The data “monster” lurking within
With the adoption of Marketing Automation technology, B2B marketing teams are able to do more with less and measure everything. But what’s the scariest thing about these systems? Sending a campaign to the wrong list. Or forgetting to finish a landing page before a campaign launch may haunt you. Maybe you’re spooked that you won’t make your demand generation and ROI goals. However, I contend the number one scariest monster to tackle now is your poor data quality. It’s just waiting to rear its ugly head. Maybe it already has, but you just haven’t realized it!
The data on B2B marketing data
According to a D&B survey of over 500 B2B companies, 27% sited poor data quality/accuracy as their biggest obstacle for maximizing return on investment in marketing technology. Without reliable sales and marketing company and contact records, how will you target the right audience with the right message at the right time?
Marketing data quality is the foundation of all your digital marketing efforts.
Another survey from Openprise and Ascend2 found 72% of B2B marketers say a top goal of their marketing data management strategy is improving ROI measurability. And 44% said data quality is the most significant barrier to marketing data management success. This same study revealed the top 3 most effective uses of marketing data are campaign targeting (62%), content personalization (51%), and sales attribution (43%). Pretty important stuff wouldn’t you say?
Let’s delve a bit deeper into 3 things you should do now to ensure quality data.
1. Conduct a data audit – Review your existing contact and company records, see where your biggest problems are, and develop an improvement plan.
- How many duplicates are there?
- Which fields are being left empty or have inconsistent entries?
- How many records haven’t been touched in 6 months?
- What fields have typos or wrong information?
Very likely you’ll quickly see where the biggest issues are. Subsequently, you can prioritize what to work on first. As you tackle each issue, think about how the you’ll use the data (or is it even needed any more). In addition, consider where the data is coming from (users or other systems such as CRM, social media, or other marketing tools).
Unstructured data, such as text fields and comments or notes entries cause many problems. For example, unstructured data fields are not very useful for filtering, creating criteria, or analysis. I’ve found that structuring field input, for example using dropdown lists or multi-picklists, reduces blank entries and increases data quality. As a result, your campaigns, personalization, and analysis will be more accurate and effective.
2. Develop a data management process – If you don’t have a data management process, develop one. Make sure you have people dedicated to routine maintenance, data input standards (with agreement from sales), and documented processes that detail who, what, and when. The data audit discussed above will highlight the lack of or broken processes. For example, data importing, duplicate handling, data cleansing, and archiving/merging/deleting old contact and company records. Clean out the cobwebs and establish processes for keeping it clean.
Read my post about business processes to learn the why and how for data management and other marketing processes.
3. Training, training, training – Let’s face it, most poor data quality comes from a lack of data management processes and user training. After you have documented data entry processes, it’s time to train users. Provide documented data entry standards, create on-boarding training for new employees, and periodic tips/updates/helpful hints to your users. Provide one-on-one help to those not in compliance. Make sure your CRM and Marketing Automation systems have on-screen field level help. Provide short tutorial videos for on-demand, self-service help.
Without good quality data your B2B marketing team struggles with personalization and campaign effectiveness. Plus you may make decisions that’ll haunt you down the road. That’s not scary, that’s terrifying!
What’s your biggest challenge to data quality management?