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.
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.
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.