The 4 Steps to Data Standardization

Data standardization is a key part of ensuring data quality. Lacking standardization results in bad data, which has numerous negative effects, from sending bad emails, to mailing to bad addresses, to losing customers altogether. Unfortunately, data standardization is often left out of discussions when planning the input and organization of your company data, especially when…

Peek Inside: How Data Normalization Works

What type of data should be normalized? Any open text field may be problematic, but data categories that describe the buyer persona or impact business processes are the best candidates for normalization. Translating crazy data into a standardized list gives you the ability to take actions that otherwise would be difficult or impossible to do properly.

For example, data such as job title, industry, state, country, or platforms/technologies impact lead scoring and nurture messaging, so accuracy and consistency are vital. Some common choices for normalization are job titles, locations, and bogus data.

Job titles. Always tricky! Job titles vary greatly among companies and industries, making it nearly impossible to associate a given job title with anything really actionable for segmentation or lead scoring. So standardizing this value can be very useful, and a number of approaches are possible. For example, we used a look-up list approach in a recent engagement. Job title is typically a combination of department/role (engineering, manufacturing, sales, finance) and level (like VP, manager, technician, analyst, associate). In this case, job levels were more meaningful to the customer’s business process, so we implemented a system that translated open-text job titles into job levels  using a look-up list.

How to Make Your Data More Effective with Data Normalization

A colleague once said something to me that changed the way I think about data. “Data is the fuel that runs your marketing automation engine.” Data is truly a passion of mine, and after hearing that metaphor, I’ve never been able to think of data any other way! So every day it drives me crazy to see marketers developing programs and initiatives that can’t be supported with the data they have, and then asking “Why isn’t my reporting showing the success of this campaign?” 

Data drives all the major initiatives of marketing automation: lead scoring, segmentation, nurturing, and measuring marketing’s contribution to revenue. Here’s the secret formula: 

Normalized Data = Effective Lead Management 

In this post, I’ll look at why marketers have to be passionate about data accuracy, and how data normalization is a key tactic in good lead management. 

CRM Tips: The One Thing You Must Do Before Account Deduping

Deduping a CRM is like an equation waiting to be solved. Just like a high school math, the more variables you know the values for, the easier the problem is to solve and the more likely you will come up with the correct answer.

CRMs include a vast number of “Data Markers.” These markers are a literal road map to filling in missing data. For example, if 100% of the emails for a particular company have the email format of, then you can probably fill in missing emails for other contacts with confidence. If you have the email domains for contacts, but the account record is lacking a website, that can be filled in too.

My data: It loves me…it loves me not

Every year, companies allocate a tremendous amount of time and money on their CRM and marketing automation platforms. Unfortunately the true value in these platforms – the customer and prospect data – gets completely forgotten. We think this is heartbreaking, so we’ve come up with four ways that you can show your data some love.…

Database Normalization Examples

As we all know, database normalization is a process that allows you to organize the data in a database and it allows you to bring in front plenty of amazing mechanics that are more than impressive. On top of that, this is designed in order to improve data quality and bring in front amazing, high…