When we begin a strategic engagement with clients, we look at the five pillars of what we call the <\/span>Predictable Pipeline<\/span><\/a>: Target Market, Content and Messaging, Buyer\u2019s Journey and Sales Cycle, Resources and Technology, and Metrics and KPIs. For Resources and Technology, we\u2019re not just looking at the tools in your arsenal, but what enables those tools to work, and of course data is the foundation of it all. In short, we want to know if our clients have access to the right data to support their objectives.\u00a0<\/span><\/p>\n Do they have companies in their database that make up their target audience and ideal customer profile? Can they segment their database into buyer persona groups? Do they have fields for and collect location data, job titles, industry, annual revenue, so on? And here is the kicker\u2014is that data complete, accurate, compliant (I\u2019m looking at you <\/span>GDPR<\/span><\/a>, <\/span>CAN-SPAM<\/span><\/a>, <\/span>CCPA<\/span><\/a>, and <\/span>CASL<\/span><\/a>), and up to date?<\/span><\/p>\n Database health isn\u2019t necessarily the most exciting aspect of marketing operations, but it’s a crucial foundational element that you simply can\u2019t ignore.\u00a0<\/span><\/p>\n Data quality is crucial in B2B marketing and sales, directly influencing decision-making, customer engagement, and revenue growth.\u00a0<\/span><\/p>\n For example, effective lead scoring models, which rely on high-quality data, can significantly boost sales efficiency by focusing efforts on leads with the highest potential. But poor data quality can lead to misguided sales and marketing strategies, resulting in wasted time and resources. This misalignment often leads to targeting the wrong leads, ultimately causing financial losses and reputational damage.<\/span><\/p>\n Bad data can be detrimental to your campaign efforts, too, not just in low conversions and poor performance, but in sales wasting time trying to call people with bad phone numbers, or email undeliverable addresses. Worse yet, bad data contributes to a lack of trust in marketing\u2019s ability to support the sales team.\u00a0<\/span><\/p>\n Bad data is also costly. In fact, according to a recent Gartner study, \u201cEvery year, poor data quality costs organizations an average of $12.9 million.\u201d<\/span><\/p>\n Data quality can be a daunting hurdle to overcome, but for this post, I want to focus on incomplete data. To determine how your organization stacks up, a database audit should be a regular marketing operations task on your agenda.<\/span><\/p>\n To get started with the database audit, you\u2019ll need to first understand your target audience. What information defines your ideal customer profile? Here are some categories of information to consider when defining your ideal customer:<\/span><\/p>\n In addition, you need a clear understanding of the buying committee members, how they work together, and what type of inputs define them\u2014job title for one.<\/span><\/p>\n You\u2019ll also need to know what marketing campaigns are planned in order to determine if you have the data to support them. If your demand generation team is planning a campaign with the goal of closing deals at 10 new healthcare companies, but you\u2019re not capturing Industry in order to know if you even have those companies in your database, they either need to adjust their goals, or put a plan in place to gain the data needed.<\/span><\/p>\n These segmentations then become the fields you want to pay particular attention to when doing a database audit. Do you have people in your database that meet these requirements?\u00a0<\/span><\/p>\n The simplest way to conduct an audit is to pull lists within your marketing automation platform to get a sense for data completeness based on your targeting. How many leads are mailable vs. undeliverable\/unsubscribed? How many are missing an email address, or company name, job title, phone number, industry, location, annual revenue, employee number, etc.?<\/span><\/p>\n You could format it into a chart as simple as this:<\/span><\/p>\n You have your audit and have assessed the completeness of your data. Now what? Take a look at the results of your data. Which fields are standing out to you as incomplete? Now\u2019s the time to make a plan to fill those gaps.<\/span><\/p>\n First, it\u2019s important to understand your data inputs. Forms, list imports, manual entries\u2014you need to ensure you are actively collecting the data your organization needs to segment by.\u00a0<\/span><\/p>\n Are there fields you could add to your forms to get the data you need? Or inputs you could require your sales team to enter as they do their prospecting work? You could also utilize progressive profiling to gather more of your important ICP and buyer persona data for your known contacts each time they visit.<\/span><\/p>\n Another option, if you don’t have one already, is utilize a data enrichment tool like <\/span>ZoomInfo<\/span><\/a>, <\/span>Clearbit<\/span><\/a>, or <\/span>Apollo.io<\/span><\/a>. There are many vendors out there for list building and data enrichment.<\/span><\/p>\n And don\u2019t be afraid to get rid of the junk. You determined what percentage of your database was unmailable. Get rid of them! They\u2019re taking up precious real estate. That delete button is scary, but your marketing data should be working for you, not against you.<\/span><\/p>\n Finally, make a plan to perform this audit again and again. It should be a quarterly exercise\u2014yearly at the very least. Save your smart lists in a folder in your marketing automation platform so that you don\u2019t have to reinvent the wheel each time you do this.<\/span><\/p>\n Your marketing and sales database is the foundation of your strategy. Of course, data completeness is only one piece of the data quality puzzle, but it\u2019s an excellent place to start.<\/span><\/p>\n Want to chat?\u00a0Email us<\/a>\u00a0for a free brainstorm session!<\/p>\n The post Completeness and The Road to Database Quality<\/a> appeared first on Heinz Marketing<\/a>.<\/p>\n\n","protected":false},"excerpt":{"rendered":" By\u00a0Lisa Heay, Vice President of Business Operations at Heinz Marketing When we begin a strategic engagement with clients, we look at the five pillars of what we call the Predictable Pipeline: Target Market, Content and Messaging, Buyer\u2019s Journey and Sales Cycle, Resources and Technology, and Metrics and KPIs. For Resources and Technology, we\u2019re not just […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[98],"tags":[50517,16120,248,2683],"dealstore":[],"offerexpiration":[],"class_list":["post-112904","post","type-post","status-publish","format-standard","hentry","category-marketing","tag-completeness","tag-database","tag-quality","tag-road"],"yoast_head":"\n![\"email](\"https:\/\/www.heinzmarketing.com\/wp-content\/uploads\/2024\/01\/Winning-the-Inbox-Battle-CTA-Blog-992x289.png\")
<\/a><\/p>\nWhy should I care about data quality?<\/b><\/h2>\n
Data Completeness<\/b><\/h2>\n
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![\"DatabaseAudit\"](\"https:\/\/www.heinzmarketing.com\/wp-content\/uploads\/2023\/05\/DatabaseAudit.jpg\")
<\/a><\/p>\nNow what?<\/b><\/h2>\n