Since the rise of big data marketing in 2005, B2B marketers have enthusiastically sought new, innovative strategies for leveraging customer and lead data to improve campaigns in the name of higher conversion rates and a better customer experience. Once-elusive information about customer buying decisions, effective marketing channels and product market fit is now easily quantified and used to create clear customer segments, buyer personas, and more effective campaigns.
Big data is king. But as marketers work to use data in new, creative ways, the demand for data quality becomes increasingly important. Bad data leads to weak analytics and ineffective campaigns, which can be costly to your marketing budget and your company’s brand.
Demanding quality data is not simple when done alone. Picture yourself as an amateur dog-walker your first day on the job. You have ten different pooches pulling you in ten different directions, all with ten different motives. The same can be said of a marketer attempting to wrangle big data without the support of data quality software. It is easy to get in over your head, trying to bring order to chaos and move in a forward direction.
The sooner your marketing team realizes you cannot manage big data alone and invests in database management software, the sooner you can begin to truly unlock the power of big data and its influence on your marketing strategy.
Here are the three essential elements that a robust data management solution should provide:
Data cleanliness focuses on the same problem identified by the old programming adage of “garbage in, garbage out”; poor quality input will always lead to erroneous output. In the world of big data, maintaining a system void of any “trash” – incorrect, incomplete, duplicate, or outdated datasets – means purer data analysis. Better data analysis leads to:
- More precise customer segmentation. Through scrubbed demographic and firmographic data, marketers are able to better determine product market fit and create customer profiles that can later be used to tailor campaign efforts to specific audiences.
- Accurate and consistent contact information for highest quality leads. Clean data means better lead scoring and quicker conversions for sales partners.
- A better customer experience. Clean data means less duplicate messages to single contacts, less emails after people have unsubscribed from your email list, and ultimately less disgruntled or annoyed customers or leads.
Data cleanliness is the first step on the road to better data quality and for those leveraging database management software, clean data is virtually out of sight, out of mind. Your data management solution should comb through and update your data multiple times per year, giving you peace-of-mind that your customer data is scrubbed of duplicates and firmographic data is validated.
Clean data means you can rest easy knowing the data you have already ingested from leads and customers remains up-to-date and accurate, but it does not solve for one of the other major issues in the world of big data marketing, namely, incomplete data.
Big data marketing cannot withstand major gaps in data. It is hard to put together a 1,000 piece puzzle if 25 percent of the pieces are missing from the box. That is why good data management relies heavily on database management software to enrich and complete big data. Complete data equals:
- Robust customer analysis. Once you have pulled back the curtain on customer data and have a complete view of firmographic data, determining target customers for quality campaigns becomes more of a science and less of an art.
- Increased ROI from automation. The more complete your data, the more experimental and customized you can be with your marketing efforts. Where most companies tend to use simple custom fields like name, email, and sometimes birthdays, marketers with clean, complete data sets can really hone in on customer needs by segment and personalize content without fear of major gaps.
No marketing team has the time or manpower to manually go about gathering complete data. Nor can the expectation lie on the customer to complete form after form in order to provide all the information you need for analysis.
Remember the dog-walker analogy from earlier? Take it one step further by calling the different breeds of dogs “data cleanliness” and knowing their personalities and interests on the walk, “data completion.” Where does data unification come into play?
Data unification is the leash. For marketers not using data quality software, it is as if they are walking the ten dogs on ten different leashes and expecting not to be exhausted. The truth is, with big data coming from so many different sources – web analytics, purchase history, and social media, to name just a few – smart marketers are investing in systems that can compile all the data from different sources into one, single database. They have found the answer to their multi-leash problem.
The results speak for themselves. Data unification helps big data marketing in the following ways:
- Better targeted buyer personas. With all of your data from multiple sources brought together in one place, customer segmentation advances beyond just who your target customers are to where, when, and how you can appropriately market to them in order to drive the highest conversion rates.
- More account-based marketing. Account-based marketing means, in short, developing campaigns specific to target accounts labeled as ideal prospects as a result of more robust, unified data.
A robust data management solution incorporates all three of these elements to ensure your data is in pristine condition and is capable of generating the insights you need to design effective marketing campaigns.
Watch the webinar: Getting Started with ABM: How to Fix Your Data Before It Kills Your Campaigns to learn more about the role data plays in your marketing success.