We had a great time at Marketo’s Marketing Nation Summit in San Francisco this week, meeting up with some of the best thinkers in marketing technology. There were tons of great sessions and information, including one session from consultant Inga Romanoff and Elliott Lowe, director of marketing operations for the Institution for Integrative Nutrition. The two looked at the importance of clean data, and the six steps it takes to get there: Perform a data audit, perform a systems audit, revise the data capture process, correct data errors, implement email alerts and reports and manage data quality across the organization.
Marketing technology is useless without clean data. This week, I’ve pulled together some articles that look at the importance of clean data in marketing analytics.
Think You Know Your Customers? You’re Wrong. CIO: “Many theories attempt to explain the perception-reality gap, a prominent one being paralysis by analysis. Marketers have become swamped by the sheer amount of customer data suddenly available to them from a myriad of sources, both internal and external. They have to separate data into three buckets: clean data that provides customer insights, dirty data that must be scrubbed before it can be used, and black data that ends up being useless.”
5 Critical Actions to Avoid the Point of No Return With Your Analytics Strategy. Forbes: “Analytics is more than just having that big ‘aha’ moment. Processes such as data integration, data quality, master data management, compliance and security must be considered. They must be acknowledged, and they may differ depending on who you’re supporting internally — the CEO who wants key performance indicators delivered by smartphone daily or the data scientist who wants to experiment on large data sets in a technology sandbox.”
Everything Marketers Need To Know About Engagement-Based Spam Filtering. MarketingLand: “Resolving reputation issues often requires optimizing your list acquisition practices, and then cleaning your list. Though many tout cleaning inactives as a technique to combat low engagement filtering, oftentimes the more valid reason to remove inactives is because they’re a sign of old, dirty data. Focusing on acquiring clean data, cleaning up existing bad data, and providing a great subscriber experience will help reach the inbox more often than focusing and worrying about engagement filtering.”
“Questionable”: Not the Word You Want Describing Your Marketing Data. Kapost: “Dun & Bradstreet NetProspex recently released their annual study on the state of B2B marketing data, which analyzed 223 million records. The results? Marketers don’t have a lot of confidence when it comes to their data. To get the most out of data, you need to understand who is in your database and how certain factors—like title, lead score, or lead source—affect a prospect’s interests and journey to purchase. But unless you can gather that information, you won’t be able to leverage it.”
Sales, Marketing and Automation: Promises Versus Expectations. Business2Community: “Both Marketing Automation and Sales Force Automation rely on a common Customer Relationship Management (CRM) database to feed their campaigns and reports, yet over 25 percent of that data is typically inaccurate or unreliable. Why? The Sales reps did not bother to enter accurate data because it didn’t help them directly make quota, so garbage in becomes garbage out, driving reports that do not provide the insights that help the C-suite manage sales performance and forecast accuracy.”