20% of revenue.
$32,000 per sales rep.
That’s just a quick snapshot of what your business could lose due to bad, inaccurate or poor-quality data.
But how can this be avoided? How can a business ensure that data collected will make an impact in each part of its marketing plan, including budgeting, campaign planning, audience analysis and reporting? Keep reading for 6 key approaches you can employ to measure your data quality.
Collecting data in digital marketing is crucial to understanding your client base. It also optimizes marketing techniques in order to reach your clients in an impactful way. But what are the best ways to analyze data quality? How can we make sure it is accurate, trustworthy and relevant for both campaigns and projects?
It all begins by understanding what exactly marketing data is. MTS defines marketing data as “the pool of information extracted from various touchpoints and interactions between a customer and a brand.”
Here Are 6 Ways to Measure Your Marketing Data Quality
The truth is, the likelihood of data being perfect is near zero. However, there are a number of parameters you can use to make sure that the quality of your marketing data is not only strong but also relevant and useful. The weight of these parameters may vary with regard to your organization’s objectives and intentions. However, it is crucial to be aware of and consider these criteria according to your goals.
With that in mind, here they are in no particular order – six great ways to measure data quality.
This is all about how much of your data set has been populated. For instance, when you send out a survey and only 50% of respondents complete it, then the survey is only half complete. A great way to ensure completeness in your data set is to record all data sets as well as data items.
Unique data is crucial when it comes to creating highly-targeted campaigns that feature personalized messaging for your clients. The first step to making sure data is unique is to purge your system of identical entries from two or more data sets. For instance, cleanse your mailing lists of any repeated entries.
This refers to the age of data. In many cases, the accuracy of data over time depends on the type of data it is. A great example of this is addresses. While it is reasonable to come to the conclusion that an address may remain the same over a course of one or two years, would that still be the case after three or four years?
The pertinent question here is – is your data correct? This is a great question to ask periodically because data that may have been correct last year could be wrong this year. Because of this, accuracy in data can sometimes be closely linked to the timeliness of data.
Not to be confused with timeliness, this is the manner in which data is recorded across media. Is it possible to compare and utilize your data in a way that helps meet your goals? Ensure you record all your data in the same way and that it can be used and treated as a whole.
Is data inputted in a certain format or field valid? It’s important that the data collected is appropriate for the field in which it is placed to improve the accuracy of your data set. For example, if you ask for an email address on a form, and the entry given is ‘abcdefg’, that is not a valid piece of data.
In conclusion, in most, if not all cases, data is rarely perfect. It’s imperative to balance managing data with actually using it. Performing regular data quality audits is key, especially when your organization collects new data sets periodically. As marketing experts, we excel at measuring data to make sure that your marketing projects are impactful and successful. Get in touch with us today.