Personalized marketing is proving to be really effective. In fact, In a study of 650 multi-channel marketing campaigns, personalized campaigns consistently and overwhelmingly beat out static campaigns in generating a high response rate from recipients. (Source: MindFire).
Data is at the heart of all personalized marketing – whether print or online. While personalized marketing is proving to be incredibly effective, it can also be quite a risky move for marketers. All it takes is one column in your Excel sheet to get bumped up or down, and you’re sending out a potential disaster for your brand and offer.
Let me give you an example… the other day, I received a beautiful, glossy full color mail piece that personalized a romantic gift – created just for me and my true love. The direct mail piece features a personalized, heart shaped, Swarovski crystal-studded necklace with “Liz” engraved on one side of the heart with my birthstone, and “Alec” on the other, with his. It’s a spectacular example of how data and creative can integrate seamlessly into a one-of-a kind printed piece. Unfortunately, this piece got my attention all the wrong reasons… Alec is my son, not my husband. WHOOPS!
They key to success is to begin with data sourced responsibly. Even if you’re using in house data, following a solid set of quality control processes can help you steer clear of potential disasters.
So, how can you reap the amazing benefits of personalized marketing while avoiding the blunders of bad data?
Sarah Bender, of New Loyalty USA, Inc., a direct mail and fulfillment company, shares a few ways that data mistakes can happen, and offers a few tips on how to prevent it.”These types of mistakes usually happen for two reasons: 1. Excel spreadsheets are often sorted, and the person doing it accidentally highlights the spreadsheet incorrectly and the columns get out of sync – thus the variable data becomes “off”. 2. The merged and printed pieces are not being quality checked thoroughly for accuracy.
A good rule of thumb is to SAVE the original data file. Only sort from a copy of the original. When the data is merged and printed with the variables applied, do a random quality check of at least 20-30 pieces against the original unsorted list. Have your IT department check the Q/C samples as well, have them verify that the variable data fields match back to the original list provided. This is critical since they could have imported the data into Excel and inadvertently merged it incorrectly – providing your digital print and/or press operators with incorrect variable data in the first place.”
If you’d like more tips on how to keep your list in top shape, read tips on building and maintaining a high quality list.
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