After running email campaigns, performance metrics start flowing in. There is a gratifying feeling in seeing open rates and click-through engagement. But there are other metrics that indicate the success, or even lack of success, of your email campaigns; yes, I am talking about bounce rates. High bounce rates are the bane of any email marketer’s existence, but they are very common.
So, how you can reduce bounce backs and see better ROI from your email marketing campaigns? The answer may be simpler than you think.
Have you ever launched an email campaign only to find out that most of your emails never even made it to the intended target due to soft or hard bounces? Have you ever spent a large amount of your budget on syndicated content and then come to find out that your target audience is from a database that was collected from tradeshows and POS systems where a consumer’s email is captured with no verification? How can you determine if those email addresses were correctly captured? Whether the information was wrong at point of capture, or became outdated, these issues ultimately contribute to your company’s email reputation.
This past week I had the pleasure of representing Experian along with three of my colleagues in Chicago at IRCE (Internet Retailer Conference + Exhibition). The show ran the full gamut of the world of online retail with Ecommerce industry leaders representing many large online retailers as well as small retailers looking to embrace the next step.
If you ask senior leaders of your company if they consider data to be an asset, chances are that they’re going to say yes. If you ask them how confident they are in the accuracy of that data however, the answers quickly start to vary.
It’s not uncommon for challenges that are considered “just the way it is” to be truly simple fixes if you attack them from a data quality standpoint. Doing a cleanse of your customer database can make a world of difference for your customer communications, customer experience, and cost control.
This blog post is the third and final post in a mini-series we are calling The art and science of matching your data. In the previous matching articles, we talked about the fundamentals of data matching, and both the art and the science of building matching rules based on the context of your end goal. In this final section, I want to discuss some of the more advanced aspects of record matching, and how they can provide business value.
This blog post is part one of three in a mini-series we are calling The art and science of matching your data.
Matching is a term used commonly throughout data management, but it is also known by several other terms: linking, deduplication, joining, aggregation, and so on. For the purposes of this discussion, let’s define matching at the process in which I can determine a relevant association between two or more individual data records.
We live in an era of healthy living (whether we like it or not). Much to my dismay, I find my doctor constantly telling me to eat more fruits and vegetables, whereas I would rather be eating a cheeseburger and fries. And that’s not all – drink more water, cut out carbohydrates, take the stairs, get more sleep – it’s endless! The reality is, my doctor is right—and if I want to live a long and prosperous life, I need to take a comprehensive approach to my healthy lifestyle. Eating a green bean occasionally isn’t going to do the trick. I must see how I can incorporate as many aspects of healthy living as I can into my everyday life.
“Boo!” Is that a ghost or ghoul? No—it’s something much spookier: bad customer contact data. Did you know that less than half of retailers trust their data to make important business decisions? In fact, 57 percent of retailers say that they rely on educated guesses or gut feelings to make decisions based on their data. While blood and guts may have a place in horror movies, gut feelings are simply not enough to go on for important business decisions. Accurate, reliable data to drive decision-making is a far stronger retail strategy.
We've used this analogy many times here at Experian Data Quality, but that's only because it makes a lot of sense when referencing data standardization. What analogy am I talking about? The one where we discuss how a robust data management strategy relies on a methodical, step-by-step approach—much like how you'd approach building a house.
So you’re tasked with your organization’s next big data migration. Maybe you’re moving to a new CRM system. Maybe you’ve just acquired another company and need to integrate their data into your system. Whatever the reason, data migrations are critical processes that many businesses go through. The continuing explosion in the volume of data businesses collect, store and use suggests that the trend of most companies engaging in some data migration project isn’t letting up anytime soon. According to a recent data migration study, 91% of companies engage in data migration projects.