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We live in a world where we are surrounded by data – online behavior data, characteristic data, contact data, firmographic data, purchasing data, etc. It’s everywhere. Every human and business alike leaves behind a digital footprint. The businesses that are succeeding the fastest are those who have discovered how to effectively capture data, and more importantly, make profound sense of it. The problem though, is that not everyone has figured out how to capture the right data, and even if they have, analyzing it accurately to tell the right story is an incredibly difficult feat. But why?
Marketers + Data
Marketers aren’t traditionally skilled at collecting data from such a wide variety of channels, let alone manipulating the massive output of it all and trying to make sense of it. Marketing Land looked at the regularly ranked Washington University Olin Business School to see what courses are required to earn a marketing degree, and then curated what they believed to be the most applicable to real life marketing. No where on their list of courses do you see anything that relates to data, audiences, or analytics. Here’s their list:
- Capital Marketing and Financial Management
- Principles of Marketing
- Market Competition and Value Appropriation
- Financial Intermediaries and Market Econ
- Capital Market Imperfections
- Marketing Research
- Intro to Entrepreneurship
- Web Development
- Venture Consulting
- Brand Management
- Marketing Strategy
- Leadership in Organizations
I’d say that’s pretty solid proof that the importance of marketing data quality hasn’t yet made its way into our education systems. Since the rise of the internet and digital marketing in the early 2000s, marketing in general has grown and evolved more than it had in the previous 100 years. College graduates are being thrusted into a business world where every marketing decision is made based on data, yet they have no institutional knowledge of data, analytics or audience development. The current competency level of the average marketer is far below what is needed to succeed, so marketers are rushing to learn how to manipulate and manage datasets but making errors along the way.
If people are moving to new cities, getting new jobs, getting promotions, getting certified in new areas – then that means their digital footprint of data is changing. The real problem isn’t change, it’s the rate of change. Our workforce in the US is more transient than it has ever been… and it isn’t showing any signs of slowing down. A recent study by LinkedIn, showed that job-hopping has nearly doubled in the last 20 years, and that the average millennial will jump jobs 4 times in just 10 years after graduating college. The only industries that aren’t seeing this increase in job-hopping are autos, manufacturing, oil, etc. This means that in order to collect data accurately, it has to be done in an ongoing basis. If millennials are in your target audience, then you’d better have a slam dunk data partner as a best friend. It isn’t scalable to manually look up contacts in your database, determine their accuracy and then update them if they’re outdated; most marketers are dealing with millions of contacts, that we now know are changing jobs pretty fast. Find a data partner that has self-reported contact information, as it’s more accurate, but also make sure that your data partner can update those records quarterly, at a minimum. This will help ensure high marketing data quality.
Any sophisticated marketer knows that you need to be data hungry. The more you know about your audience, the better you can market to them; so it only makes sense to invest time and resources into getting as many relevant data points for each contact as possible. Some of these data points will come straight from your CRM/MAP – things like behavior data; what kind of content have they been consuming, are they active on your website, how many times did they click, etc. Some of the data may come from your sales team/bdr team as they do their prospecting outreach. Some of it may be legacy data that’s been in your database for years and you can’t delete it. Some are probably from events and from other marketing platforms as well. However, the most important data point is technically the contact information, because none of the other fields can really help you if you can’t actually communicate with the person. When a marketer feels the pressure to get more leads, they usually turn to their audience to see who they can target campaigns at – and most of the time, they have to face the reality that they either have outdated data that they’re afraid to use, or they simply don’t have enough contacts in the first place…or both. This is when having a good data partner pays off; just make sure that they are capable of refreshing whatever you purchase in an ongoing fashion, they validate the email addresses for you, and it certainly helps if they can marry the contact to their company.
Every organization has different permissions in place for who is allowed to add/omit/edit the company database so it’s important to first understand how it works at your org. Once you understand who is responsible for that, then you can start to better understand how the data is getting in and out of the database. Typos happen and they can happen upon entry, afterward, or even before. If an email address contains a typo, then obviously it will bounce and the intended recipient will not receive it. If other fields contain typos, like city or state for example, your segmentation rules will fail to capture them and you’ll miss out on potential leads.
Data can be completely accurate but depending on how you are analyzing the data, you may be using your findings to tell a story that isn’t actually accurate. For example, say you have 2 data points – one is global temperature, and the other is number of pirates in the world. If the number of pirates are decreasing at the same time as global temperatures are increasing, then you could technically say that to solve global warming, you need to increase the number of pirates in the world. Sounds crazy, right? Because it’s an inaccurate analysis of data. Now take that example and apply it to your marketing data. Imagine overall prospect engagement is increasing at the same time that email campaigns are decreasing – if you acted on that, you’d be sending out less emails campaigns and expecting overall engagement to increase. Be careful how you are analyzing your data, even if the data itself is actually accurate. There are data partners that help immensely with this so you don’t have to.
To conclude, the current state of data quality for b2b marketers is behind the curve due to data entry issues, various sourcing issues, competency issues, and data expiration issues. Not all is lost though, marketers are getting better at manipulating data every day and remarkable technologies have been created to help marketers through their data debacles. Data expiration isn’t going anywhere though so be sure to always be cleaning that database!