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Melissa Nazar

Melissa is manager of content marketing for Experian Data Quality, spending her days helping businesses learn how to better connect with their customers through better data quality. When she is not correcting other people’s grammar or arguing of the disadvantages of the Oxford comma, she is an aspiring snowboarder, semi-professional kickball player and occasional knitter. Follow her @melnazar.

The real nightmare on Elm Street: Horror stories from your email list

Halloween: A time of tricks, treats and non-stop horror flicks playing on television. I don’t consider myself a scary movie aficionado by any means, but I've watched enough of these films to learn certain facts: don’t ever investigate scary noises alone, kids who see ghosts should be taken seriously and there is never cell phone service during paranormal events.

As a marketer, this got me thinking: horror movies are ripe with certain clichés storylines, just like certain scary themes that seem to recur for marketers. I’m talking about the unexpected, fear-inducing issues that can pop up and derail email marketing efforts, monsters living in your email list that quickly drive your engagement rates and other metrics into the ground.

Having nightmares yet? Check out my list of the most common scary stories we see related to email marketing.

Retailer insights from the Twitterverse at Shop.org

What’s top of mind for today’s retailers? The EDQ crew is back from a trip to Philadelphia for this year’s Shop.org conference, the National Retail Federation’s (NRF) event specifically for digital and multichannel retailers. The floor was busy with conversation, but so was the social sphere: the conference Twitter hashtag #shoporg15 revealed a few common themes and insights on the mind of modern digital retailers today.

Getting to a better data quality framework

To make best use of your data asset across your organization, it is essential to have a solid data quality framework to ensure that your data is accurate and complete. But all data quality management models are not created equal: different organizations have different levels of data quality maturity, depending on organizational priorities and needs.

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