information is usually stored and applied to a number of technologies to automate sales and marketing strategies and tie back reporting. Two common examples are a CRM and marketing automation platform.
According to Forrester Research, 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 such rampant market adoption: the strategies and capabilities that marketing automation and CRMs enact have proven to be successful. For example, 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’re too often sold on outcomes from rich feature sets, rather than the path to enabling capabilities. Case in point, while we’re not suggesting that inconsistent data is the sole reason, it’s worth noting 85% of B2B marketers using marketing automation platforms feel they are not realizing the software’s full potential (source).
Tackling Marketers’ Most Pressing Objectives
Logically, we know data consistency matters. But how does data accuracy through automation directly affect your marketing initiatives?
Well, 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:
- Real-time personalization: Successful data-driven marketers cite “personalizing the customer experience” as the most important objective within their strategy (source). Marketing automation helps marketers realize this goal through capabilities that trigger nurture campaigns and on-site content 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 that when leads fill out forms, they use a variety of abbreviations in the industry/company type field. For example, someone could write in “CU” vs “Credit Union.” We know they both work at the same type of financial institution, but a marketing automation platform cannot intuitively connect this commonality unless you link these phrases – so you risk selling into the wrong job title! And while doing so may work in select instances, there are a number of other inputs related to a lead that vary too much to do so at scale.
2. Lead scoring & routing: With sound processes around lead scoring and routing, everyone wins. Studies show companies using lead scoring had a 77% boost in lead generation ROI over those not using scoring (source). Again, marketing automation and CRMs 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. Just like the state discrepancy previously described can affect the execution of your marketing strategy, inconsistent inputs can also erroneously score and route leads to the wrong reps. Not only will sales quickly become frustrated, they will not be able to confidently prioritize who to follow up with.
3. Reporting: Because 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 are under more pressure to deliver measurable ROI. To make matters worse, half of 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 about what you’re reporting on.
In a nutshell, the over-arching goal of data normalization entails making data more usable for marketers. So what’s the use in going against the usable?
Interested in hearing more from data-driven leaders who have seen and experienced success in marketing? Attend ZoomInfo’s 2018 Growth Acceleration Summit in Boston, June 18-20, where sales and marketing industry leaders will share their secrets of the trade.
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