All posts by Anna Kayfitz

How To Identify A Data Quality Issue in your Marketing
January 29, 2018 Uncategorized

It is not uncommon to clean your data prior to moving to a new tool or when the problem gets so bad that you have no choice but to clean your data. So how do you stay on top of your data and clean it before it starts to effect your bottom line. So how do you become proactive in identifying a data quality issue?

Well let’s begin with describing what good data looks like:

    1. Useful – Your data should be used, if you are collecting data and you are not using it you have an opportunity cost of collecting data you need. Secondly, your data should be usable for segmentation and analysis. An example, of data that is not usable is the collection of job titles without standardization. If you have values that include: VP of Marketing, Marketing Vice President, Digital Marketing VP and so on it is very difficult to deploy emails to VP of Marketing as you have many different combinations. Therefore, you have data that you are collecting that at this moment you are not utilizing. But you could, by simply standardizing historical data and changing forms to include drop down for job titles.
    2. Current – Your data should be up to date. Your prospects move, change jobs, their lifestyle changes and so on. Therefore, your database should change with them. If you do not bring in the same amount of leads that become outdated you will soon be in a declining business. The solution, is to keep track of what data becomes outdated and to make sure you are dealing with old historical data by either updating it with up to date information or removing them from your database.
    3. Consistent – Your data should be consistent so you can use it for your marketing. An example of inconsistent data is dates that have two different date formats: DD/MM/YY and MM/DD/YY making it a mess to deal with. You can fix it by overwritten historical data and identifying where the mis-match is coming from to prevent it in the future.
    4. Accurate – Bogus Data gets collected by companies every day. A customer does not want to provide their real e-mail address because they receive too much spam. You can introduce data governance practices to prevent that, for example verifying the email address right then and there. Another way is to verify data using third party providers that can either fix your data or at the very least you will be able to flag bad data so you do not use it.
    5. Usable by Multiple Departments – Finally, it is vital that marketing providers data that is helpful to other departments. For example, if you are not collecting phone numbers because you do not do any telemarketing. This maybe fine for marketing, however you are doing a disservice to sales, customer service and accounting that may need a phone number. Therefore, you should be on the same page across all departments to make sure that you are working together to increase profitability. A good solution is to make sure all tools are connected. The second solution is to make sure that there is an open communication across all departments regarding data needs.

Now that you know what good data looks like, it is time to see if bad data has already impacted your marketing performance. A quick test is to ask yourself the following questions?

  • Do you find that there are limitation to your analytics or segmentation?
  • Are you noticing an increase in bounce rate and unsubscribe rates?
  • Finally, are you seeing a decrease in performance?

If you are seeing a data quality issues it is time to fix it. You can have a third party data cleansing company clean the data for you as to not disrupt your marketing initiative.

Anna Kayfitz is C.E.O. and founder of StrategicDB Corporation, an analytics and data cleansing company. Anna has over 10 years of marketing and analytics experience with companies such as Oracle Marketing Cloud (Previously known as Eloqua), Harlequin Enterprises, Sunwing Travel Group and a few start-ups. She also holds an MBA from a top business school in Canada.

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