Effective Tips For Postcard, Designing and Printing

Eye-catchy and one needn't open it to read what is written on it. There is no necessity for envelopes that would actually attract the clients to open and see what is inside it. A good customized card would stand apart from the rest of the marketing materials and would reach the message to the customers on time. It can be called a way to attract the customer to either call the company or visit the website which would further lead to sales. They don't have any lengthy set of words that would be boring to the customers. 

They are really short and crisp, and deliver the message in a sentence or two. But, many people find designing a proper postcard as a gargantuan task as they don't know the tips and shortcuts that they can utilize to design them. Though printing them is less difficult and needs proper care and attention, still it is not an unmanageable task. If you want to print a postcard for professional or business purpose, then you must hire a professional custom postcard printing services.

But, while designing and printing customized postcards certain things should be followed.

Tips and Tricks for Better Custom Postcard Designing and Printing

The custom postcards should be visually appealing to the customers. If the custom postcards are printed in full color with stunning and attractive images. But simple, relevant photograph will not fulfill the purpose. The audience should be able to relate to the images and the information you are trying to tell them.

•  These can be called one of the best marketing tools which reach the audience directly taking your message. Moreover, it brings in the positive branding about the product or services you are selling. Try to keep the message unique which should start out in the crowd. If you are not able to do it personally approach a professional who would do it.

•  The words used in the custom postcards should be short, crisp and at the same time attractive. A one-liner is more than enough.

•  Use different fonts, colors and patterns of texts on the card. A headline and a call-to-action by-line would be more prominent.

•  Leave sufficient white spaces. Without it the whole card would look cluttered.

•  Don't leave the address-side completely blank. There should be something really striking that would make the customers turn and read. The address side the first side actually the customer looks into and they should find a reason to turn to the other side.

•  Do not try to close the deal. The card should be in such a way that the prospective customer should call you or visit your website for further queries.

•  Use a cordial and friendly tone and not a demanding or ordering tone, as for most of the people postcards are just personal notes sent from family and friends. So it should be informal and fun-filled. 

Designing For Data Quality Technique

The quality of data across and in your enterprise systems.

Most, if not all, data quality problems are caused by human error.

Approximately 80% of errors are simple data capture errors - users entering the wrong information - with the balance largely arising through poor data integration.

Over the last fifteen years I have delivered multiple data quality audits and assessments, in different environments and, based on my experience, suggest that a few simple design choices can have a dramatic impact on your ability to manage information quality at an holistic level.

1. Plan to capture the User and Date that information was captured, or modified.

Data profiling and discovery tools uncover interesting patterns of behavior in your systems. If this behavior can be linked to specifics users, groups, or time periods then it can be managed.

For example, we may identify that x% of our information has an incorrect link between supplier and product code. We can now go ahead and fix the problem but we have no real insight as to when, or why, it occurred. Data governance, and root cause analysis, require context for our information.

Date of Capture information gives you important context.

Is this an old problem that has subsequently been resolved?

System validation may have improved but we have been left with a legacy of erroneous, poor quality records.

Or maybe the errors can be tied back to a historical event. Do these records link back to the migration of information

from the previous ERP platform into the current one?

Maybe the errors have started recently - have there been any recent system changes that may have allowed users to capture faulty records?

 Similarly, User information gives you context

Can you track patterns of behavior to specific users or teams?

Users will develop certain patterns of behavior, or work around, in order to bypass system restrictions where these are considered to be onerous, or where they do not allow the task to be performed.

For example, a system may require a Client Account ID to be captured before allowing a call to be completed. If the client does not know, or will not share, this information the call center agent, under pressure to complete the call timorously, may capture another Client's ID instead.

Patterns in behavior by specific users, or groups of users, are a key indicator of a broken business process.

Further investigation will need to be done by the data stewards.

Maybe the problem can be tied back to overly ambitious system validations?

Do the users need training or additional support? In many cases, these errors can be solved by education.

Do your user's KPIs need adjustment? Many data quality errors are caused because users are measured on volume of data captured rather than on quality of data captured.

Quite possibly there will be a combination of some or all of these factors.

Designing with data quality in mind means giving context to errors! You may want to add additional information to your systems.

2. Use a "soft" delete / merge

Another issue we may uncover in your information is that of so-called "orphan records" - records that have lost their partner.

Two simple examples - a delivery note that does not have a delivery address, or an order that does not have a customer.

In some cases, these records are simply captured incorrectly - the user accidentally types in a non-existent customer number.

In this case, you can do root cause analysis as per point 1.

However, in many cases this issue is caused by one of the records being deleted after the event. Your user linked the order to an existing customer and, later, another user deleted the customer record.

Deletion and merging are important tools for managing data integrity. If you want to reduce faulty or duplicate records you must give users the tools to sort out these issues.

A deletion is used when a record is no longer relevant. There can be a number of good business reasons to delete a record
- for example, a legal requirement to cease doing business with a particular client. A so-called soft delete provides you with a means to treat the record as deleted, without losing any information.

A soft delete means that, instead of physically removing the record from the underlying database, the record is marked as deleted. This means that users will not be able to access or use that record, but that it will still be available for audit purposes.

A merge is used when you identify that two or more records exist for the same entity. This is an extremely common problem, most efficiently picked up through the use of automated data cleansing and matching tools.

For example, the supplier records for "Mr J Bloggs, CDO at Widgets Co" and "Joseph P. Bloggs, Chief Data Officer, Widgets Company Inc." represent the same supplier.

In order to clean up our system we need to merge these records to create a single, unified supplier records.

A soft merge would link both records via a common key, allowing us to maintain the integrity of all linking transactions, before soft deleting all but one of the set.

Your system should be designed to facilitate soft deletes and soft merges.

Plan to allow the addition of linking keys to group similar or related records, as well as for the use of a soft delete.

When used in combination with a data quality metric program these simple tips provide a solid foundation to solve most data quality issues.