In nearly every use of “data” it is better to have no data than inaccurate data. At least with no information you know you either need to get data or can make decisions based on an educated guess. When you have bad data you make bad decisions and often times don’t find out until it’s too late.
There are so many types of data, let’s examine two types here. Data used in B2B direct marketing and data used in AI, machine learning, and deep learning.
B2B direct marketers work with information on companies and contacts at those companies for the purpose of targeted marketing. Most companies will have several types of target markets with varying industries, company sizes, geographies, and target title types within the target companies. If the data on these companies and contact is inaccurate, they end up wasting marketing dollars, damaging their brand, and ultimately a company can fail if they have the wrong target audience information.
For example, if you have inaccurate contact emails, marketing campaigns don’t get to your target audience. This typically happens when contacts change jobs. To avoid this, companies either identify and remove inaccurate emails themselves or work with email verification services.
Bad data can be much worse than an email bouncing or a phone number that is disconnected. That type of dirty data let’s you know there’s a problem. Picture that same company running a marketing campaign with direct mail pieces, telemarketing, email outreach and contact specific social media ads. Imagine these are being targeted to contacts in their target audience of event planners. Everything seems good and the data is clean with good phone numbers and emails etc, but it’s not correct. In this instance the company targeted contacts with “Event” anywhere in their title. This created a situation where titles like “Loss Prevention Manager” ended up in their marketing campaign. This shows that having clean data is having both accurate information and correct information.
The more accurate information marketers have the better experience they can provide to their target audience. Marketers love to cater their messaging to be as relevant as possible to each audience segment and this can all be done beautifully with clean data. We all prefer to see advertisements targeted to our interests and needs, but dirty and inaccurate data puts marketing messages in front of the wrong audience creating a bad experience for them. This can majorly damage the company’s brand. Ultimately, bad data reflects negatively on the company using it.
With marketing, it’s important to check the accuracy of both the contact information and all data that is relevant to the reason you are reaching out. This also applies when marketers use data to identify their most successful client segments. If you have 1000 customers and you want to know which of your target industries is most profitable, you must have accurate industry information for each client. If this information is incomplete or inaccurate it could cause you to focus your efforts in the wrong place.
There are countless ways data can be inaccurate, so it’s important to both verify and question data before you act on it. Whatever data you are using, be sure to put in the work to make sure the information is accurate and relevant to your needs.
About the Author
Sky Cassidy, CEO of MountainTop Data and co-host of the If You Market podcast, is a data and B2B marketing specialist. MountainTop Data has provided data services for almost two decades for some of the biggest brands executing B2B marketing, including data cleaning, data appending, data maintenance and email campaign management services. It was the first company to guarantee the accuracy of its licensed data and business emails.
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Some great information about bad data , this has really helped me. Thanks!