The Merriam-Webster Dictionary defines the word visualization as “the act or process of interpreting in visual terms or of putting into visible form.” At Service Objects, we would take this a step further – we define it as “making your customer data instantly clear and actionable.” In this article, we’ll show you some cool examples of how.
Numbers versus pictures
Service Objects APIs return a plethora of results related to validating your addresses, emails, names, phone numbers, devices, and leads. Of course, most of these return key values for fast validation, including the Delivery Point Validation (DPV) for address validity, a validity score for Email Validation, and the quantitative Certainty and Quality scores for Lead Validation. But there is so much more to see.
Along with these key fields, our services deliver an entire payload of extra results, metadata, and flags to provide you with as much insight as possible. These results can seem overwhelming in their natural state as a pile of data, but visually a picture is truly worth a thousand words. Now, let’s take a look at some visualizations of sample data and see how they can help you make informed decisions.
Sample data visualization charts
As an example, let’s look at the distribution of Quality scores for a sample of business lead data along with some other visualizations of the results.
The first chart clearly shows the distribution of Quality scores for this sample dataset, with a majority of leads being flagged as Reject.
Along with the overall distribution, we can see how the Quality scores fared in different countries in the second plot. We see that the countries with the most Accepts are the US and UK, and the most rejects come from Canada and Mexico.
A graph like this can quickly show you which areas of your market need more care and consideration. Another way of considering this data is to look at the category scores and see which data points are causing you the most issues. Looking at the Component Score Quality Distribution, you can see Phone has the highest rate of Reject, indicating that the phone validation results are often returning a lot of invalid or bogus phone data.
We can dig deeper into the results to pull some statistics on the matches and mismatches from cross validation. The Matching and Mismatched Note Codes bar charts show the note codes returned from the service for cross validation that were either matched or mismatched. The Matching chart shows that the highest rate of matches came from names and emails, business names and emails, and names matching phone contacts (when a contact is found).
The Mismatched chart indicates that the highest rate of mismatches came from business names not matching emails and names not matching emails. While this seems contradictory to the Matching chart, it simply indicates there is a large spread in the quality of the email data; some emails have high quality and match across name/business name, while others have low quality and no matching data points. This type of conclusion can inform you about shifting business practices to maintain the highest quality of data possible.
As another example, let’s take a look at the DPV spread for a sample address set. The first chart tells us at a high level the result of the validation results. We can see that the vast majority (85%) of the supplied addresses are valid and deliverable addresses, with a small percentage missing data or using partly bad data (most likely related to a wrong or missing secondary number), and an even smaller percentage coming back as an error (address not found).
