TABLE OF CONTENTS

• 1. Introduction
• 2. The Graph
• 3. The Categories
  • Green Categories
    • 1. Deliverable
    • 2. Corrected
  • Yellow Categories
    • 1. Duplicated
    • 2. Missing First Name 
    • 3. Suspicious Name
    • 4. Unverified 
  • Red categories
    • 1. International 
    • 2. Undeliverable 
• 4. Where to find further Information
  • 1. During mailing creation
  • 2. After mailing creation
    
    

1. Introduction

When uploading a set of addresses, the platform automatically sorts through the data. Before you can send over the design to our Operations team, you will be required to have a closer look at the breakdown and confirm your choice. This way, we ensure that your mailing will be sent to as many recipients as possible while keeping the print- and postage costs as soon as possible. This article gives a detailed explanation of all categories and elaborate on the selections and their consequences.

2. The Graph

On the left, you can find an automatically generated pie chart to give you all the necessary information on first sight. The blue outer ring displays how much of all addresses uploaded will be sent. If you change your selection on the right-hand side to in-or exclude certain categories, the blue ring changes automatically. 

3. The Categories

The list on the right-hand side can be roughly split into three major categories:

• Green categories (Deliverable, Corrected) — Addresses in this category are always included

• Yellow categories (Duplicated, Missing First Name, Suspicious Name, Unverified) — Addresses may be included, depending on your choice
• Red categories (International, Undeliverable) — Addresses in this category will always be excluded.

Let's have a closer look at those tags one by one!

Green Categories

1. Deliverable

 This is the standard result of the address review. Most of your addresses should be classified as part of this category. Being “deliverable” means just that: Without any correction, adjustment, or question, these addresses reach the intended person.

2. Corrected

Some addresses may just need a little help. Those include small typos or a postcode that's slightly off. Checking the uploaded addresses against our internal database, we were able to identify and alleviate the issue.


There's no doubt that these addresses, with our without our assistance, constitute a valid address.* Therefore, there is no way to exclude them in this step. If you notice unwanted data in those categories, please remove the addresses before uploading them to the platform.

Yellow Categories

1. Duplicated

This is by far the most complex category we offer. Generally, you want to avoid sending two letters to one and the same customer. Not only would that result in extra printing - and shipping costs, it's also not going to go unnoticed by the customer and may not leave a great impression. We try to address this issue with the filter “Duplicated”.

When checking for a duplicate entry, the system first checks the address: Street, number, postcode, city, country. Only if all of those are a 100% match, the system checks if the names are at least a 90% match. If so, the first entry will be used and the second entry will be discarded. 

Why 90% and not 100%? There may be small variations in writing or format, which may lead to small differences. Let's have a look at an example:

Philip Mustermann
Philipp Mustermann
Phillip Mustermann
The second and third version of the spelling are similar enough to the first name to be treated as identical, 93% identical to be precise. Even though it's not a full 100%, we want the name to be excluded, as we're reasonably sure that the entries lead to the same recipients.

Unfortunately, the system is not infallible. Especially when it comes to special characters which may not be entered, stored, or formatted by different systems leading up to the upload, we may see small variations which are not caught. Amèlie and Amelie are judged to be different names, and so are Jörg and Jorg. 

However, if there are duplicates in your upload, it's typically not many, and we recommend including them. If you see a noticeable number of duplicates, especially with other yellow or red categories, ask our Operations team for an address check to ensure the continuous health of your data!

Additionally, there's one more exception where we would recommend strongly to leave in duplicates — if you're bundling use cases with a small threshold to make use of a quicker turnaround. If you included both birthdays as well as new customers in one mailing, for example, one and the same address may happen to appear in both lists. As always, it depends on your campaign, but usually it makes more sense to include both mailings.  

2. Missing First Name 

Thankfully, we now arrive at a more intuitive category. Sometimes, customers don't give a first name, or they state a company address here. Leaving the results of this category in your mailing means that “Mrs. Miller” or “Local Business, c/o Mrs. Miller” will both receive your mailing, so we recommend you include them.

3. Suspicious Name

 
Names of your mailing are checked against a list of common names in the background. This is designed to catch the “John Doe” or “Abraham Lincoln” of your list, which may be more or less common, depending on the source of your data. If your addresses exclusively come from a digital raffle or newsletter signup, the hits here may be high, and it may be worth excluding.

However, names as an expression of individual character have become increasingly variable and creative in recent years, and you may play to a colourful, diverse audience. Just because somebody's name doesn't subscribe to a certain standard, it's not worth excluding them from your mailing. Therefore, we strongly recommend including these results. If you're unsure, please address your CS manager or the support, and we'll be happy to address any doubts or worries!

4. Unverified 

This is a unique filter, as addresses here could go either way. We're checking your data against a static list in the background and trying to check if the combination of street, number, postcode, and city is likely to be delivered. This seems like a straight-forward answer, but the literal landscape is constantly changing. New roads are being built, neighbourhoods are newly divided by postcode if they grow too large, and streets are merged or renamed completely.

We recommend including those results, but if this category constitutes a large share of your upload, please read below how to get further insights into the reasons for exclusion. 

If there is any data in any of the yellow categories, you will have a choice to either include or exclude the whole batch. You cannot decide on an individual address. To include or exclude a single address line, please do so on your end before uploading or pushing the data.

Red categories

1. International 

Addresses in this category are not directed to the same country as you chose when creating the mailing. If you want to send internationally, please create a new mailing. After creating a name, you can select a country. Please mind that you can't change the country selection after creating a mailing. 

The reason why this is categorically excluded is that chances are, the mailing could not be delivered. But even worse, if it could be delivered, the format for machine-readability or postage may be completely off, leading to quickly escalating additional costs of potentially more than double or triple the regular costs. 

If you encounter issues setting up a mailing for a country of your choosing, please contact your CS Manager or our support! We happily assist you!

2. Undeliverable 

Addresses that are considered undeliverable are just that - undeliverable. Maybe the postcode has too few or nonsensical characters, maybe the street number is in date formate, maybe one or many fields are completely blank. Either way, there's no way to fix the data here to make sure that you can reach the intended recipient with the fractured data we have. 

To save on print and postage, those addresses cannot be included.

4. Where to find further Information

There are two ways to check in detail why some data was included. 

1. During mailing creation

Once the address upload is complete, you will find the following button at the bottom of the screen:

Clicking this link will download a CSV file which you can open with Excel. You will see a long list of the data transmitted. Scroll to the very right to see details about the category and reason:

Please don't hesitate to explore the filters to get details about certain exclusions, like here for the category "Unverified":

This will return a handy list to check specific categories and enable you to make an informed decision.

2. After mailing creation

But what if you finish the setup and wonder, later, why some addresses were excluded? No problem! Simply open the overview of your mailing. Here you can find another download button:

This will download a zip folder with three subfolders: The originally uploaded data set in full, a list of excluded addresses, and the final list used to send the mailing. You can check and filter the Excel sheet of excluded addresses as described above and glean the same information from it.

We hope that this article helps to clarify the different categories in the recipient list. If you have any further questions, please do not hesitate to contact us! We're happy to help!