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Showing posts with the label data decay

Have you Tested and Validated your data

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Many businesses fail to test their data before and after the data vendor has added, appended or updated it. The ideal approach should be that once the data is cleansed and you have zeroed in on a vendor, run a test. Select a random sample of 5,000 records and ask the vendor to run them through the data append process to append the elements you want. This test will let you get an idea on how good the match rate will be for your full file. Matching will never be at 100 percent, but the test can let you know if the rate will be enough to produce a useful file in the 60 to 80 percent range. And, because not all data elements are likely to be appended to all records, you will be able to determine how much coverage you will get of the data elements you want the most. European Lists provides a Free Trial test to show the accuracy rate, the turnaround time, the details of the data and the support we can provide in helping you interpret the results. You should ideally validate the re...

Know the future of your data, before you enhance its past

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Many a times data is enhanced without understanding the end purpose of the data. It is important to first know what is the data being used and then get ahead with enhancing it. If you are planning to use your data for tele-calling, to invite your clients either to attend your seminar or event, then there is no point in enhancing the entire data. Just getting the phone append service on your contacts which are less than 4 years old with your company makes sense. Think through whether you want to create models and target customers by identifying the ones most likely to buy your products, or just screen customers on age and income for new offers. This brainstorming will help you decide whether you need household level demographics such as age, income, household size and educational level or Zip code-level or census tract-level data about consumer behavior. These decisions will let you seek out one or more data vendors that can supply the right data to your marketing service burea...

Clean and Repair Your Own Data

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Data gets decayed pretty fast and data append is something you cannot get away from. But is it practical to get your age old database updated through data append service. The answer is no. Sticking to more recent customers, who are not more than 36 or 48 months old association with your company makes more sense. Why do you really want to spend money enhancing the records of customers who haven’t bought from you in years? You can standardize your file yourself with postal and data hygiene software or send it out for appending services team. You want first and last names, first and second address lines, states, Zip codes and phone numbers in their proper fields with the same formatting you would use for mailing. Even if you don’t mail your file now, but might want to, performing postal standardization and change of address processing will make your file cleaner and more usable, and it will improve the match rate to the third party data. Respect private information and your c...

When you plan to change your data service provider

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Before making that transition, make sure that you seek answers to certain important questions. Do just listen to what your peers say or what the website does. You make a clear check, before hand. Here are some of the questions you need to get the answers from your data provider. What is the degree of flexibility of the data formats - You have been comfortable using a certain format for your business. What if your data provider uses something else. Don’t get stuck in converting those files to the most compatible format. You have to seek an answer to this question, even before you share you data with them.  Check on their Analytics Expertise - Does the supplier have an internal analytics team? Or do they rely on mass-produced segmentation systems? If you want a custom built solution that will achieve the highest ROI for you, you’ll want access to a team that can customize anything from a simple good customer model to a sophisticated regression model.  Time take for th...