Normalization to Rationalization

Boost Marketing Efficiency and Outcomes Through Data Normalization

Disparate data sources and marketing applications are leading to inconsistent and inaccurate data making it difficult for marketers to keep up with lead requirements and demonstrate marketing’s contribution to revenue.

Marketing teams are used to hearing one thing over and over: “We need more leads!” The task of keeping up with demand never ends, and simply dumping more contacts into your database creates some unintended consequences. We all understand the value of quality data over quantity, but disparate lead sources moving through disparate, disconnected mar-tech applications creates data quality issues. In this instance, data quality refers to the completeness, accuracy, and consistency of data; without data quality assurance, segmentation is limited, and reporting is unreliable. Let’s take a more in-depth look: 


Database marketing is nothing new, and success has always hinged on the ability to segment the database based on the characteristics that define your market segments and buyers. Today, marketers are not only competing against traditional competitors with similar offerings, but they are also competing against the noise, i.e., every other vendor that is marketing and selling to a given prospect. Personalization improves response and conversion rates, but to ensure that relevant content and messaging is being delivered to the right audience, you need to be able to segment your database based on the characteristics that matter most. 


Leads generated via PPC are usually intent-driven, i.e., the prospect is “in-market” for a particular product or service offered by the advertising company. Engaging with prospective buyers quickly not only sets the right tone with a prospect but provides a clear advantage. The challenge, however, is that most inbound lead data captured via web forms often include open text fields leading to inconsistent data. Inconsistent data makes it more difficult to segment and route leads quickly to the appropriate sales channels. 


Inconsistent data, data silos, and marketing applications that are not connected make it much harder to provide accurate reporting. Marketing teams need to rationalize program spend, investment in marketing applications, and demonstrate marketing’s contributions to the sales funnel. Additionally, without consistent/correct data: How do you know what percentage of your available market you have in your database? How do you know which marketing campaigns are yielding the best outcomes? Where should you spend your next marketing dollar? Accurate data ensures reliable lead attribution and ensures resource allocation to programs that yield the best returns.

This is where data normalization can tip the scales…

Data normalization is the process of structuring data elements according to standard conventions that reduce data redundancy and improve data integrity. While data normalization is nothing new, automating the process amidst disparate applications, large volumes of data, and varying data sources can be very taxing for any sales and marketing operations team. Most marketers today carry out normalization as part of a broader data wrangling exercise that requires manual effort and creates bottlenecks in the demand-gen process.

So where do you begin?  Here are a few recommendations to consider:

Data normalization should be part of a broader data hygiene process based on business process management principles.

  • Assign a leader to own and manage your data hygiene program; without a clear leader and owner, your program will likely fail. 
  • Business requirements and objectives should be defined jointly by sales & marketing with buy-in from sales and marketing leadership.
  • Conduct a thorough mapping of your demand generation process, including all data sources, data movement, lead routing, and applications.
  • Identify gaps and bottlenecks in your process that are contributing to missing or incorrect data elements.
  • Review your tech stack to determine integration gaps that are causing data silos and hindering lead routing.  
  • Conduct regularly scheduled business reviews to identify areas for process improvement and optimization.

In summary, data normalization is critical to the success of sales and marketing. Data normalization is not a one-time event but should be a continuous process that rolls up into your data hygiene program. To ensure success, assign a leader, document your existing processes, develop business requirements with buy-in from sales and marketing, and schedule recurring business reviews to identify areas for improvement. While the process seems daunting, inaction will only compound the issues; the longer you wait, the more your data decays and limits your demand gen output. If you’re interested in further guidance, contact us to learn how Vertify can help improve your data hygiene process and, ultimately, improve marketing outcomes.