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.
Why is data accuracy so important in marketing?
Without accurate data, all your marketing initiatives miss the mark. When we talk about lead scoring, what we are really doing is identifying and calculating data points. For example, at DemandGen our lead scoring system looks at the size of company, what marketing automation system is deployed (if any), whether a CRM system is deployed, and if it is Salesforce.
These are the essential data points that contribute to our lead score. If we were not capturing those data points, or were evaluating on big open data values—for example if we were looking for a specific job title using a text entry field—that reduces our ability to determine if this particular person fits into our profile. So unfortunately, it’s very easy to have a perfectly good scoring program that still can’t provide the insight you need to make informed decisions.
For lead nurturing, the challenge is to communicate in an automated fashion while giving the impression that we’re speaking to the individual. We can only tug at individual heartstrings through segmentation: using data points to align our tactics, offers, and voice effectively. Moreover, we can’t segment appropriately without knowing a prospect’s stage in the buying cycle: for example, it’s pointless to send a campaign about service costs to a prospect who’s just looking at product offerings. Without good data, campaigns are limited to the “batch and blast” variety, and you won’t be able to benefit from more sophisticated nurturing techniques. Essentially, inaccurate data translates directly to ineffective nurtures—and a lot of wasted effort.
Marketing contribution to revenue
This is all about understanding the ROI of a particular activity. Data plays into this not only because of dollars and cents, but because in order to appropriately allocate revenue you need to know the Who (is this person), What (identified the prospect’s interest), Where (did this prospect come from), and the How (did we associate this prospect with a given campaign). By having this data, we can develop a critical understanding of the Why: why this prospect became a customer. When we know whether a particular campaign or lead source, for instance, is contributing more than others, we know the right places to put our money.
Data drives many business processes beyond marketing
This includes routing and notifications through sales, operations, accounting and more. The validity and success of a wide variety of efforts are often tied to accuracy of marketing data. Data normalization means ensuring consistent capture of the data points that you care about as a marketing and sales organization–and the key word there is “consistent.” An effective tactic for data normalization is to identify the data points that tend to see a lot of disparate values and define a standard set of values to which they can be translated.
Data normalization is a contributor to, but not the same thing as, good data hygiene
Tackling dirty data up front is “a stitch in time” that saves time, effort, and problems later. And it’s not just about data that enters your systems through forms; often, new data like purchased lists and tradeshow contacts arrive in less-than-perfect condition. So it’s best to make data normalization a routine part of your marketing initiatives.